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Krausková, Veronika, and Henrich Pifko. "Use of Artificial Intelligence in the Field of Sustainable Architecture: Current Knowledge." Architecture Papers of the Faculty of Architecture and Design STU 26, no. 1 (March 1, 2021): 20–29. http://dx.doi.org/10.2478/alfa-2021-0004.
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Abstract Innovative technologies help automate the work of the designer. A 3D model of the building can be used to calculate the required values. This will also allow you to create associative sections that, when changing the geometry of the 3D model, automatically adjust the drawn elements on the resulting 2D drawing. Information technologies enable participants from all over the world to work on one project and, thanks to the BIM (building information modelling) method, to design buildings during their life cycle more efficiently. At present, critical studies are published on interoper-ability in BIM and its lack of coordination or amount of information that is misinterpreted, etc. However, working in BIM is the most effective way to use computer technology to design buildings. There is a lot of information about the building in the 3D model itself, which can also be used for purposes other than construction (building management, reconstruction). But how to process a large amount of information in a 3D model? Many buildings already have their 3D models shared on cloud platforms, these contain information that could help, for example, find solutions for green construction using artificial intelligence (AI). We meet with AI every day. It supports internet search engines, predicts auto-completion words as you type. AI can also be found in architecture – not only as visions at exhibitions, but also in research on process optimization in BIM.
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Farzaneh, Hooman, Ladan Malehmirchegini, Adrian Bejan, Taofeek Afolabi, Alphonce Mulumba, and Precious P. Daka. "Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency." Applied Sciences 11, no. 2 (January 14, 2021): 763. http://dx.doi.org/10.3390/app11020763.
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The emerging concept of smart buildings, which requires the incorporation of sensors and big data (BD) and utilizes artificial intelligence (AI), promises to usher in a new age of urban energy efficiency. By using AI technologies in smart buildings, energy consumption can be reduced through better control, improved reliability, and automation. This paper is an in-depth review of recent studies on the application of artificial intelligence (AI) technologies in smart buildings through the concept of a building management system (BMS) and demand response programs (DRPs). In addition to elaborating on the principles and applications of the AI-based modeling approaches widely used in building energy use prediction, an evaluation framework is introduced and used for assessing the recent research conducted in this field and across the major AI domains, including energy, comfort, design, and maintenance. Finally, the paper includes a discussion on the open challenges and future directions of research on the application of AI in smart buildings.
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Matin, Sahar S., and Biswajeet Pradhan. "Earthquake-Induced Building-Damage Mapping Using Explainable AI (XAI)." Sensors 21, no. 13 (June 30, 2021): 4489. http://dx.doi.org/10.3390/s21134489.
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Building-damage mapping using remote sensing images plays a critical role in providing quick and accurate information for the first responders after major earthquakes. In recent years, there has been an increasing interest in generating post-earthquake building-damage maps automatically using different artificial intelligence (AI)-based frameworks. These frameworks in this domain are promising, yet not reliable for several reasons, including but not limited to the site-specific design of the methods, the lack of transparency in the AI-model, the lack of quality in the labelled image, and the use of irrelevant descriptor features in building the AI-model. Using explainable AI (XAI) can lead us to gain insight into identifying these limitations and therefore, to modify the training dataset and the model accordingly. This paper proposes the use of SHAP (Shapley additive explanation) to interpret the outputs of a multilayer perceptron (MLP)—a machine learning model—and analyse the impact of each feature descriptor included in the model for building-damage assessment to examine the reliability of the model. In this study, a post-event satellite image from the 2018 Palu earthquake was used. The results show that MLP can classify the collapsed and non-collapsed buildings with an overall accuracy of 84% after removing the redundant features. Further, spectral features are found to be more important than texture features in distinguishing the collapsed and non-collapsed buildings. Finally, we argue that constructing an explainable model would help to understand the model’s decision to classify the buildings as collapsed and non-collapsed and open avenues to build a transferable AI model.
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Das, Sushmita, Aleena Swetapadma, and Chinmoy Panigrahi. "A STUDY ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES FOR PREDICTING THE HEATING AND COOLING LOADS OF BUILDINGS." Journal of Green Building 14, no. 3 (June 2019): 115–28. http://dx.doi.org/10.3992/1943-4618.14.3.115.
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The prediction of the heating and cooling loads of a building is an essential aspect in studies involving the analysis of energy consumption in buildings. An accurate estimation of heating and cooling load leads to better management of energy related tasks and progressing towards an energy efficient building. With increasing global energy demands and buildings being major energy consuming entities, there is renewed interest in studying the energy performance of buildings. Alternative technologies like Artificial Intelligence (AI) techniques are being widely used in energy studies involving buildings. This paper presents a review of research in the area of forecasting the heating and cooling load of buildings using AI techniques. The results discussed in this paper demonstrate the use of AI techniques in the estimation of the thermal loads of buildings. An accurate prediction of the heating and cooling loads of buildings is necessary for forecasting the energy expenditure in buildings. It can also help in the design and construction of energy efficient buildings.
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Z. Allam, Sammar. "ARTIFICIAL INTELLIGENCEINTERVENTION TO URBAN BUILDING RENEWABLE ENERGY MODELING INTERVENTION FOR ROBUST FLEXIBLE COMMUNITIES." International Journal of Advanced Research 9, no. 03 (March 31, 2021): 314–21. http://dx.doi.org/10.21474/ijar01/12586.
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This research coveys a comparative analysis between Urban Building energy model (UBEM) generated by scholar, researchers, and professional in academia and industry while highlighting the reliable main components to manifest a successful and reliable UBEM technologies. Nevertheless, it consolidates distributed generation on building blocks rather than a whole district relying on renewable energy sources. It guides engineers through energy system model simulation on Openmodelica platform to feed green sustained communities. Moreover, energy use-pattern is mapped and analyzed by internet of things (IOT) technologies to fine-tune energy uses and refine use-pattern. Demonstrating artificial Intelligence (AI) algorithmto predict energy consumption can reflect on the amount of energy required for storage to cover energy needs. AI shapes a robust positive energy district (PED) through storinggenerated renewable solar or bio-energy to cover predicted energy use-pattern.Distributed -power-plant stations capacity to cover clusters using AI in predicting energy consumption consolidates on-site energy generation recommended by multiple International rating systems. AI-based Energy management plan guide engineers and planners to design distributed-power-plants of energy generation capacity lies between the actual energy need and a predicted scenario.
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Paschen, Jeannette, Jan Kietzmann, and Tim Christian Kietzmann. "Artificial intelligence (AI) and its implications for market knowledge in B2B marketing." Journal of Business & Industrial Marketing 34, no. 7 (August 5, 2019): 1410–19. http://dx.doi.org/10.1108/jbim-10-2018-0295.
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Purpose The purpose of this paper is to explain the technological phenomenon artificial intelligence (AI) and how it can contribute to knowledge-based marketing in B2B. Specifically, this paper describes the foundational building blocks of any artificial intelligence system and their interrelationships. This paper also discusses the implications of the different building blocks with respect to market knowledge in B2B marketing and outlines avenues for future research. Design/methodology/approach The paper is conceptual and proposes a framework to explicate the phenomenon AI and its building blocks. It further provides a structured discussion of how AI can contribute to different types of market knowledge critical for B2B marketing: customer knowledge, user knowledge and external market knowledge. Findings The paper explains AI from an input–processes–output lens and explicates the six foundational building blocks of any AI system. It also discussed how the combination of the building blocks transforms data into information and knowledge. Practical implications Aimed at general marketing executives, rather than AI specialists, this paper explains the phenomenon artificial intelligence, how it works and its relevance for the knowledge-based marketing in B2B firms. The paper highlights illustrative use cases to show how AI can impact B2B marketing functions. Originality/value The study conceptualizes the technological phenomenon artificial intelligence from a knowledge management perspective and contributes to the literature on knowledge management in the era of big data. It addresses calls for more scholarly research on AI and B2B marketing.
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van Bekkum, Michael, Maaike de Boer, Frank van Harmelen, André Meyer-Vitali, and Annette ten Teije. "Modular design patterns for hybrid learning and reasoning systems." Applied Intelligence 51, no. 9 (June 18, 2021): 6528–46. http://dx.doi.org/10.1007/s10489-021-02394-3.
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AbstractThe unification of statistical (data-driven) and symbolic (knowledge-driven) methods is widely recognized as one of the key challenges of modern AI. Recent years have seen a large number of publications on such hybrid neuro-symbolic AI systems. That rapidly growing literature is highly diverse, mostly empirical, and is lacking a unifying view of the large variety of these hybrid systems. In this paper, we analyze a large body of recent literature and we propose a set of modular design patterns for such hybrid, neuro-symbolic systems. We are able to describe the architecture of a very large number of hybrid systems by composing only a small set of elementary patterns as building blocks. The main contributions of this paper are: 1) a taxonomically organised vocabulary to describe both processes and data structures used in hybrid systems; 2) a set of 15+ design patterns for hybrid AI systems organized in a set of elementary patterns and a set of compositional patterns; 3) an application of these design patterns in two realistic use-cases for hybrid AI systems. Our patterns reveal similarities between systems that were not recognized until now. Finally, our design patterns extend and refine Kautz’s earlier attempt at categorizing neuro-symbolic architectures.
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Cherkina, Vera, Elena Petrenko, Maxim Kirichenko, and Pavel Samarin. "The role of information modeling and automated technologies in the design and construction of high-rise buildings." E3S Web of Conferences 215 (2020): 03007. http://dx.doi.org/10.1051/e3sconf/202021503007.
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The purpose of the work is to consider the role of information modeling and automated technologies in the design and construction of high - rise buildings. The introduction of digital information tools in the construction sector provides rich ground for the birth and growth of companies specializing in the application of technology in design and construction. While some of the technologies are new, many companies implement ideas proposed in building research several decades ago that were impractical without a reliable digital database of building information. High-rise building information modeling (BIM) itself dates back to a landmark 1975 paper; ideas for artificial intelligence and code-checking tools emerged in the mid-1980s; and construction robots have been working in research labs for decades. However, only during the last five years, venture capital began to actively look for a new company in the sector of construction technologies. After analyzing a number of innovations in the field of digital construction through their known past and their uncertain present, it was concluded that the prospects for the development of these innovations in the construction industry, especially in the construction of high-rise buildings, are quite high. The author came to the following conclusion: a review of three specific areas of construction technologies, representing design and planning, BIM-to-field and field-to-BIM applications, shows that the widespread adoption of BIM environments in the construction industry is not sufficient to ensure the effective use of the information they contain, or to use the potential of AI in this context.
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Zhang, Jingwen, Yoo Jung Oh, Patrick Lange, Zhou Yu, and Yoshimi Fukuoka. "Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet: Viewpoint." Journal of Medical Internet Research 22, no. 9 (September 30, 2020): e22845. http://dx.doi.org/10.2196/22845.
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Background Chatbots empowered by artificial intelligence (AI) can increasingly engage in natural conversations and build relationships with users. Applying AI chatbots to lifestyle modification programs is one of the promising areas to develop cost-effective and feasible behavior interventions to promote physical activity and a healthy diet. Objective The purposes of this perspective paper are to present a brief literature review of chatbot use in promoting physical activity and a healthy diet, describe the AI chatbot behavior change model our research team developed based on extensive interdisciplinary research, and discuss ethical principles and considerations. Methods We conducted a preliminary search of studies reporting chatbots for improving physical activity and/or diet in four databases in July 2020. We summarized the characteristics of the chatbot studies and reviewed recent developments in human-AI communication research and innovations in natural language processing. Based on the identified gaps and opportunities, as well as our own clinical and research experience and findings, we propose an AI chatbot behavior change model. Results Our review found a lack of understanding around theoretical guidance and practical recommendations on designing AI chatbots for lifestyle modification programs. The proposed AI chatbot behavior change model consists of the following four components to provide such guidance: (1) designing chatbot characteristics and understanding user background; (2) building relational capacity; (3) building persuasive conversational capacity; and (4) evaluating mechanisms and outcomes. The rationale and evidence supporting the design and evaluation choices for this model are presented in this paper. Conclusions As AI chatbots become increasingly integrated into various digital communications, our proposed theoretical framework is the first step to conceptualize the scope of utilization in health behavior change domains and to synthesize all possible dimensions of chatbot features to inform intervention design and evaluation. There is a need for more interdisciplinary work to continue developing AI techniques to improve a chatbot’s relational and persuasive capacities to change physical activity and diet behaviors with strong ethical principles.
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Yi, Hwang. "Visualized Co-Simulation of Adaptive Human Behavior and Dynamic Building Performance: An Agent-Based Model (ABM) and Artificial Intelligence (AI) Approach for Smart Architectural Design." Sustainability 12, no. 16 (August 18, 2020): 6672. http://dx.doi.org/10.3390/su12166672.
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Human (occupant) behavior has been a topic of active research in the study of architecture and energy. To integrate the work of architectural design with techniques of building performance simulation in the presence of responsive human behavior, this study proposes a computational framework that can visualize and evaluate space occupancy, energy use, and generative envelope design given a space outline. A design simulation platform based on the visual programming language (VPL) of Rhino Grasshopper (GH) and Python is presented so that users (architects) can monitor real-time occupant response to space morphology, environmental building operation, and the formal optimization of three-dimensional (3D) building space. For dynamic co-simulation, the Building Controls Virtual Test Bed, Energy Plus, and Radiance were interfaced, and the agent-based model (ABM) approach and Gaussian process (GP) were applied to represent agents’ self-learning adaptation, feedback, and impact on room temperature and illuminance. Hypothetical behavior scenarios of virtual agents with experimental building geometry were produced to validate the framework and its effectiveness in supporting dynamic simulation. The study’s findings show that building energy and temperature largely depend on ABMs and geometry configuration, which demonstrates the importance of coupled simulation in design decision-making.
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Singh, Lalit, Ritesh K. Tiwari, Shashi Verma, and Vijay Sharma. "The Future of Artificial Intelligence in Pharmaceutical Product Formulation." Drug Delivery Letters 9, no. 4 (October 31, 2019): 277–85. http://dx.doi.org/10.2174/2210303109666190621144400.
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Background: Conventional approach of formulating a new dosage form is a comprehensive task and uses various sources like man, money, time and experimental efforts. The use of AI can help to obtain optimized pharmaceutical formulation with desired (best) attributes. AI minimizes the use of resources and increases the understanding of impact, of independent variable over desired dependent responses/variables. Objective: Thus, the aim of present work is to explore the use of Artificial intelligence in designing pharmaceutical products as well as the manufacturing process to get the pharmaceutical product of desired attributes with ease. The review is presenting various aspects of Artificial intelligence like Quality by Design (QbD) & Design of Experiment (DoE) to confirm the quality profile of drug product, reduce interactions among the input variables for the optimization, modelization and various simulation tools used in pharmaceutical manufacturing (scale up and production). Conclusion: Hence, the use of QbD approach in Artificial intelligence is not only useful in understanding the products or process but also helps in building an excellent and economical pharmaceutical product.
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Dutta, Shubhankar, and Kakoli Bose. "Remodelling structure-based drug design using machine learning." Emerging Topics in Life Sciences 5, no. 1 (April 7, 2021): 13–27. http://dx.doi.org/10.1042/etls20200253.
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To keep up with the pace of rapid discoveries in biomedicine, a plethora of research endeavors had been directed toward Rational Drug Development that slowly gave way to Structure-Based Drug Design (SBDD). In the past few decades, SBDD played a stupendous role in identification of novel drug-like molecules that are capable of altering the structures and/or functions of the target macromolecules involved in different disease pathways and networks. Unfortunately, post-delivery drug failures due to adverse drug interactions have constrained the use of SBDD in biomedical applications. However, recent technological advancements, along with parallel surge in clinical research have led to the concomitant establishment of other powerful computational techniques such as Artificial Intelligence (AI) and Machine Learning (ML). These leading-edge tools with the ability to successfully predict side-effects of a wide range of drugs have eventually taken over the field of drug design. ML, a subset of AI, is a robust computational tool that is capable of data analysis and analytical model building with minimal human intervention. It is based on powerful algorithms that use huge sets of ‘training data’ as inputs to predict new output values, which improve iteratively through experience. In this review, along with a brief discussion on the evolution of the drug discovery process, we have focused on the methodologies pertaining to the technological advancements of machine learning. This review, with specific examples, also emphasises the tremendous contributions of ML in the field of biomedicine, while exploring possibilities for future developments.
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Xu, Yueqiang, Petri Ahokangas, Jean-Nicolas Louis, and Eva Pongrácz. "Electricity Market Empowered by Artificial Intelligence: A Platform Approach." Energies 12, no. 21 (October 30, 2019): 4128. http://dx.doi.org/10.3390/en12214128.
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Artificial intelligence (AI) techniques and algorithms are increasingly being utilized in energy and renewable research to tackle various engineering problems. However, a majority of the AI studies in the energy domain have been focusing on solving specific technical issues. There is limited discussion on how AI can be utilized to enhance the energy system operations, particularly the electricity market, with a holistic view. The purpose of the study is to introduce the platform architectural logic that encompasses both technical and economic perspectives to the development of AI-enabled energy platforms for the future electricity market with massive and distributed renewables. A constructive and inductive approach for theory building is employed for the concept proposition of the AI energy platform by using the aggregated data from a European Union (EU) Horizon 2020 project and a Finnish national innovation project. Our results are presented as a systemic framework and high-level representation of the AI-enabled energy platform design with four integrative layers that could enable not only value provisioning but also value utilization for a distributed energy system and electricity market as the new knowledge and contribution to the extant research. Finally, the study discusses the potential use cases of the AI-enabled energy platform.
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Trakadas, Panagiotis, Lambros Sarakis, Anastasios Giannopoulos, Sotirios Spantideas, Nikolaos Capsalis, Panagiotis Gkonis, Panagiotis Karkazis, et al. "A Cost-Efficient 5G Non-Public Network Architectural Approach: Key Concepts and Enablers, Building Blocks and Potential Use Cases." Sensors 21, no. 16 (August 19, 2021): 5578. http://dx.doi.org/10.3390/s21165578.
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The provision of high data rate services to mobile users combined with improved quality of experience (i.e., zero latency multimedia content) drives technological evolution towards the design and implementation of fifth generation (5G) broadband wireless networks. To this end, a dynamic network design approach is adopted whereby network topology is configured according to service demands. In parallel, many private companies are interested in developing their own 5G networks, also referred to as non-public networks (NPNs), since this deployment is expected to leverage holistic production monitoring and support critical applications. In this context, this paper introduces a 5G NPN architectural approach, supporting among others various key enabling technologies, such as cell densification, disaggregated RAN with open interfaces, edge computing, and AI/ML-based network optimization. In the same framework, potential applications of our proposed approach in real world scenarios (e.g., support of mission critical services and computer vision analytics for emergencies) are described. Finally, scalability issues are also highlighted since a deployment framework of our architectural design in an additional real-world scenario related to Industry 4.0 (smart manufacturing) is also analyzed.
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Urbieta, Itziar, Marcos Nieto, Mikel García, and Oihana Otaegui. "Design and Implementation of an Ontology for Semantic Labeling and Testing: Automotive Global Ontology (AGO)." Applied Sciences 11, no. 17 (August 24, 2021): 7782. http://dx.doi.org/10.3390/app11177782.
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Modern Artificial Intelligence (AI) methods can produce a large quantity of accurate and richly described data, in domains such as surveillance or automation. As a result, the need to organize data at a large scale in a semantic structure has arisen for long-term data maintenance and consumption. Ontologies and graph databases have gained popularity as mechanisms to satisfy this need. Ontologies provide the means to formally structure descriptive and semantic relations of a domain. Graph databases allow efficient and well-adapted storage, manipulation, and consumption of these linked data resources. However, at present, there is no a universally defined strategy for building AI-oriented ontologies for the automotive sector. One of the key challenges is the lack of a global standardized vocabulary. Most private initiatives and large open datasets for Advanced Driver Assistance Systems (ADASs) and Autonomous Driving (AD) development include their own definitions of terms, with incompatible taxonomies and structures, thus resulting in a well-known lack of interoperability. This paper presents the Automotive Global Ontology (AGO) as a Knowledge Organization System (KOS) using a graph database (Neo4j). Two different use cases for the AGO domain ontology are presented to showcase its capabilities in terms of semantic labeling and scenario-based testing. The ontology and related material have been made public for their subsequent use by the industry and academic communities.
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Berberich, Nicolas, Toyoaki Nishida, and Shoko Suzuki. "Harmonizing Artificial Intelligence for Social Good." Philosophy & Technology 33, no. 4 (September 1, 2020): 613–38. http://dx.doi.org/10.1007/s13347-020-00421-8.
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Abstract To become more broadly applicable, positions on AI ethics require perspectives from non-Western regions and cultures such as China and Japan. In this paper, we propose that the addition of the concept of harmony to the discussion on ethical AI would be highly beneficial due to its centrality in East Asian cultures and its applicability to the challenge of designing AI for social good. We first present a synopsis of different definitions of harmony in multiple contexts, such as music and society, which reveals that the concept is, at its core, about well-balanced relationships and appropriate actions which give rise to order, balance, and aesthetically pleasing phenomena. The mediator for these well-balanced relationships is Takt which is an ability to act thoughtfully and sensibly according to the specific situation and to put things into proportion and order. We propose that the central challenge of building harmonizing AI is to make intelligent systems tactful and also to design and use them tactfully. For an AI system to become tactful, it needs to be able to have an advanced sensitivity to the specific contexts which it is in and their social and ethical implications and have the capability of approximately inferring the emotional and cognitive states of people with whom it is interacting.
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Lu, S. C. Y., and D. K. Tcheng. "Building Layered Models to Support Engineering Decision Making: A Machine Learning Approach." Journal of Engineering for Industry 113, no. 1 (February 1, 1991): 1–9. http://dx.doi.org/10.1115/1.2899617.
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This paper presents a new model building methodology which, given a detailed mechanistic model of a task, can optimally produce a set of models with layered abstraction according to the user’s specified modeling objectives. These layered models can be used to evaluate decisions made at different levels of abstraction during a typical problem-solving process such as engineering design and planning. In our research, the model building process is viewed as a learning activity and inductive machine learning techniques from AI are combined with traditional optimization methods to form our prototype model building system called AIMS (Adaptive and Interactive Modeling System). The layered analysis models built by AIMS offer several distinctive advantages over those traditional analysis models which can only provide evaluations at very detailed stages of decision making. These advantages include: early evaluation to avoid costly iterations, fast execution for interactive applications, more comprehensibility for human inspection, and deep roots in domain physics for higher accuracy. Case study results of building layered models for a process design task of an intermittent cutting process are presented as a demonstration of the potential use of our system. We also explain this model building research in the context of the knowledge processing technology as a new foundation for advanced engineering automation.
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Marshall, Thomas Edward, and Sherwood Lane Lambert. "Cloud-Based Intelligent Accounting Applications: Accounting Task Automation Using IBM Watson Cognitive Computing." Journal of Emerging Technologies in Accounting 15, no. 1 (March 1, 2018): 199–215. http://dx.doi.org/10.2308/jeta-52095.
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ABSTRACT This paper presents a cognitive computing model, based on artificial intelligence (AI) technologies, supporting task automation in the accounting industry. Drivers and consequences of task automation, globally and in accounting, are reviewed. A framework supporting cognitive task automation is discussed. The paper recognizes essential differences between cognitive computing and data analytics. Cognitive computing technologies that support task automation are incorporated into a model delivering federated knowledge. The impact of task automation on accounting job roles and the resulting creation of new accounting job roles supporting innovation are presented. The paper develops a hypothetical use case of building a cloud-based intelligent accounting application design, defined as cognitive services, using machine learning based on AI. The paper concludes by recognizing the significance of future research into task automation in accounting and suggests the federated knowledge model as a framework for future research into the process of digital transformation based on cognitive computing.
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Gale, William A. "Statistical applications of artificial intelligence and knowledge engineering." Knowledge Engineering Review 2, no. 4 (December 1987): 227–47. http://dx.doi.org/10.1017/s0269888900004136.
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AbstractKnowledge engineering (KE) has now provided some effective techniques for formalization of knowledge about goals and actions. These techniques could open new areas of research to statisticians. Experimental systems designed to assist users of statistics have been constructed in experiment design, data analysis, technique application, and technique selection. Knowledge formalization has also been used in experimental programs to assist statisticians in doing data analysis and in building consultation systems. The best-explored application of KE techniques is building consultation systems. It is now a promising area for development. Analogies with successful artificial intelligence AI applications in other fields suggest other statistical applications worth exploring. Opening new areas to research and providing new tools to users would make considerable changes in the use and production of statistical techniques. However, applying currently available KE techniques will lead to more work for statisticians, not less.
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Tanaka, Takayuki, Junji Ohwi, Ludmila V. Litvintseva, Kazuo Yamafuji, and Sergei V. Ulyanov. "Intelligent Control of a Mobile Robot for Service Use in Office Buildings and Its Soft Computing Algorithms." Journal of Robotics and Mechatronics 8, no. 6 (December 20, 1996): 538–54. http://dx.doi.org/10.20965/jrm.1996.p0538.
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The arrangement principles and design methodology on soft computing for complex control framework of AI control system are introduced. The basis of this methodology is computer simulation of dynamics for mechanical robotic system with the help of qualitative physics and search for possible solutions by genetic algorithms (GA). On fuzzy neural network (FNN) optimal solutions for navigation with avoidance of obstacles and technological operations as opening of door with a manipulator are obtained and knowledge base (KB) for fuzzy controller is formed. Fuzzy qualitative simulation, GA and hierarchical node map (HN), and FNN have demonstrated their effectiveness for path planning of a mobile robot for service use. New approach for direct human-robot communication with natural language and cognitive graphics is introduced. The results of fuzzy robot control simulation, monitoring, and experimental investigations are presented.
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Su, Zhaohui, Dean McDonnell, Barry L. Bentley, Jiguang He, Feng Shi, Ali Cheshmehzangi, Junaid Ahmad, and Peng Jia. "Addressing Biodisaster X Threats With Artificial Intelligence and 6G Technologies: Literature Review and Critical Insights." Journal of Medical Internet Research 23, no. 5 (May 25, 2021): e26109. http://dx.doi.org/10.2196/26109.
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Background With advances in science and technology, biotechnology is becoming more accessible to people of all demographics. These advances inevitably hold the promise to improve personal and population well-being and welfare substantially. It is paradoxical that while greater access to biotechnology on a population level has many advantages, it may also increase the likelihood and frequency of biodisasters due to accidental or malicious use. Similar to “Disease X” (describing unknown naturally emerging pathogenic diseases with a pandemic potential), we term this unknown risk from biotechnologies “Biodisaster X.” To date, no studies have examined the potential role of information technologies in preventing and mitigating Biodisaster X. Objective This study aimed to explore (1) what Biodisaster X might entail and (2) solutions that use artificial intelligence (AI) and emerging 6G technologies to help monitor and manage Biodisaster X threats. Methods A review of the literature on applying AI and 6G technologies for monitoring and managing biodisasters was conducted on PubMed, using articles published from database inception through to November 16, 2020. Results Our findings show that Biodisaster X has the potential to upend lives and livelihoods and destroy economies, essentially posing a looming risk for civilizations worldwide. To shed light on Biodisaster X threats, we detailed effective AI and 6G-enabled strategies, ranging from natural language processing to deep learning–based image analysis to address issues ranging from early Biodisaster X detection (eg, identification of suspicious behaviors), remote design and development of pharmaceuticals (eg, treatment development), and public health interventions (eg, reactive shelter-at-home mandate enforcement), as well as disaster recovery (eg, sentiment analysis of social media posts to shed light on the public’s feelings and readiness for recovery building). Conclusions Biodisaster X is a looming but avoidable catastrophe. Considering the potential human and economic consequences Biodisaster X could cause, actions that can effectively monitor and manage Biodisaster X threats must be taken promptly and proactively. Rather than solely depending on overstretched professional attention of health experts and government officials, it is perhaps more cost-effective and practical to deploy technology-based solutions to prevent and control Biodisaster X threats. This study discusses what Biodisaster X could entail and emphasizes the importance of monitoring and managing Biodisaster X threats by AI techniques and 6G technologies. Future studies could explore how the convergence of AI and 6G systems may further advance the preparedness for high-impact, less likely events beyond Biodisaster X.
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Chandrasekaran, B. "Problem solving methods and knowledge systems: A personal journey to perceptual images as knowledge." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 23, no. 4 (October 14, 2009): 331–38. http://dx.doi.org/10.1017/s0890060409990035.
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AbstractI was among those who proposed problem solving methods (PSMs) in the late 1970s and early 1980s as a knowledge-level description of strategies useful in building knowledge-based systems. This paper summarizes the evolution of my ideas in the last two decades. I start with a review of the original ideas. From an artificial intelligence (AI) point of view, it is not PSMs as such, which are essentially high-level design strategies for computation, that are interesting, but PSMs associated with tasks that have a relation to AI and cognition. They are also interesting with respect to cognitive architecture proposals such as Soar and ACT-R: PSMs are observed regularities in the use of knowledge that an exclusive focus on the architecture level might miss, the latter providing no vocabulary to talk about these regularities. PSMs in the original conception are closely connected to a specific view of knowledge: symbolic expressions represented in a repository and retrieved as needed. I join critics of this view, and maintain with them that most often knowledge is not retrieved from a base as much as constructed as needed. This criticism, however, raises the question of what is in memory that is not knowledge as traditionally conceived in AI, but can support theconstructionof knowledge in predicate–symbolic form. My recent proposal about cognition and multimodality offers a possible answer. In this view, much of memory consists of perceptual and kinesthetic images, which can be recalled during deliberation and from which internal perception can generate linguistic–symbolic knowledge. For example, from a mental image of a configuration of objects, numerous sentences can be constructed describing spatial relations between the objects. My work on diagrammatic reasoning is an implemented example of how this might work. These internal perceptions on imagistic representations are a new kind of PSM.
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Muehlbauer, Manuel. "Towards typogenetic tools for generative urban aesthetics." Smart and Sustainable Built Environment 7, no. 1 (April 3, 2018): 20–32. http://dx.doi.org/10.1108/sasbe-10-2017-0052.
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Purpose Urban typogenetics investigates the use of machine intelligence for the evaluation of performance measures as a decision support system (DSS) with a focus on urban aesthetics evaluation. This framework allows designers to address performance measures, urban measures and aesthetic criteria in an adaptive, interactive generative design approach. The purpose of this paper is to provide an understanding of the structure and the nature of the framework and the application of human-in-the-loop design systems to urban design. Design/methodology/approach Significant literature reviewed lead to the identification of an application potential in the decision-making process. This potential is situated around the use of AI for the evaluation of subjective performance criteria in a DSS. Recognising that the key decisions about urban aesthetics are based on the individual evaluation of the designer, an HITL approach for computational design software to support creative decisions is presented in this paper. Findings Urban typogenetics for interactive generative urban design allows the exploration of complex design spaces by using a human-in-the-loop design system in the context of urban aesthetics. Hybrid aesthetic evaluation allows the designer to analyse morphological features and urban aesthetics during exploratory search and reveal hidden aspects of the urban context by visualisation of the results of the aesthetic evaluation. Integrating performance measures and urban aesthetics in urban typogenetics addresses major criteria of urban design at the beginning of the creative process. Originality/value The use of a broad interactive approach to typogenetic design in an application to urban scenarios is a novel conceptual approach to the design of urban configurations. The suggested adaptive mechanism would allow the user of a typogenetic tool to subjectively evaluate solutions by sight and reason about aesthetic, social and cultural implication of the reviewed design solutions.
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Ateya, Abdelhamied A., Ammar Muthanna, Anastasia Vybornova, Irina Gudkova, Yuliya Gaidamaka, Abdelrahman Abuarqoub, Abeer D. Algarni, and Andrey Koucheryavy. "Model Mediation to Overcome Light Limitations—Toward a Secure Tactile Internet System." Journal of Sensor and Actuator Networks 8, no. 1 (January 2, 2019): 6. http://dx.doi.org/10.3390/jsan8010006.
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Enabling haptic communication as well as voice and data over the future 5G cellular system has become a demand. Tactile Internet is one of the main use cases of the 5G system that will allow the transfer of haptic communications in real time. Latency, availability, reliability, and security are the main design challenges of the tactile Internet system and haptic based bilateral teleoperation systems. An end-to-end latency of 1 ms remains the main challenge toward tactile Internet system realization, not only for the processing and coding delays but mainly for the limitations of light. In this work, we analyze the key solutions to overcome the light limitations and enable the tactile Internet over any distances with the required latency. Building a virtual model or model mediated for the remote environment at the edge cloud unit near to the end user is the main solution. By means of AI, the virtual model can predict the behavior of the remote environment and thus, the end user can interact with the virtual environment with a high system experience. This literature review covers the existing work of the model mediated bilateral teleoperated systems and discusses its availability for the tactile Internet system. Furthermore, the security issues of tactile Internet system and the effect of model mediated system on the required security level of tactile Internet applications are discussed. Finally, a structure for the tactile Internet system, with the deployment of model mediation, is suggested.
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Karyono, Karyono, Badr Abdullah, Alison Cotgrave, and Ana Bras. "A Novel Adaptive Lighting System Which Considers Behavioral Adaptation Aspects for Visually Impaired People." Buildings 10, no. 9 (September 21, 2020): 168. http://dx.doi.org/10.3390/buildings10090168.
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The number of visually impaired people and elderly people groups are significant, but the current lighting system used in buildings, which is based on the current standard, cannot provide the necessary lighting comfort for them. The lighting system should provide the correct illuminance for every activity and even pattern of light. This research presents the work in progress in developing the novel adaptive lighting system tailored for visually impaired people, which becomes the solution to the problem. The behavioral adaptation aspects and the experience and memory principle are taken into account in the system design. It also makes use of the latest independent adjustable artificial light (LED) technology, to get an even pattern of lighting, while still considering efficient energy usage. The proposed system structure uses a wireless sensor network (WSN), big data processing, and the Artificial Intelligence (AI) sub-system, which can predict and adaptively regulate the illumination level based on the occupant’s needs and routines. The initial simulation of the lighting model is presented in this paper. The simulation uses five scenarios in different seasons and daylight. The simulation shows satisfactory results for illuminance values 200, 250, 300, 500, and 750 lux, needed by the occupants.
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Marchenko, Alla, Alenka Temeljotov-Salaj, Victor Rizzardi, and Odne Oksavik. "The Study of Facial Muscle Movements for Non-Invasive Thermal Discomfort Detection via Bio-Sensing Technology. Part I: Development of the Experimental Design and Description of the Collected Data." Applied Sciences 10, no. 20 (October 19, 2020): 7315. http://dx.doi.org/10.3390/app10207315.
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In the time of climate change, as heat waves become a more regular occurrence, indoor thermal comfort is an important factor in day to day life. Due to such circumstances, many researchers have focused their studies on finding an effective solution that will not only enable thermal comfort, but also increase satisfaction within the indoor environment and, as a result, productivity. The fast development of the biometrical field encouraged the study focused on the investigation of how bio-markers, in combination with artificial intelligence algorithms, can be collected within an experimental setting to create a new approach for non-invasive thermal discomfort detection. The developed experimental design provides synergy between automatic facial coding, pulse, and galvanic skin response measurements via iMotions software in a controlled environment. The iMotions software has built-in machine vision algorithms, and with Shimmer sensors and a post-processing tool through Affectiva AFFDEX, is able to collect facial action data through detection of the facial muscle movements and various bio-markers. The Zero Emission Building (ZEB) Test Cell laboratory was used as the control environment and transformed to imitate an office space for the data collection campaign at NTNU in Trondheim. The given experimental design provides an opportunity to create an immense database with bio-markers that are linked to the subcortical level of the brain, indoor parameters, and direct feedback on the comfort level of occupants within an office-like environment. In total, 111 data collection sessions were registered with iMotions. The discomfort button was pressed 240 times and 1080 planned indoor comfort evaluation surveys were held during experiment. The discomfort button was pressed 49 times to indicate that participant felt discomfort due to low temperature and 52 due to high temperature. Collected data revealed a big deviation in the discomfort temperature values for experiment participants with respect to performed temperature ramps. While it is common to use the same predefined temperature range for facility management, it became clear that the complexity of the task is greater and should not be approached on a human computational level. Implementation of AI can potentially provide higher value accuracy within thermal discomfort detection and enable unique personal user experience at the workplace.
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Darwiche, Adnan, Raymond E. Levitt, and Barbara Hayes-Roth. "OARPLAN: Generating project plans by reasoning about objects, actions and resources." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 2, no. 3 (August 1988): 169–81. http://dx.doi.org/10.1017/s0890060400000639.
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This paper describes OARPLAN, a prototype planning system that generates construction project plans from a description of the objects that comprise the completed facility. OARPLAN is based upon the notion that activities in a project plan can be viewed as intersections of their constituents: objects, actions and resources. Planning knowledge in OARPLAN is represented as constraints based on activity constituents and their interrelationships; the planner functions as a constraint satisfaction engine that attempts to satisfy these constraints. The goal of the OARPLAN project is to develop a planning shell for construction projects that (i) provides a natural and powerful constraint language for expressing knowledge about construction planning, and (ii) generates a facility construction plan by satisfying constraints expressed in this language.To generate its construction plan, OARPLAN must be supplied with extensive knowledge about construction objects, actions and resources, and about spatial, topological, temporal and other relations that may exist between them. We suggest that much of the knowledge required to plan the construction of a given facility can be drawn directly from a three-dimensional CAD model of the facility, and from a variety of databases currently used in design and project management software. In the prototype OARPLAN system, facility data must be input directly as frames. However, we are collaborating with database researchers to develop intelligent interfaces to such sources of planning data, so that OARPLAN will eventually be able to send high level queries to an intelligent database access system without regard for the particular CAD system in which the project was designed.We begin by explaining why classical AI planners and domain specific expert system approaches are both inadequate for the task of generating construction project plans. We describe the activity representation developed in OARPLAN and demonstrate its use in producing a plan of about 50 activities for a steel-frame building, based on spatial and topological constraints that express structural support, weather protection and safety concerns in construction planning. We conclude with a discussion of the research issues raised by our experiments with OARPLAN to date.
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BIRMINGHAM, WILLIAM P. "The AI in AI EDAM." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, no. 1 (January 2007): 5–6. http://dx.doi.org/10.1017/s0890060407070035.
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A natural question is why AI in design? Although the design applications written about in the journal vary widely, the common thread is that researchers use AI techniques to implement their ideas. The use of AI techniques for design applications, at least when AI EDAM was started, was partially a reaction against the predominant design methods based on some form of optimization. Knowledge-based techniques, particularly rule-based systems of various sorts, were very popular. One of the draws of these methods, I believe, was their ability to represent knowledge that is hard or awkward to represent in traditional optimization frameworks. This mirrors my experience: at the time, I was working in configuration with components that had a large number compatibility and resource constraints. Although many constraints could be represented in mixed integer linear programming systems, it was not easy to conceptualize, write, and most importantly, maintain the constraints in those systems.
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Khosrowshahi, Farzad. "Enhanced project brief: structured approach to client-designer interface." Engineering, Construction and Architectural Management 22, no. 5 (September 21, 2015): 474–92. http://dx.doi.org/10.1108/ecam-10-2014-0128.
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Purpose – The focus of this work is on the client-designer interface where decisions have significant impact over the lifecycle of the project. Therefore, the briefing stage is examined in the context of clients’ needs which is divided into project-based strategy and broader clients’ strategy. The purpose of this paper is to address the pitfalls in the briefing process which has been attributed to the shortcomings in the client-designer communication interfaces. This will be achieved by developing an automated brief generation framework. The research examines the efficiency of standard approaches to modelling and design, and the benefits that these methodologies have offered to the computer industry. The work reviews the similarities between the two industries and argues in support of the potential benefits in adopting a standard methodology in the construction industry. The structure upon which the framework is developed is based on system analysis and design methodology (SSADM) which has proven to be an effective platform used within the software development industry. Design/methodology/approach – SSADM is an established methodology within the software development industry. The paper will demonstrate that due to fundamental similarities between the construction and software development industries, SSADM is likely to offer a viable platform upon which an automated enhanced brief generation model is developed for use in the construction industry. The construction design and construction process will be mapped on SSADM high-level definition before focusing and honing on the design phase. The methodology for the development of the framework will be based on the rationalist approach of generating knowledge through reasoning leading to model-building. Findings – A model that is based on SSADM is proposed for the design development phase of construction projects. In order to shape the project strategy, the model considers the combined role of clients’ requirements with organisation strategy and environmental factors. The paper has shown that it is feasible to increase the automation of the briefing process and enhanced the briefing output. The model here does not diminish the importance of direct communication between the client and the design team. It provides a more structured way of doing so, while taking advantage of vast array of data and technology in order to improve the brief outcome. Research limitations/implications – From practical perspective, the proposed framework is in its formative stage, thus requiring incremental refinement through several case studies. This is particularly true about the AI components of the system which typically rely on extensive data representing the real-case scenarios. Therefore, the work invites further research into the examination of various parts as well as the overall system. Practical implications – There are several ways by which construction projects are procured. There may be fluctuation in their rate of usage, but while there is no indication of any procurement option fading, new ones such as PPP and PFI are periodically introduced. The existence of this diversity is indicative of the fact that the industry tends to respond to problems rather than attempting to instigate a measured solution supported by theoretical underpinning. Subsequently, there have been suggestions of a communication and information discourse between actors and within processes involved in project lifecycle. This project is aimed at addressing the gap in the client-designer communication. The automated approach to brief generation will lead to better briefs while reducing ambiguities as well as the overhead associated with brief generation. Social implications – The quality of project brief has a significant impact on decisions at the design stage. In turn, these decisions will influence all phases of construction project lifecycle. The briefing session and requirement analysis of a construction project can be very difficult for inexperienced clients particularly for complex projects. Therefore, there is potential for the process of client-requirement-analysis to be optimised. The work promises to improve the quality of the briefing process, thus helping clients to realise their intended objectives and minimise resource waste. Originality/value – The work builds on the commonalities of the construction and software development industries and takes advantage of the advancements in the latter. In doing so, project quality is defined quantitatively which is used to develop project strategy in a three-dimensional space. The development of the model was also contingent upon enhancement of artificial neural network structure.
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Oreni, D., R. Brumana, S. Della Torre, and F. Banfi. "SURVEY, HBIM AND CONSERVATION PLAN OF A MONUMENTAL BUILDING DAMAGED BY EARTHQUAKE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5/W1 (May 15, 2017): 337–42. http://dx.doi.org/10.5194/isprs-archives-xlii-5-w1-337-2017.
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Surveying a monumental building damaged by the earthquake means to analyse its geometries, the structural elements, the connection still exist between the different parts, in order to define its state of conservation, to make structural analysis and to plan a proper project of restoration, consolidation, seismic improvement or addition of new elements. The survey of structural geometry represents the first necessary moment of building’ knowledge investigation, to be performed after the securing of the building by the Firefighters or Civil Protection. How and by which instruments the geometric analysis are conducted depends on many factors, not always exclusively on the will of the experts involved in the restoration project, but more often dictated by political, technical, social or economic needs. The accurate geometrical survey is referred as fundamental operation even by national <i>Directive for evaluation and earthquake risk reduction of cultural heritage</i> (GU n. 24 &ndash; 29/01/2008 and 2011 updates), which defines guidelines for preventive interventions on built heritage in order to make the structures less vulnerable in case of earthquake. <br><br> Nowadays, the wide use of tools and accurate surveying techniques makes it possible to achieve an adequate level of accuracy of information related to the buildings, overcoming the difficulties due to accessibility of the damaged structures. <br><br> The geometrical survey of the Basilica of Santa Maria di Collemaggio in L'Aquila, was made by Politecnico di Milano starting from 2013, within the project "Ripartire da Collemaggio" (<a href=" http://www.ungiornoacollemaggio.it/content/2027"target="_blank">http://www.ungiornoacollemaggio.it/content/2027</a>), financed by Eniservizi. The basilica, an important symbol for the community of L'Aquila, was gravely damaged by the earthquake of 6<sup>th</sup> April 2009. The objective of Eni was to turn the restoration of the building in a re-birth moment for all the community. The knowledge step was aimed to plan a restoration project able of returning the basilica to a safe and full use. In the two years knowledge investigation steps (geometric survey, historical and stratigraphic analysis, materials investigation, structures and soils examination, <i>in situ</i> tests and numerical elaborations) was involved an interdisciplinary group of researchers from various Italian universities, Politecnico di Milano, Università La Sapienza in Rome and the University of L'Aquila, called to provide a scientific advice to the Soprintendenza ai Beni Architettonici e Paesaggistici per l’Abruzzo, project manager of the restoration step. In early 2016 the yard of restoration was opened and it is still on-going. <br><br> The geometrical survey of the basilica was aim to investigate, measure and represent the exact geometry of the damaged structures and their three-dimensional complexity; the survey was planned choosing the tools and methods most appropriate in relation with the accessibility and safety of the structures, the operating ranges of acquisition and the level of detail required by the analysis and the project. This meant to integrate global and local surveying techniques, in order to cross relate data derived by different tools and to make the graphical restitution of the entire monumental complex: topography, laser scanning, photogrammetry, but also hands on instruments, at a scale of representation from 1&thinsp;:&thinsp;1 to 1&thinsp;:&thinsp;50. The laser scanner survey, registered in the accurate geomatics network, responded to the need to punctually investigate and interpret the geometrical configuration of different spaces of the Basilica, as well as its structural articulation, enabling a series of horizontal and vertical profiles, as requested by various experts involved in the diagnostic steps. <br><br> HBIM model (Historical Building Information Modeling) of the entire basilica was primarily intended as a tool for stereotomic description of the building and its parts, following the constructive logic of each structural element; then it was used as a support tool for the restoration simulation, project, management and yard. To perform the division of the building in its constructive elements, sometimes it has been used stratigraphic methodologies and instruments of analysis. The entirety of the geometric and structural complexity of the basilica, was guarantee using sophisticated 3D software and drawing complex entities, integrated and stored in the parametric BIM logic. This process has allowed to accurately and timely represent the geometry of the structural elements, of the areas characterized by crushing, empties, variations of the masonry sections and out of plumbs. <br><br> It is on the pillars of the nave that was focused the attention of survey: the will to preserve as much as possible the existing structures, in their material authenticity, required a careful analysis of each individual stone element of each pillar, investigated in its geometry, texture and state of conservation. The aim of the project was to ward a complete replacement of the pillars, preferring instead a removing and replacing intervention of only of the stone ashlars completely deteriorated and no longer recoverable, considering the question of structural safety as fundamental. The HBIM of the basilica had the primary function of connecting into one virtual space all the available data; that model has also been made as a tool for managing the restoration yard, supporting the computation of stone to buy, quarry and grossly slot, saving time on site. <br><br> Different and complementary skills were used in every knowledge and restoration steps; the accurate analysis of the structures made it possible to plan a consolidation and restoration project as close as possible to the structural conception of the existing building, adding only the new structural elements necessary to increase the resistance and to guarantee the safety of the structures, also in case of new earthquake. The Italian <i>Codice dei Beni culturali e del paesaggio</i> (DL n.42/2004), at paragraph 4 of art. 29, states that the restoration includes structural improvements. For the restoration of Collemaggio the solutions chosen were the ones able to be more effective and, at the same time, less invasive, more respectful as possible of the sacredness of the architecture, its highest historical significance, the authenticity of the material and its cultural significance. <br><br> The main principles and criteria of restoration were: maximum correspondence of conservation doctrine, with a focus on improving the structural characteristics compared to the seismic risk; exemplarity of the management practices of the restoration process; exemplarity of innovative techniques; transparency of all processes and accurate communication of cultural and scientific content. Despite of some inevitable compromises, the detailed knowledge of the building allowed to design punctual interventions, inserting new structures where the oldest were collapsed and consolidating damaged elements, in order to improve the global safety of the building but without modifying substantially the structural conception of the stratified basilica.
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Vaquero, Tiago Stegun, José Reinaldo Silva, and J. Christopher Beck. "Post-design analysis for building and refining AI planning systems." Engineering Applications of Artificial Intelligence 26, no. 8 (September 2013): 1967–79. http://dx.doi.org/10.1016/j.engappai.2013.04.006.
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Pill, Shane, Brendon Hyndman, Brendan SueSee, and John Williams. "Physical Education Teachers’ Use of Digital Game Design Principles." Journal of Teaching in Physical Education 40, no. 1 (January 1, 2021): 1–9. http://dx.doi.org/10.1123/jtpe.2019-0036.
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Purpose: The research applies a multidisciplinary perspective to create knowledge and insight about the opportunities that digital game design principles offer to physical education (PE) pedagogy. Methods: Data were initially collected through an appreciative inquiry (AI). AI offers an alternative research perspective to critical theory that has dominated the investigation of the work of PE teachers. This study uniquely used AI with a narrative approach and multidisciplinary analysis to examine two teachers’ use of digital game design pedagogy in PE. Results: It was found that the teachers were motivated to use digital game design principles to provide students with means to solve problems, manage learning motivations, evaluate progress, and gain control over their learning in ways that are not normally associated with the common PE method. Conclusion: The two examples provided illustrate the generative potential of AI research combined with a multidisciplinary perspective directed at examples of pedagogical change in PE.
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FENVES, STEVEN J. "Building a community." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, no. 1 (January 2007): 15. http://dx.doi.org/10.1017/s0890060407070072.
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Artificial Intelligence for Engineering Design, Analysis and Manufacturing has accomplished a lot, but we still have far to go in building a community. I was reminded of this earlier this year when I attended a workshop on a topic within the scope of AI EDAM's coverage. In addition to engineers and educators, workshop speakers included a linguist studying languages used by professionals, an ethnographer studying CAD users and a psychologist studying innovation. All three social scientists started their presentations essentially the same way:
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Gyory, Joshua T., Binyang Song, Jonathan Cagan, and Christopher McComb. "COMMUNICATION IN AI-ASSISTED TEAMS DURING AN INTERDISCIPLINARY DRONE DESIGN PROBLEM." Proceedings of the Design Society 1 (July 27, 2021): 651–60. http://dx.doi.org/10.1017/pds.2021.65.
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AbstractHuman-artificial intelligent (AI) - assisted teaming is becoming a strategy for coalescing the complementary strengths of humans and computers to solve difficult tasks. Yet, there is still much to learn regarding how the integration of humans with AI agents into a team affects human behavior. Accordingly, this work begins to inform this research gap by focusing specifically on how the communication structure and interaction changes within AI-assisted human teams. The underlying discourse data for this work originates from a prior research study in which teams solve an interdisciplinary drone design and path-planning problem. Several metrics are employed in this work to study team discourse, including count, diversity, content richness, and semantic coherence. Results show significant differences in communication behavior in AI-assisted teams including more diversity and frequency in communication, more exchange of information regarding principal design parameters and problem-solving strategies, and more cohesion. Overall, this work takes meaningful steps towards understanding the effects of AI agents on human behavior in teams, critical for fully building effective human-AI hybrid teams in the future.
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Selim, Maher, Ryan Zhou, Wenying Feng, and Peter Quinsey. "Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design." Energies 14, no. 1 (January 5, 2021): 247. http://dx.doi.org/10.3390/en14010247.
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Building safe, reliable, fully automated energy smart grid systems requires a trustworthy electric load forecasting system. Recent work has shown the efficacy of Long Short-Term Memory neural networks in energy load forecasting. However, such predictions do not come with an estimate of uncertainty, which can be dangerous when critical decisions are being made autonomously in energy production and distribution. In this paper, we present methods for evaluating the uncertainty in short-term electrical load predictions for both deep learning and gradient tree boosting. We train Bayesian deep learning and gradient boosting models with real electric load data and show that an uncertainty estimate may be obtained alongside the prediction itself with minimal loss of accuracy. We find that the uncertainty estimates obtained are robust to changes in the input features. This result is an important step in building reliable autonomous smart grids.
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Khairulzaman, Hanis Adibah, and Fathoni Usman. "Automation in Civil Engineering Design in Assessing Building Energy Efficiency." International Journal of Engineering & Technology 7, no. 4.35 (November 30, 2018): 722. http://dx.doi.org/10.14419/ijet.v7i4.35.23096.
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Automation in Civil Engineering design is no longer unfamiliar anymore in the construction industry. It brings great efficiency gains and has reduced many of the complicated aspects of design. Automation has been developing since over the last four decades where advancement of computer hardware and in line with software developments has enabled various sort of design to be computerized. The design process is now more fast and accurate, and the entire process has been outstandingly improved by the available technology. However, the problems related to computerized design, started from the possibilities for unrecognized or unanticipated consequences as the design becomes more complicated. In line with it is the global warming issue that has been threatening the human being. Therefore, this paper reviews the design automation and its application in civil engineering design emphasizing in assessing the energy efficiency of buildings, which includes background information, its application, advantages, and challenges and also suggestions to further improve the automation in civil engineering design by artificial intelligence (AI). It was discovered that since over the past few years, BIM implementation in Civil Engineering design has started to be adopted massively in construction industry due to its tremendous advantages. In this paper, a new flow process in design automation in assessing building sustainability has been proposed by implementing AI BIM which is linked with MyCREST, a green rating tool in Malaysia in order to assist designers to conceptually design for a more energy efficient building.
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Liu, Chunyan, Zhe Ren, and Sen Liu. "Using Design and Graphic Design with Color Research in AI Visual Media to Convey." Journal of Sensors 2021 (September 15, 2021): 1–11. http://dx.doi.org/10.1155/2021/8153783.
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With the development of science and technology and social progress, people can reveal design information in different forms on a daily basis. The more common visual design information, the more important its development status. In visual communication design, the two most important factors are color and image. These two factors can play a great role and can attract the public’s attention. This article is based on AI technology to study the design of color matching and image application in visual media communication design, aiming to innovate the method of visual media communication design, breaking through the traditional color matching and image application, so as to better use color art in visual communication design and graphic art and design excellent works. First, this article elaborates on the concept, main components, and influencing factors of visual communication design and also introduces the application of AI technology, then designs a visual media communication design model based on AI technology, and finally analyzes the current status of visual media communication design, usage, and satisfaction. From the analytical analysis, it appears that the optical media interface is commonly used for packaging design, responding to 45.6%; it is used more in the film and television media industry, reaching 68%; 49% of the public are very satisfied with AI visual media communication design. The above data fully shows that the application of AI visual media communication design is very wide, and the color matching and image use are very prominent, and the public is willing to accept AI visual media communication design works.
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Arabi, Alya A. "Artificial intelligence in drug design: algorithms, applications, challenges and ethics." Future Drug Discovery 3, no. 2 (June 2021): FDD59. http://dx.doi.org/10.4155/fdd-2020-0028.
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The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.
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Goel, Ashok K., and David A. Joyner. "Using AI to Teach AI: Lessons from an Online AI Class." AI Magazine 38, no. 2 (July 1, 2017): 48–59. http://dx.doi.org/10.1609/aimag.v38i2.2732.
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In fall 2014, we launched a foundational course in artificial intelligence (CS7637: Knowledge-Based AI) as part of the Georgia Institute of Technology's Online Master of Science in Computer Science program. We incorporated principles and practices from the cognitive and learning sciences into the development of the online AI course. We also integrated AI techniques into the instruction of the course, including embedding 100 highly focused intelligent tutoring agents in the video lessons. By now, more than 2000 students have taken the course. Evaluations have indicated that OMSCS students enjoy the course compared to traditional courses, and more importantly, that online students have matched residential students' performance on the same assessments. In this article, we present the design, delivery, and evaluation of the course, focusing on the use of AI for teaching AI. We also discuss lessons we learned for scaling the teaching and learning of AI.
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Yigitcanlar, Tan, Kevin C. Desouza, Luke Butler, and Farnoosh Roozkhosh. "Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature." Energies 13, no. 6 (March 20, 2020): 1473. http://dx.doi.org/10.3390/en13061473.
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Artificial intelligence (AI) is one of the most disruptive technologies of our time. Interest in the use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban locations that are enabled by community, technology, and policy to deliver productivity, innovation, livability, wellbeing, sustainability, accessibility, good governance, and good planning—has increased the demand for AI-enabled innovations. There is, nevertheless, no scholarly work that provides a comprehensive review on the topic. This paper generates insights into how AI can contribute to the development of smarter cities. A systematic review of the literature is selected as the methodologic approach. Results are categorized under the main smart city development dimensions, i.e., economy, society, environment, and governance. The findings of the systematic review containing 93 articles disclose that: (a) AI in the context of smart cities is an emerging field of research and practice. (b) The central focus of the literature is on AI technologies, algorithms, and their current and prospective applications. (c) AI applications in the context of smart cities mainly concentrate on business efficiency, data analytics, education, energy, environmental sustainability, health, land use, security, transport, and urban management areas. (d) There is limited scholarly research investigating the risks of wider AI utilization. (e) Upcoming disruptions of AI in cities and societies have not been adequately examined. Current and potential contributions of AI to the development of smarter cities are outlined in this paper to inform scholars of prospective areas for further research.
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Wanigarathna, Nadeeshani, Fred Sherratt, Andrew Price, and Simon Austin. "Design re-use: critical application of healthcare building design evidence." Engineering, Construction and Architectural Management 26, no. 3 (April 15, 2019): 350–66. http://dx.doi.org/10.1108/ecam-07-2017-0118.
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Purpose The re-use of good design solutions is a key source of evidence and knowledge in the design of healthcare buildings. However, due to the unique nature of healthcare built environments, the critical application of this evidence is of paramount importance. The purpose of this paper is to investigate the features of such critical application and identify the aspects that need to be considered during the re-use of good designs. Design/methodology/approach Data from three case studies of hospital designs in the UK were used to explore the processes behind the adaption and re-use of design solutions during the design of healthcare buildings. Data were thematically analysed to distinguish the aspects that should be carefully compared and contrasted during design re-use. Findings Existing designs of healthcare buildings should be captured and evaluated along with: patient demographics, care models of the hospital, other local departmental needs and facility operational aspects in order to ensure the effectiveness of re-use. In addition, properly introducing the design to the users is also a part of successful design re-use. Research limitations/implications The findings of this research were integrated into a framework to support healthcare designers on the effective re-use of good designs. This data-driven framework could be validated further with design practitioners. Further, this research relied on memory recall of the interviewees and the accuracy and completeness of documentary records. Practical implications This research provides details of how healthcare built environment designs are embedded in project-unique circumstances. The results could therefore be used to develop meaningful and informative evaluation mechanisms for new and re-used healthcare building design features. Originality/value This research extends the understanding of the critical application of healthcare design evidence, by explaining how healthcare design solutions should be evaluated during the design process.
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Jadhav, H. T., and S. J. Hoff. "Use of Air Infiltration in Swine Housing Ventilation System Design." Applied Engineering in Agriculture 35, no. 3 (2019): 325–38. http://dx.doi.org/10.13031/aea.12965.
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Abstract.The objective of this research was to develop and analyze the procedure for using recent air infiltration (AI) data collected from commercial swine finishing rooms (SFRs) in the design of negative pressure mechanical ventilation systems (VSs). Air infiltration is an integral part of any ventilation process. Infiltration reduces the pressure differential across planned inlets and at very low pressure differences, cold air jets may drop directly on the animals causing significant discomfort. In this article, a design procedure is proposed for swine housing ventilation systems with the influence of air infiltration included. The method was used on one SFR for which air infiltration data was collected by in-field testing. The air-jet throw, jet momentum number, a newly developed coverage factor, and Archimedes number were used to assess the influence of infiltration on predicted air-jet and fresh-air distribution and to help guide the design of planned inlets in SFR VSs with known infiltration. The analysis completed quantifies the severity of AI on air-jet and air distribution performance, and suggests that for the analysis room to ventilate properly requires a 50% reduction in AI levels beyond field measured curtain and fan infiltration. The analysis completed suggests a method for systematically planning three-dimensional ceiling inlet placement and operation and provides design guidance for new ceiling inlets suitable for SFR VSs. Keywords: Air distribution, Air-jets, Archimedes number, Infiltration, Jet Momentum Number.
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Kulsha, A. Y., M. A. Klimovich, M. V. Sterjanov, V. N. Tesluk, and N. G. Egorova. "Mechatronic device of AI systems." Doklady BGUIR 18, no. 4 (June 25, 2020): 28–35. http://dx.doi.org/10.35596/1729-7648-2020-18-4-28-35.
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The purpose of this work is to demonstrate the phased development of a mechatronic device, to describe the development process of the device design and software, to demonstrate the application of the theory of selected sections of mathematics and physics in robotics, and, in particular, linear algebra, geometry, computational mathematics, discrete mathematics and mechanics. The study was based on the mechatronic device, which had been designed by the youth team of the Republic of Belarus in preparation for the international robotics competition “First Global Challenge 2019”, which became the winner of this event. The article describes in detail the statement of the problem at this competition, identifies the basic requirements for the robot being built, provides a general description of the stages of building the device both during the design process and during the immediate implementation of the project, as well as substantiates the engineering decisions that were made during the design process. The stages that are of the greatest interest in terms of applying the theory of applied physics and mathematics are described in more detail. Particular attention is paid to the design and development of structural modules, as well as to the development of software for controlling the device. The robot is an experimental model that can be used in further research in the field of artificial intelligence, machine learning, automation systems, and is also a potential platform for teaching robotics at the level of specialized secondary and higher education.
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Wang, Nengmou, and Hojjat Adeli. "SUSTAINABLE BUILDING DESIGN." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 20, no. 1 (March 10, 2014): 1–10. http://dx.doi.org/10.3846/13923730.2013.871330.
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Sustainable building design has become a wide and multidisciplinary research endeavor including mechanical, electrical, electronic, communication, acoustic, architectural, and structural engineering. It involves the participation of owners, contractors, suppliers and building users. There has been a lot of talk about sustainable buildings in the past few years. Most of the published research is concerned with saving energy and water and making the buildings more environmentally friendly by, say, reducing the carbon emissions. In this article, sustainable building design is reviewed from the viewpoint of structural engineering. Different strategies presented in the literature are summarized. Finally, the authors argue that the next big leap in sustainable building design should come from the integration of the smart structure technology including the use of hybrid and semi-active vibration controllers that can result in substantially lighter and more efficient structures.
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Fathi, Soheil, Ravi S. Srinivasan, Charles J. Kibert, Ruth L. Steiner, and Emre Demirezen. "AI-Based Campus Energy Use Prediction for Assessing the Effects of Climate Change." Sustainability 12, no. 8 (April 16, 2020): 3223. http://dx.doi.org/10.3390/su12083223.
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In developed countries, buildings are involved in almost 50% of total energy use and 30% of global annual greenhouse gas emissions. The operational energy needs of buildings are highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based energy modeling of buildings, Artificial Intelligence (AI) has the capability to provide faster and higher accuracy estimates, given buildings’ historic energy consumption data. Looking beyond individual building levels, forecasting building energy performance can help city and community managers have a better understanding of their future energy needs, and to plan for satisfying them more efficiently. Focusing at an urban scale, this research develops a campus energy use prediction tool for predicting the effects of long-term climate change on the energy performance of buildings using AI techniques. The tool comprises four steps: Data Collection, AI Development, Model Validation, and Model Implementation, and can predict the energy use of campus buildings with 90% accuracy. We have relied on energy use data of buildings situated in the University of Florida, Gainesville, Florida (FL). To study the impact of climate change, we have used climate properties of three future weather files of Gainesville, FL, developed by the North American Regional Climate Change Assessment Program (NARCCAP), represented based on their impact: median (year 2063), hottest (2057), and coldest (2041).
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Horowitz, Michael C., and Lauren Kahn. "How Joe Biden can use confidence-building measures for military uses of AI." Bulletin of the Atomic Scientists 77, no. 1 (January 2, 2021): 33–35. http://dx.doi.org/10.1080/00963402.2020.1860331.
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MAHER, MARY LOU. "Blurring the boundaries." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, no. 1 (January 2007): 7–10. http://dx.doi.org/10.1017/s0890060407070047.
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Early research in artificial intelligence (AI) in design was “AI in the large.” Artificial intelligence promised a revolutionary approach to assisting or automating design processes, separate from other computational systems. As the field matures, “AI in the small” has the most potential. Rather than seeing large AI programs, we see the results of research in AI in design providing the glue that holds larger systems together using reasoning systems that represent or manage processes, information, and interaction devices that use conventional procedural programming; effectively blurring the boundaries between AI and non-AI. Alternatively, we see research in AI in design as playing a role in blurring the boundaries between the physical and the virtual.
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Su, Ling-chu, Xiqiang Wu, Xiaoning Zhang, and Xinyan Huang. "Smart performance-based design for building fire safety: Prediction of smoke motion via AI." Journal of Building Engineering 43 (November 2021): 102529. http://dx.doi.org/10.1016/j.jobe.2021.102529.
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Yigitcanlar, Tan, Luke Butler, Emily Windle, Kevin C. Desouza, Rashid Mehmood, and Juan M. Corchado. "Can Building “Artificially Intelligent Cities” Safeguard Humanity from Natural Disasters, Pandemics, and Other Catastrophes? An Urban Scholar’s Perspective." Sensors 20, no. 10 (May 25, 2020): 2988. http://dx.doi.org/10.3390/s20102988.
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In recent years, artificial intelligence (AI) has started to manifest itself at an unprecedented pace. With highly sophisticated capabilities, AI has the potential to dramatically change our cities and societies. Despite its growing importance, the urban and social implications of AI are still an understudied area. In order to contribute to the ongoing efforts to address this research gap, this paper introduces the notion of an artificially intelligent city as the potential successor of the popular smart city brand—where the smartness of a city has come to be strongly associated with the use of viable technological solutions, including AI. The study explores whether building artificially intelligent cities can safeguard humanity from natural disasters, pandemics, and other catastrophes. All of the statements in this viewpoint are based on a thorough review of the current status of AI literature, research, developments, trends, and applications. This paper generates insights and identifies prospective research questions by charting the evolution of AI and the potential impacts of the systematic adoption of AI in cities and societies. The generated insights inform urban policymakers, managers, and planners on how to ensure the correct uptake of AI in our cities, and the identified critical questions offer scholars directions for prospective research and development.
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Bédard, Claude. "Computers in building design." Canadian Journal of Civil Engineering 15, no. 4 (August 1, 1988): 673–78. http://dx.doi.org/10.1139/l88-087.
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The process of designing buildings presents characteristics that set it apart from the design process in other engineering fields. Traditionally, computers have been assigned the number-crunching and bookkeeping tasks in building design activities. Also, few participants in the design team have used computers on a regular basis. However, many changes are taking place which will affect the way a building design is carried out and the contribution computers could make in the process. All factors appear to advocate greater use of computers to achieve better designs. Three active areas of development are finally presented to illustrate new roles computers could assume in future building design projects. Key words: building design process, algorithm, synthesis/analysis, preliminary design, integration.