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Ekung, Samuel, Adeniran Lashinde, and Emmanuel Adu. "Critical Risks to Construction Cost Estimation." Journal of Engineering, Project, and Production Management 11, no. 1 (January 1, 2021): 19–29. http://dx.doi.org/10.2478/jeppm-2021-0003.
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AbstractThe prevalence of cost overrun in project delivery suggests an acute dearth of inclusive understanding of the effect of risks on construction cost estimation. In aberrant to the generic assumptions, customary to inquiries in construction risk researches, this paper appraised critical construction estimating risks. The study evaluated the sources, frequency and significance of construction estimating risks, using data from a questionnaire survey of 206 quantity surveyors in Nigeria. The data were analysed using factor analysis, Fussy Set Theory, Terrell Transformation Index (TTI), and Kruskal Wallis H tests. The results showed that estimating risks are correlate seven principal sources, namely: estimating resources, construction knowledge, design information, economic condition, the expertise of estimator, geographic factor, cost data, and project factors (λ, > 0.70 <1.0). Twenty-nine risk factors likewise emerged critical construction estimation risks (TTI, 69-87 > 65 percent) and the top three were low construction knowledge, inaccurate cost information and changes in government regulations (factor scores > 0.60 > 0.50). The awareness and accurate assessment of these risks into project cost estimation would reduce cost overrun. The study, therefore, recommends synergies between projects’ internal/ external environments for proper scoping of these risks into project estimates.
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Sonmez, Rifat. "Conceptual cost estimation of building projects with regression analysis and neural networks." Canadian Journal of Civil Engineering 31, no. 4 (August 1, 2004): 677–83. http://dx.doi.org/10.1139/l04-029.
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Conceptual cost estimates play a crucial role in initial project decisions, although scope is not finalized and very limited design information is available during early project stages. In this paper, the advantages and disadvantages of the current conceptual cost estimation methods are discussed and the use of regression, neural network, and range estimation techniques for conceptual cost estimation of building projects are presented. Historical cost data of continuing care retirement community projects were compiled to develop regression and neural network models. Three linear regression models were considered to identify the significant variables affecting project cost. Two neural network models were developed to examine the possible need for nonlinear or interaction terms in the regression model. Prediction intervals were constructed for the regression model to quantify the level of uncertainty for the estimates. Advantages of simultaneous use of regression analysis, neural networks, and range estimation for conceptual cost estimating are discussed.Key words: conceptual cost estimation, regression analysis, neural networks, range estimation.
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Kitchenham, Barbara A., and N. R. Taylor. "Software project development cost estimation." Journal of Systems and Software 5, no. 4 (November 1985): 267–78. http://dx.doi.org/10.1016/0164-1212(85)90026-3.
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Galli, Brian J. "Cost Estimation Methods in Quality Management and Continuous Improvement." International Journal of Service Science, Management, Engineering, and Technology 12, no. 1 (January 2021): 38–61. http://dx.doi.org/10.4018/ijssmet.2021010103.
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This paper investigated cost estimation methods and continuous improvement in terms of project management planning, as well as the factors that influenced these actions. Estimating the cost was one of the most imperative tasks to be done by the managers for a project. Key factors, such as cost, size, schedule, quality, people resources, maintenance costs, and complexity, were usually estimated in the beginning of project development. The techniques used for cost estimation included data composed from past projects that were combined with mathematical formulae to get the closest estimation. In regards to continuous improvement, the four-step quality model (PDCA cycle) was used as an ongoing effort to improve products or services. PDCA stands for plan, do, check, and act, which were the steps to successfully implement change. Notably, project management, when tasked with cost estimation and continuous improvement, was challenged to cope with evolving and situational alterations, which required a different set of skills.
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Yang, Ming, Yuan Li, and Yong Chao Wang. "Using Gray Forecasting Estimates Cost of Aviation Project." Materials Science Forum 532-533 (December 2006): 725–28. http://dx.doi.org/10.4028/www.scientific.net/msf.532-533.725.
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China aviation project has little sample and the traditional method can not estimate the project cost accurately. This paper proposes a method estimating aviation project cost based on gray systematic theory. We divide aviation product into systems or parts and regard them as cost estimation unit. The gray forecasting model GM (1, 1) was set up based on historical cost data of cost estimation unit. If the model’s precision doesn’t meet with the requirement of aviation project cost estimation, the revision model GM (1, 1) must be set up to raise the estimation precision The experimental results show the method can get the more high-accuracy estimation value using the little sample and is suitable for the cost estimation of the our country aviation project.
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Sridarran, Pournima, Kaushal Keraminiyage, and Leon Herszon. "Improving the cost estimates of complex projects in the project-based industries." Built Environment Project and Asset Management 7, no. 2 (May 11, 2017): 173–84. http://dx.doi.org/10.1108/bepam-10-2016-0050.
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Purpose Project-based industries face major challenges in controlling project cost and completing within the budget. This is a critical issue as it often connects to the main objectives of any project. However, accurate estimation at the beginning of the project is difficult. Scholars argue that project complexity is a major contributor to cost estimation inaccuracies. Therefore, recognising the priorities of acknowledging complexity dimensions in cost estimation across similar industries is beneficial in identifying effective practices to reduce cost implications. Hence, the purpose of this paper is to identify the level of importance given to different complexity dimensions in cost estimation and to recognise best practices to improve cost estimation accuracy. Design/methodology/approach An online questionnaire survey was conducted among professionals including estimators, project managers, and quantity surveyors to rank the identified complexity dimensions based on their impacts in cost estimation accuracy. Besides, in-depth interviews were conducted among experts and practitioners from different industries, in order to extract effective practices to improve the cost estimation process of complex projects. Findings Study results show that risk, project and product size, and time frame are the high-impact complexity dimensions on cost estimation, which need more attention in reducing unforeseen cost implications. Moreover, study suggests that implementing a knowledge sharing system will be beneficial to acquire reliable and adequate information for cost estimation. Further, appropriate staffing, network enhancement, risk management, and circumspect estimation are some of the suggestions to improve cost estimation of complex projects. Originality/value The study finally provides suggestions to improve cost estimation in complex projects. Further, the results are expected to be beneficial to learn lessons from different industries and to exchange best practices.
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Kurasova, O., V. Marcinkevičius, V. Medvedev, and B. Mikulskienė. "Early Cost Estimation in Customized Furniture Manufacturing Using Machine Learning." International Journal of Machine Learning and Computing 11, no. 1 (January 2021): 28–33. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1010.
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Accurate cost estimation at the early stage of a construction project is a key factor in the success of most projects. Many difficulties arise when estimating the cost during the early design stage in customized furniture manufacturing. It is important to estimate the product cost in the earlier manufacturing phase. The cost estimation is related to the prediction of the cost, which commonly includes calculation of the materials, labor, sales, overhead, and other costs. Historical data of the previously manufactured products can be used in the cost estimation process of the new products. In this paper, we propose an early cost estimation approach, which is based on machine learning techniques. The experimental investigation based on the real customized furniture manufacturing data is performed, results are presented, and insights are given.
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Emhjellen, Magne, Kjetil Emhjellen, and Petter Osmundsen. "Cost Estimation Overruns in the North Sea." Project Management Journal 34, no. 1 (March 2003): 23–29. http://dx.doi.org/10.1177/875697280303400104.
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Recently, a Norwegian government report on the cost overruns North Sea projects was presented (NOU 1999:11). It concluded that there was a 25% increase in development costs from project sanction (POD, Plan for Operation and Development) to last CCE (Capital Cost Estimate) for the 11 oil field projects investigated. Many reasons like unclear project assumptions in early phase, optimistic interpolation of previous project assumptions, optimistic estimates, and underestimation of uncertainty were given as reasons for overruns. In this paper we highlight the possibility that the cost overruns are not necessarily all due to the reasons given, but also to an error in the estimation and reporting of the capital expenditure cost (CAPEX). Usually the CAPEX is given by a single cost figure, with some indication of its probability distribution. The oil companies report, and are required to do so by government authorities, the estimated 50/50 (median) cost estimate instead of the estimated expected value cost estimate. We demonstrate how the practice of using a 50/50 (median) CAPEX estimate for the 11 projects, when the cost uncertainty distributions are asymmetric, may explain at least part of the “overruns.” Hence, we advocate changing the practice of using 50/50 cost estimates instead of expected value cost estimates for project management and decision purposes.
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Relich, Marcin. "Computational Intelligence for Estimating Cost of New Product Development." Foundations of Management 8, no. 1 (January 1, 2016): 21–34. http://dx.doi.org/10.1515/fman-2016-0002.
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AbstractThis paper is concerned with estimating cost of various new product development phases with the use of computational intelligence techniques such as neural networks and fuzzy neural system. Companies tend to develop many new products simultaneously and a limited project budget imposes the selection of the most promising new product development projects. The evaluation of new product projects requires cost estimation. The model of cost estimation contains product design, prototype manufacturing and testing, and it is specified in terms of a constraint satisfaction problem. The illustrative example presents comparative analysis of estimating product development cost using computational intelligence techniques and multiple regression model.
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Bhawana Verma, Satish Kumar Alaria. "Design & Analysis of Cost Estimation for New Mobile-COCOMO Tool for Mobile Application." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 1 (January 31, 2019): 27–34. http://dx.doi.org/10.17762/ijritcc.v7i1.5222.
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Software cost estimation is a resource forecasting method, which is required by the software development process. However, estimating the workload, schedule and cost of a software project is a complex task because it involves predicting the future using historical project data and extrapolating to see future values. For cost estimates for software projects, several methods are used. Among the various software cost estimation methods available, the most commonly used technology is the COCOMO method. Similarly, to calculate software costs, there are several cost estimating tools available for software developers to use. But these released cost estimation tools can only provide parameters (i.e. cost, development time, average personnel) for large software with multiple lines of code. However, if a software developer wants to estimate the cost of a small project that is usually a mobile application, the available tools will not give the right results. Therefore, to calculate the cost of the mobile application, the available cost estimation method COCOMO II is improved to a new model called New Mobile COCOMO Tool. The New Mobile COCOMO tool developed specifically for mobile applications is a boon for software developers working in small software applications because it only includes important multipliers that play a vital role in estimating the cost of developing mobile applications. Therefore, the objective of this paper is to propose a cost estimation model with a special case of COCOMO II, especially for mobile applications, which calculates the person-month, the programmed time and the average personnel involved in the development of any mobile app.
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Moselhi, Osama, and Mohammadjavad Arabpour Roghabadi. "Risk quantification using fuzzy-based Monte Carlo simulation." Journal of Information Technology in Construction 25 (February 4, 2020): 87–98. http://dx.doi.org/10.36680/j.itcon.2020.005.
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Estimating cost contingency of construction projects depends largely on data captured from previous projects and/or experience and judgment of members of project team. Mote Carlo simulation is commonly used in estimating contingency, where its accuracy was reported to depend on number of iterations used in the simulation process, probability density functions associated with each project cost item being considered and the correlation among these cost items. The literature reveals that the latter is the most important issue for accurate estimate of contingency. It, however, requires the calculation of coefficients of correlation among cost items based on captured historical records of cost data. Subjective correlation was introduced to alleviate the difficulties associated with the calculation of these coefficients. This paper presents a newly developed method for cost contingency estimation that considers subjective correlations and allows for contingency estimation with and without computer simulation. Unlike the methods reported in the literature, the present method considers uncertainty associated with the coefficients of correlation and utilizes earlier work of the first author in calculating the variance of total project cost. It also allows for assessing the impact of variable covariance matrix on the estimated project cost using a simple and user-friendly computational platform. The application of the developed method on cost data captured from two databases demonstrates its use and accuracy in estimating cost contingency. The results are compared to those produced by others using Monte Carlo Simulation with and without correlation using an actual project data.
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Dunovic, Ivana Burcar, Kristijan Robert Prebanic, and Pavao Durrigl. "Method for Base Estimation of Construction Time for Linear Projects in Front-end Project Phases." Organization, Technology and Management in Construction: an International Journal 12, no. 2 (November 13, 2020): 2312–26. http://dx.doi.org/10.2478/otmcj-2018-0026.
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AbstractEven though horizontally linear projects have low complexity schedules, they are still not successful in meeting planned time. The deadlines are mostly based on estimations done in front-end project development when limited data are available. Early time estimation models in literature rely on few variables and, almost in all cases, one of them is the estimated cost. Early cost estimations can significantly deviate from actual costs and thus lead to unreliable time estimation. Time estimation models based on neural network and other alternative methods require databases and software, which complicates the process of time estimation. The purpose of this paper is to bridge the gap of scarce time estimation models and unreliable time estimates by developing a new method for time estimation. This research has been done on one large sewer system project. The case study shows how to extract several continuous activities for a pipeline project chosen from a sewer system. Moreover, a new algorithm for the calculation of project duration is devised based on the existing equation related to the linear scheduling method, and this algorithm works with continuous activities. The new method for construction time estimation is based on the extraction of linear continuous activities, usage of the algorithm for identification of minimal buffer between activities, and calculation of the project duration. To verify the algorithm, this method is used on another pipeline project from a sewer system. The limitation is that this method can be used only for base estimation. Further research needs to be done to include uncertainties and risks in the method.
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Rahikkala, Jurka, Ville Leppänen, Jukka Ruohonen, and Johannes Holvitie. "Top management support in software cost estimation." International Journal of Managing Projects in Business 8, no. 3 (June 1, 2015): 513–32. http://dx.doi.org/10.1108/ijmpb-11-2014-0076.
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Purpose – A cost estimate is considered to have a high impact on software project success. Because of this, different methodologies for creating an accurate estimate have been studied over decades. Many methodologies produce accurate results, when used properly. However, software projects still suffer from inaccurate estimates. The disparity may result from organisational hindrances. This paper focuses on top management support (TMS) for software cost estimation (SCE). The purpose of this paper is to identify current practices and attitudes of top management involvement in SCE, and to analyse the relationship between these two and project success. Design/methodology/approach – A list of 16 TMS practices for SCE has been developed. A survey was conducted to capture the frequency of use and the experienced importance of support practices. Data has been collected from 114 software professionals in Finland. Correlations between the frequency of use, attitudes and project success were analysed. Findings – Top management invests a significant amount of attention in SCE. The extent of use and experienced importance do not correlate strongly with each other or project success. Research limitations/implications – The results may lack generalisability. Researchers are encouraged to validate the results with further studies. Practical implications – Software professionals invite senior managers to participate in SCE. A list of practices for participating is provided. Originality/value – This paper suggests a list of 16 TMS practices for SCE. The paper also reports on the extent of use and experienced importance of practices, and the correlations between these two and project success.
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Stamelos, Ioannis, and Lefteris Angelis. "Managing uncertainty in project portfolio cost estimation." Information and Software Technology 43, no. 13 (November 2001): 759–68. http://dx.doi.org/10.1016/s0950-5849(01)00183-5.
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Chan, Swee Lean, and Moonseo Park. "Project cost estimation using principal component regression." Construction Management and Economics 23, no. 3 (March 2005): 295–304. http://dx.doi.org/10.1080/01446190500039812.
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Najadat, Hassan, Izzat Alsmadi, and Yazan Shboul. "Predicting Software Projects Cost Estimation Based on Mining Historical Data." ISRN Software Engineering 2012 (April 10, 2012): 1–8. http://dx.doi.org/10.5402/2012/823437.
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In this research, a hybrid cost estimation model is proposed to produce a realistic prediction model that takes into consideration software project, product, process, and environmental elements. A cost estimation dataset is built from a large number of open source projects. Those projects are divided into three domains: communication, finance, and game projects. Several data mining techniques are used to classify software projects in terms of their development complexity. Data mining techniques are also used to study association between different software attributes and their relation to cost estimation. Results showed that finance metrics are usually the most complex in terms of code size and some other complexity metrics. Results showed also that games applications have higher values of the SLOCmath, coupling, cyclomatic complexity, and MCDC metrics. Information gain is used in order to evaluate the ability of object-oriented metrics to predict software complexity. MCDC metric is shown to be the first metric in deciding a software project complexity. A software project effort equation is created based on clustering and based on all software projects’ attributes. According to the software metrics weights values developed in this project, we can notice that MCDC, LOC, and cyclomatic complexity of the traditional metrics are still the dominant metrics that affect our classification process, while number of children and depth of inheritance are the dominant from the object-oriented metrics as a second level.
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Dang, Chau Ngoc, and Long Le-Hoai. "Revisiting storey enclosure method for early estimation of structural building construction cost." Engineering, Construction and Architectural Management 25, no. 7 (August 20, 2018): 877–95. http://dx.doi.org/10.1108/ecam-07-2015-0111.
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Purpose The purpose of this paper is to develop several predictive models for estimating the structural construction cost and establish range estimation for the structural construction cost using design information available in early stages of residential building projects. Design/methodology/approach Information about residential building projects is collected based on project documents from construction companies with regard to the design parameters and the actual structural construction costs at completion. Storey enclosure method (SEM) is fundamental for determining the building design parameters, forming the potential variables and developing the cost estimation models using regression analysis. Nonparametric bootstrap method is used to establish range estimation for the structural construction cost. Findings A model which is developed from an integration of advanced SEM, principle component analysis and regression analysis is robust in terms of predictability. In terms of range estimation, cumulative probability-based range estimates and confidence intervals are established. While cumulative probability-based range estimates provide information about the level of uncertainty included in the estimate, confidence intervals provide information about the variability of the estimate. Such information could be very crucial for management decisions in early stages of residential building projects. Originality/value This study could provide practitioners with a better understanding of the uncertainty and variability included in the cost estimate. Hence, they could make effective improvements on cost-related management approaches to enhance project cost performance.
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Moynihan, Gary P., and Anh D. Chau. "Application of Decision Support Technology for Conceptual Cost Estimation." Engineering Management Research 8, no. 2 (September 13, 2019): 30. http://dx.doi.org/10.5539/emr.v8n2p30.
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Conceptual cost estimates are often made at the beginning of the project when project scope is not yet well defined. Hence, predicting the conceptual costs on time, with high accuracy, presents a considerable challenge. One potential solution is to more effectively utilize historical data via integration with predictive analytical models. In this project, a decision support system was developed which predicts conceptual costs of construction projects and supports decision-making for long-term capital planning in public universities. The prototype system was developed based on historical data for roofing projects at the University of Alabama. We collected this historical data via a web-based data entry form subsystem. The developed system uses ridge regression models to train historical data. This system has a user-friendly interface and supports what-if analysis, allowing the user to see multiple scenarios of the estimation. The system also encompasses capabilities to forecast the effects of inflation on multi-year projects. Subsequent validation has demonstrated improvement in the resulting accuracy of the conceptual estimates.
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Fanny, Fanny. "A Simple Method for Cost Estimating and Controlling." International Journal of New Media Technology 5, no. 2 (March 19, 2019): 58–61. http://dx.doi.org/10.31937/ijnmt.v5i2.833.
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Based on software project survey from Standish Group’s “CHAOS Report”, 29% of software projects in large enterprises can be delivered on time and not over budget, 53% of software project were over budget or had some problem with the schedule, and 18% of software project were failed. This means that cost management is very important to prevent the possibility of project’s failure. Therefore, in this paper, we provide and recommend some methods and technique in order to do cost estimation and cost controlling as the crucial part of project cost management. We recommend Activity-Base Costing method for cost estimation and Earned Value Analysis for cost controlling. As the result of this paper, these two methods can be applied properly to real project and ease the project manager to estimate and control the cost of the project.
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Ba’abbad, Ibrahim Mohammad, and M. Rizwan Jameel Qureshi. "Quality Extended Use Case Point (QUCP): An Improved Cost Estimation Method." International Journal of Computer Science and Mobile Computing 10, no. 6 (June 30, 2021): 1–9. http://dx.doi.org/10.47760/ijcsmc.2021.v10i06.001.
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The quality of a product is one of the major interests of the manufacturing process in all industries. The software industry imposes to construct a project with several phases to ensure producing high-quality software. A software development company estimates time, effort and cost of the project during planning phase. It is important to have accurate estimations to reduce the risks of project failure. Several cost estimation methods are practiced in the software development companies such as Function Point (FP), Use Case Points (UCP), Constructive Cost Model I and II and Story Points (SP). UCP cost estimation method is taken in this research to improve the accuracy of its estimation. UCP estimation depends on the use case diagram of the proposed system. A use case diagram describes the main functional requirements of the proposed system. UCP partially considers non-functional requirements through the technical and environmental factors. There is a lacking in the UCP method to consider the importance of quality attributes in the estimating process. This paper proposes an extended version of the existing UCP method named Quality Extended Use Case Point (QUCP) method in which quality attributes are included to improve the accuracy of cost estimation. A questionnaire is used to validate the proposed QUCP method. It is found after data analysis that seventy five percentages of the participants are agreed that the proposed method will not only help to improve the accuracy of cost estimation but it will also enable a software development company to deliver high-quality products.
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Kalinina, O. N. "Developing the Process of Value Estimation and Modeling the Project Cost." Vestnik of the Plekhanov Russian University of Economics, no. 3 (May 13, 2020): 110–23. http://dx.doi.org/10.21686/2413-2829-2020-3-110-123.
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The article shows results of researching the projects that exist in St. Petersburg. Constructive and cost parameters of power stations, which pass and distribute electric power were found and possibility to estimate the degree of parametric similarity of the project being devised with the analogues project by using the method of cluster analysis by homogeneity principle. It was found out that the procedure of clustering gives an opportunity to get homogeneous in quality samples of needed projects, which in its turn provides a chance to choose the nearest analogue and on its foundation to forecast the cost of the project. On the basis of the method of expert estimations and respective coefficients methodology of modeling the project cost was designed, whose goal is to evaluate the precise cost of the project, which differs from the analogue by parameters. This methodology can allow us with more trustworthiness to identify the future cot of the project and to give possibility to change the cost. The approach put forward by the authors is built on the basis of mathematic model data that speed up the process of selecting analogues and modeling cost of the project.
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Yan, Min Ren. "Risk Assessment and Transaction Cost Incorporated Budgeting for Construction Joint Ventures." Advanced Materials Research 255-260 (May 2011): 3878–82. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.3878.
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Proper budgeting and cost control are essential efforts for a successful project. The task has been conventionally supported by sophisticated project cost estimation from a production perspective. However, from a transaction cost perspective, the alliance risks with inter-organizational partners in joint venture (JV) projects should always be considered for budgeting. Aiming to assess the risks of a JV project, this paper proposes a transaction cost incorporated budgeting model with multi-criteria utility analysis. A case study is presented to demonstrate the applications of the proposed model which would help project managers to identify a proper risk contingency for practical project budgeting in construction JVs. The rationale of estimating the alliance risks with different partners is also enhanced by the proposed model.
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Zakaria, Noor Azura, Amelia Ritahani Ismail, Nadzurah Zainal Abidin, Nur Hidayah Mohd Khalid, and Afrujaan Yakath Ali. "Optimized COCOMO parameters using hybrid particle swarm optimization." International Journal of Advances in Intelligent Informatics 7, no. 2 (April 24, 2021): 177. http://dx.doi.org/10.26555/ijain.v7i2.583.
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Software effort and cost estimation are crucial parts of software project development. It determines the budget, time, and resources needed to develop a software project. The success of a software project development depends mainly on the accuracy of software effort and cost estimation. A poor estimation will impact the result, which worsens the project management. Various software effort estimation model has been introduced to resolve this problem. COnstructive COst MOdel (COCOMO) is a well-established software project estimation model; however, it lacks accuracy in effort and cost estimation, especially for current projects. Inaccuracy and complexity in the estimated effort have made it difficult to efficiently and effectively develop software, affecting the schedule, cost, and uncertain estimation directly. In this paper, Particle Swarm Optimization (PSO) is proposed as a metaheuristics optimization method to hybrid with three traditional state-of-art techniques such as Support Vector Machine (SVM), Linear Regression (LR), and Random Forest (RF) for optimizing the parameters of COCOMO models. The proposed approach is applied to the NASA software project dataset downloaded from the promise repository. Comparing the proposed approach has been made with the three traditional algorithms; however, the obtained results confirm low accuracy before hybrid with PSO. Overall, the results showed that PSOSVM on the NASA software project dataset could improve effort estimation accuracy and outperform other models.
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Salari, Mostafa, Nooshin Yousefi, and Mohhmad Mahdi Asgary. "Cost Performance Estimation in Construction Projects Using Fuzzy Time Series." International Journal of Information Technology Project Management 6, no. 1 (January 2015): 66–75. http://dx.doi.org/10.4018/ijitpm.2015010104.
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In this paper, the authors develop a model to estimate future performance of construction projects. For the purpose of estimation, fuzzy times series models are used as an effective approach in estimation process. Furthermore, linguistic terms are applied to interpret the fuzzy-based results. The proposed model can assists project managers to develop their knowledge concerning the future aspects of project cost performance. It also provides the early warning of weak upcoming performance of project and extends the feasible time for corrective actions. Eventually, a small example has been provided to illustrate how the new model can be implemented in reality.
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Rao, B. Tirimula, Satchidananda Dehuri, and Rajib Mall. "Functional Link Artificial Neural Networks for Software Cost Estimation." International Journal of Applied Evolutionary Computation 3, no. 2 (April 2012): 62–82. http://dx.doi.org/10.4018/jaec.2012040104.
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Software cost estimation is the process of predicting the effort required to develop a software system. Software development projects often overrun their planned effort as defined at preliminary design review. Software cost estimation is important for budgeting, risk analysis, project planning, and software improvement analysis. In this paper, the authors propose a faster functional link artificial neural network (FLANN) based software cost estimation. By means of preprocessing, i.e., optimal reduced datasets (ORD), the authors make the functional link artificial neural network faster. Optimal reduced datasets, which reduce the whole project base into small subsets that consist of only representative projects. The representative projects are given as input to FLANN and tested on eight state-of-the-art polynomial expansions. The proposed methods are validated on five real time datasets. This approach yields accurate results vis-à-vis conventional FLANN, support vector machine regression (SVR), radial basis function (RBF), classification, and regression trees (CART).
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Al-Tabtabai, Hashem, and Alex P. Alex. "Modeling the Cost of Political Risk in International Construction Projects." Project Management Journal 31, no. 3 (September 2000): 4–13. http://dx.doi.org/10.1177/875697280003100302.
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This paper describes a neural network model that can provide assistance in predicting the additional increase in project cost, due to political risk source variables affecting a construction project. The risk factors that affect a construction project are classified as “political source variables” and “project consequence variables.” These source variables are identified and represented in a neural network model. The paper explains how the developed political risk control model can be incorporated directly into a project cost estimation process. The paper concludes with a discussion of the capabilities and limitations of the proposed political risk estimation method, and how it will assist project managers in computing a realistic cost estimate for typical international construction projects under different political conditions.
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Alqahtani, Ayedh, and Andrew Whyte. "Evaluation of non-cost factors affecting the life cycle cost: an exploratory study." Journal of Engineering, Design and Technology 14, no. 4 (October 3, 2016): 818–34. http://dx.doi.org/10.1108/jedt-02-2015-0005.
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Purpose This paper aims to identify the main non-cost factors affecting accurate estimation of life cycle cost (LCC) in building projects. Design/methodology/approach Ten factors affecting LCC in building project cost estimates are identified through literature and interviews. A questionnaire survey is conducted to rank these factors in order of priority and provide the views of cost practitioners about the significance of these factors in the accurate estimation of LCC. The data from 138 construction building projects completed in UK were collected and analysed via multiple regression to discover the relationship between capital and LCCs and between non-cost factors and cost estimation at each stage of the life cycle (capital, operation, maintenance and LCC). Findings The results of analysis of existing LCC data of completing project and survey data from cost professionals are mostly consistent with many literature views and provide a reasonable description of the non-cost factors affecting the accuracy of estimates. Originality/value The value of this study is in the method used, which involves analysis of existing life data and survey data from cost professionals. The results provide a plausible description of the non-cost factors affecting the accuracy of estimates.
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Тихомирова, Ольга, and Olga Tikhomirova. "The complex estimation of project efficiency." Russian Journal of Management 2, no. 4 (September 1, 2014): 188–207. http://dx.doi.org/10.12737/10883.
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The article discusses the methods of estimation of project efficiency at various stages of projects’ life cycle, from feasibility study and cost estimate to estimation of efficiency of final product. The author gives standard algorithms and examples ofcalculations of the main economic indicators of investment efficiency.
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Raza, Syed Hassan, Rameez Khalid, and Muhammad Wasif. "Impact of Critical Risks on the Major Constraints of Small Engineering Projects." April 2021 40, no. 2 (April 1, 2021): 415–25. http://dx.doi.org/10.22581/muet1982.2102.15.
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In this research, generic and specific risks related to the critical activities of the engineering projects are identified. The surveys and interviews from various project managers and engineers of the leading consultant firms conducting engineering projects in Pakistan are the core identifiers. Specific risks associated with the common engineering projects are qualitatively analyzed to prioritize the risks according to their impact on the schedule and cost of the project. Critical activities related to the specific risks are identified. Estimated man-hours without and with quantitative risk assessment have been determined to study the impact of including risk analysis on the schedule and cost of the engineering projects. The relationship between the critical activities and the total man-hours have also been developed to identify the most critical activity or activities influencing the man-hours and ultimately the cost of the project. The likelihood of occurrence of risks are also related to the man-hour completion, to analyze the effect of certainty of the man-hour’s determination, which is an important aspect of estimating schedule, EMV and finally cost. Using the analysis, it has been inferred that the 95% confidence level is not suitable all the time for the estimation of risk impact on the schedule and cost. It increases the man-hour estimation resulting in less competitive proposal to win the project.
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Sayadi, Ahmad Reza, Ali Lashgari, Mohammad Majid Fouladgar, and Miroslaw J. Skibniewski. "ESTIMATING CAPITAL AND OPERATIONAL COSTS OF BACKHOE SHOVELS." Journal of Civil Engineering and Management 18, no. 3 (June 29, 2012): 378–85. http://dx.doi.org/10.3846/13923730.2012.692705.
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Material loading is one of the most critical operations in earthmoving projects. A number of different equipment is available for loading operations. Project managers should consider different technical and economic issues at the feasibility study stage and try to select the optimum type and size of equipment fleet, regarding the production needs and project specifications. The backhoe shovel is very popular for digging, loading and flattening tasks. Adequate cost estimation is one of the most critical tasks in feasibility studies of equipment fleet selection. This paper presents two different cost models for the preliminary and detailed feasibility study stages. These models estimate the capital and operating cost of backhoe shovels using uni-variable exponential regression (UVER) as well as multi-variable linear regression (MVLR), based on principal component analysis. The UVER cost model is suitable for quick cost estimation at the early stages of project evaluation, while the MVLR cost function, which is more detailed, can be useful for the feasibility study stage. Independent variables of MVLR include bucket size, digging depth, dump height, weight and power. Model evaluations show that these functions could be a credible tool for cost estimations in prefeasibility and feasibility studies of mining and construction projects.
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Lakusic, Stjepan. "Use of artificial intelligence for estimating cost of integral bridges." Journal of the Croatian Association of Civil Engineers 73, no. 03 (April 2021): 265–73. http://dx.doi.org/10.14256/jce.2831.2019.
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Estimation of costs is important in every phase of realisation of construction projects. However, the influence of cost estimation is the highest in early phases as it is then that the decision about accepting the job or withdrawing from the project is made. The quantity of data available in initial phases of the project is smaller compared to subsequent phases, which affects accuracy of cost estimation in such early phases. A research making use of artificial intelligence to estimate construction costs of integral road bridges is presented in the paper. The estimation model is prepared by means of neural networks. The best neural network model has proven to be highly accurate in the estimation of costs based on the mean absolute error, which amounts to 13.40 %.
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Winalytra, Inas, Arief Setiawan Budi Nugroho, and Andreas Triwiyono. "Cost Estimation Model for I-Girder Bridge Superstructure Using Multiple Linear Regression and Artificial Neural Network." Applied Mechanics and Materials 881 (May 2018): 142–49. http://dx.doi.org/10.4028/www.scientific.net/amm.881.142.
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One of the owner’s common problem before executing construction projects is the complexity in estimating the project cost in an early stage. Inaccurate cost estimation will force the owner to make further arrangement to the project budget. This study aims to develop an initial cost estimation model for superstructures of Precast I-girder Bridge. Cost estimation model was developed based on thirteen data of detail engineering design of I-girder bridge in Daerah Istimewa Yogyakarta (DIY). Factors influencing the cost of the superstructures of the I-girder bridge were identified. Bridge span and width, the size of the sidewalk, and railing’s type are considered as variables affecting the cost of superstructures. These variables are then arranged into two different analysis Multiple Linear Regression (MLR) analysis and Artificial Neural Network (ANN), in order to obtain the best estimation model. The results of the analysis showed that bridge span and width were the significant factors influencing cost. The correlation value of bridge span is 89.0%, bridge width is 74.2%, the size of the sidewalk is 66.1%, and railing’s type is 46.1% as identified factors that affect the cost of the superstructure. A comparative model of two approaches shows that the ANN has better accuracy than that of MLR, although the difference was not significant.
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Briciu, C. V., I. Filip, and I. I. Indries. "Methods for cost estimation in software project management." IOP Conference Series: Materials Science and Engineering 106 (February 1, 2016): 012008. http://dx.doi.org/10.1088/1757-899x/106/1/012008.
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Jaafari, A. "Probabilistic unit cost estimation for project configuration optimization." International Journal of Project Management 6, no. 4 (November 1988): 226–34. http://dx.doi.org/10.1016/0263-7863(88)90007-5.
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Odendaal, M. M., F. NS Vermaak, and E. Du Toit. "Cost estimation and management over the life cycle of metallurgical research projects." Southern African Business Review 19 (February 26, 2019): 137–56. http://dx.doi.org/10.25159/1998-8125/5811.
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This study investigates whether all expected costs over the life cycle of metallurgical research projects are included in initial, normal and final cost estimates, and whether these costs are managed throughout a project’s life cycle since there is not enough emphasis on the accurate estimation of costs and their management over the life cycle of metallurgical research projects. The study also determines during which phase of the life cycle of metallurgical research projects’ costs are normally determined, during which phase most of the costs are incurred, and during which phase costs are managed. Project life cycles, techniques of cost estimation and cost management are examined. 10A survey was used to gather information by means of face-to-face and telephonic interviews, as well as an electronic questionnaire. The total population of entities in South Africa that conduct metallurgical research projects is small, numbering only 12 in all. The ten entities that conducted the largest metallurgical research projects in terms of average size were selected for this study. 11The conclusion drawn from the survey was that all costs over the life cycle of metallurgical research projects are not taken into account in the initial cost estimate of a project. Costs are mainly managed during the growth phase of a project and not during the introduction phase, when 80% of the costs are normally committed. The implication of this is that cost estimates for metallurgical research projects may not be accurate and costs are not necessarily managed properly throughout the life cycle of such projects. This may lead to cost overruns of project budgets, project budgets being depleted before the delivery stage and research sections running at a loss.
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Hatamleh, Muhammad T., Mohammed Hiyassat, Ghaleb Jalil Sweis, and Rateb Jalil Sweis. "Factors affecting the accuracy of cost estimate: case of Jordan." Engineering, Construction and Architectural Management 25, no. 1 (February 19, 2018): 113–31. http://dx.doi.org/10.1108/ecam-10-2016-0232.
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Purpose Cost estimating process is an important element within the project life cycle. Comprehensive information, expanded knowledge, considerable expertise, and continuous improvement are needed to obtain accurate cost estimation. The purpose of this paper is to identify the critical factors that affect accuracy of cost estimation and evaluate the degree to which these factors are important from contractors’ and consultants’ viewpoints. Design/methodology/approach Qualitative and quantitative research approaches were adopted in collecting and analyzing the data, and testing the hypotheses. Based on the literature review, a questionnaire was prepared and then was modified according to the results of face-to-face open-ended interviews conducted with 11 project managers. The final version of the questionnaire was distributed to a random sample of 265 respondents. For analyzing the collected data Kendall’s and Mann-Whitney tests were conducted. Findings The analysis revealed that there is a strong agreement between contractors and consultants in the ranking of the factors related to consultant, contractor, design parameters, and information. A slightly weak agreement between contractors and consultants was noted regarding the factors related to market conditions (external factors) and factors related to project characteristics. Furthermore, the results show that the top ten factors affecting the accuracy of cost estimate are clear and detail drawings and specification, pricing experience of construction projects, perception of estimation importance, equipment (cost/availability/performance), project complexity, clear scope definition, accuracy and reliability of cost information, site constraints (access, storage, services), material availability, financial capabilities of the client, and availability of database of bids on similar project (historical data). Originality/value Offers an original view of the concept of accuracy of cost estimates as it relates to the efficiency of the project relying on both literature review and empirical evidence.
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Rudíus, Michael. "Cost estimation and cost risk analysis in early design stages of naval projects." Ciencia y tecnología de buques 8, no. 17 (July 8, 2015): 31. http://dx.doi.org/10.25043/19098642.119.
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During the design and engineering process for naval projects a comprehensive cost management is required. The overall aim is to ensure the financial feasibility throughout the project with a low financial risk. Cost management combines acquisition costs and in-service costs in order to obtain life cycle costs. In this context methods for cost estimation have to be adapted to the phase of the planning process of a naval vessel. This paper introduces challenges and solutions for the cost estimation in general and describes MTGÅLs cost estimation approach for conceptual and preliminary designs. Additionally the utilization of cost risk analyses in order to ensure a reliable budget planning and the identification of cost risk drivers will be described.
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Haider, Usman, Usama Khan, Asif Nazir, and Muhammad Humayon. "Cost Comparison of a Building Project by Manual and BIM." Civil Engineering Journal 6, no. 1 (January 1, 2020): 34–49. http://dx.doi.org/10.28991/cej-2020-03091451.
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This article focuses on comparison between Manual/Traditional and Building Information Modelling (BIM) software based approaches for cost comparison. Centre line method for manual and Revit software for BIM based approaches are used in this research. The principal objectives of this research are to calculate quantities by Manual Centre line method, then to make the cost estimation fast, accurate, efficient, and errorless by using BIM software Rivet, and finally the comparison study of manual based and BIM / Software based estimation. For manual approach, quantities are calculated by multiplying the measurements of length, breadth, and height. Then to get the total quantities the deduction is subtracted from the quantities and final BOQ is prepared for which estimated cost of building is calculated. For BIM based approach, 3D model from 2D floor plan of building is prepared in Revit software, then to estimate the cost of building structure elements, sheets of quantities are generated in the schedule option of the view tab in the software. The Percentage difference between Manual and BIM / Revit Software estimation in brick work, RCC Slab, Plaster Work, PCC for Flooring, Floor Tile Work, Skirting, Paint Work, False Ceiling, Doors and Aluminum Work is 4.57, 2.61, 7.58, 3.27, 1.87, 6.73, 8.03, 1.87, and 0.00% respectively. The total cost difference between manual and BIM based estimation approach comes out to be 4.8%. It is thus concluded that the BIM-assisted estimates have better performance over traditional/manual estimating methods.
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Yang, Hai. "Research on Improved Staged Software Cost Estimation Method Based on COCOMO Model." Advanced Materials Research 989-994 (July 2014): 1501–4. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1501.
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The accuracy of software cost estimation is essential for software development management. By introducing and analyzing the estimation methods of software cost systematically, the paper discussed the necessary of considering the software maintenance stage and estimating the software cost by separating the procedure of software development into several small stages. Then a staged software cost estimation method based on COCOMO model was proposed. The use of the new software cost estimation method proposed by this paper not only contributes to the cost control of software project, but also effectively avoids the bias problem due to using by single cost estimation method so that the accuracy of cost estimation could be improved.
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Salari, Mostafa, Hassan Ali Aria, and Mohammad Mahdi Asgari Dehabadi. "A new model for estimation of project total cost in construction projects." International Journal of Information and Decision Sciences 9, no. 2 (2017): 128. http://dx.doi.org/10.1504/ijids.2017.084886.
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Dehabadi, Mohammad Mahdi Asgari, Hassan Ali Aria, and Mostafa Salari. "A new model for estimation of project total cost in construction projects." International Journal of Information and Decision Sciences 9, no. 2 (2017): 128. http://dx.doi.org/10.1504/ijids.2017.10005874.
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Ji, Sae-Hyun, Joseph Ahn, Hyun-Soo Lee, and Kyeongjin Han. "Cost Estimation Model Using Modified Parameters for Construction Projects." Advances in Civil Engineering 2019 (July 30, 2019): 1–10. http://dx.doi.org/10.1155/2019/8290935.
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Construction projects require huge amounts of capital and have many risk factors due to the unique industry characteristics. For a project to be successful, accurate cost estimation during the design phase is very important. Thus, this research aims to develop a cost estimation model where a modification method integrates influential factors with significant parameters. This study identified a modified parameter-making process, which integrates many influential factors into a small number of significant parameters. The proposed model estimates the cost using quantity-based modified parameters multiplied by their price. A case study was conducted with 24-residence building project, and the estimation accuracy of the suggested method and a CBR model were compared. The proposed model achieved higher overall cost-estimation accuracy and stability. A large number of influence factors can be modified as simple representatives and overcome the limitations of a conventional cost estimation model. The paper originality relates to providing a modified parameter-making process to enhance reliability of a cost estimation. In addition, the suggested cost model can actively respond to the iterative requirements of recalculation of the cost.
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Hirijanto, I. Wayan Mundra, and Addy Utomo. "Cost prediction model based on system dynamics in water resource project." MATEC Web of Conferences 258 (2019): 02027. http://dx.doi.org/10.1051/matecconf/201925802027.
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Project’s cost is one of important components in project achievement. Because of the uniqueness of construction projects, cost estimation always differs from project to project. The rate of cost components always change over time make difficult to forecast the cost for the upcoming project. The cost component consists of many influencing variables where there is interrelationship each other affecting to the total project cost. This paper objective is to develop a cost prediction model to assist the project planners in cost estimation for future projects. System dynamic is one of the appropriate methods to analyse system behaviour with interrelationship referring to the historic data, so it is able to predict the future project. Developing the model, primary and secondary data are collected from previous studies, interview with the government planner and survey in Malang Regency. The model simulation is Brick work unit with its components. Data from last thirteen years are used to verify and validate the developed model by causal loop diagram as a basic method in system dynamic. The finding showed that the model is closed to real condition through the validation mechanism. The developed system is useful in decision making of budget planning based on work quantity.
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Ahiaga-Dagbui, Dominic D., and Simon D. Smith. "Rethinking construction cost overruns: cognition, learning and estimation." Journal of Financial Management of Property and Construction 19, no. 1 (April 7, 2014): 38–54. http://dx.doi.org/10.1108/jfmpc-06-2013-0027.
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Purpose – Drawing on mainstream arguments in the literature, the paper presents a coherent and holistic view on the causes of cost overruns, and the dynamics between cognitive dispositions, learning and estimation. A cost prediction model has also been developed using data mining for estimating final cost of projects. The paper aims to discuss these issues. Design/methodology/approach – A mixed-method approach was adopted: a qualitative exploration of the causes of cost overrun followed by an empirical development of a final cost model using artificial neural networks. Findings – A conceptual model to distinguish between the often conflated causes of underestimation and cost overruns on large publicly funded projects. The empirical model developed in this paper achieved an average absolute percentage error of 3.67 percent with 87 percent of the model predictions within a range of ±5 percent of the actual final cost. Practical implications – The model developed can be converted to a desktop package for quick cost predictions and the generation of various alternative solutions for a construction project in a sort of what-if analysis for the purposes of comparison. The use of the model could also greatly reduce the time and resources spent on estimation. Originality/value – A thorough discussion on the dynamics between cognitive dispositions, learning and cost estimation has been presented. It also presents a conceptual model for understanding two often conflated issues of cost overrun and under-estimation.
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Silas, Faki Agebee, Musa Yusuf, and Anah Hassan Bijik. "Hybridization of Class Responsibility Collaborators Model (HCRCM) with Function Point to enhance Project Estimation Cost in Agile Software Development." Circulation in Computer Science 2, no. 6 (July 20, 2017): 20–24. http://dx.doi.org/10.22632/ccs-2017-252-32.
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Estimating software cost in an agile system in terms of effort is very challenging. This is because the traditional models of software cost estimation do not completely fit in the agile development process. This paper presents a methodology to enhance the cost of project estimation in agile development. The hybridization adopts Class Responsibility Collaborators models with function point thereby boosting the agile software development estimation process. The study found out that adopting the Hybridized Class Responsibility Collaborator with function point has great improvement on cost estimation in agile software development.
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Khatimi, Husnul, Muhammad Reza Fardian, and Yuslena Sari. "EFFECTIVENESS OF APPLYING BIM BASED COST ESTIMATION IN DEVELOPMENT OF THE SYAMSUDIN NOOR AIRPORT PROJECT BANJARMASIN." astonjadro 10, no. 1 (March 24, 2021): 109. http://dx.doi.org/10.32832/astonjadro.v10i1.4200.
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<p>Development of The Syamsudin Noor Airport Project in Banjarmasin is one of the largest projects in Banjarmasin, South Kalimantan. This project applied BIM-based cost estimation on a steel roof structure. However, the cost estimation for this steel roof structure is applied conventionally. The BIM-based cost estimation could have been applied in collaborating a building information becomes unity in one model. This research will raise the issue of applying BIM-based cost estimation at The Syamsudin Noor Airport Project to find out the effectiveness calculation of cost estimation conventionally and BIM-based cost estimation. The report result by 3D modeling of Tekla is quantity take-offs using as a data for processing the cost analysis conventionally. Whereas the 3D model made by Tekla will be exported to Revit through the interoperability of IFC or application of extention of Tekla warehouse that is “Export to Revit Geometry” for the processing the BIM-based cost estimation analysis. The unit price for the cost calculation is acquired by list price (AHSP or subcontractor value). The result of these both cost calculation, there are large enough difference in cost of these both calculations. Difference of conventional calculations and BIM-based cost estimation using Revit worth Rp 3,690,741,474 - Rp 5,047,206,780 with a percentage of 14% - 20%. Cause of these large enough differences in cost due to the model exported is only 90% succeeded. It happened due to difference thing in the mapping of object profile and difference in shape BREP geometry conditions.</p>
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Arif Ghuzdewan, Toriq, and Brigitta Petra Kartika Narindri. "Project Cost Estimation Based on Standard Price of Goods and Services (SHBJ)." MATEC Web of Conferences 159 (2018): 01012. http://dx.doi.org/10.1051/matecconf/201815901012.
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Cost estimation is determining the probable cost of a project, which is critical in project development. For government projects in Indonesian construction industry, it is common to use a standard price of goods and services (called SHBJ) publised by the government as a cost reference. This research examines whether the SHBJ provide sufficient and reliable data. A case study from a contractor’s bidding document (RAB) and the SHBJ from the city of Yogyakarta are employed. The result shows that cost estimation based on the SHBJ and the contractor’s RAB gives 12% difference. Using SHBJ data from years 2010 – 2016 a regression equation is obtained as Yi = 62,156 X + 2,003,962 which can be used to estimate project cost in the future. This research also suggests some improvments for the SHBJ.
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Meharie, Meseret Getnet, Zachary C. Abiero Gariy, Raphael Ngumbau Ndisya Mutuku, and Wubshet Jekale Mengesha. "An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects." Advances in Civil Engineering 2019 (June 25, 2019): 1–14. http://dx.doi.org/10.1155/2019/4092549.
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Accurate cost estimates are vital to the effective realisation of construction projects. Extended knowledge, wide-ranging information, substantial expertise, and continuous improvement are required to attain accurate cost estimation. Cost estimation at the preliminary phase of the project is always a challenge as only limited information is available. Hence, rational selection of input variables for preliminary cost estimation could be imperative. A systematic input variable selection approach for preliminary estimating using an integrated methodology of factor analysis and fuzzy AHP is presented in this paper. First, the factor analysis is used to classify and reduce the input variables and their variable coefficients are determined. Second, fuzzy AHP based on the geometric mean method is employed to determine the weights of input variables in a fuzzy environment where the subjectivity and vagueness are handled with natural language expressions parameterized by triangular fuzzy numbers. Then, the input variables are suggested to be selected starting with those having high coefficient and high importance weight. A set of three variables, one from each group, can be added to the estimating model at a time so that the problem of collinearity can vanish and good accuracy of the estimate can be ensured. The proposed approach enables cost estimators to better understand the complete input variable selection process at the early stage of project development and provide a more accurate, rational, and systematic decision support tool.
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Park, Young-Jun, and Chang-Yong Yi. "Resource-Based Quality Performance Estimation Method for Construction Operations." Applied Sciences 11, no. 9 (April 30, 2021): 4122. http://dx.doi.org/10.3390/app11094122.
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Construction quality is one of the primary management objectives relating to duration and cost for construction projects. Project managers struggle with minimizing duration and cost while maximizing quality for construction projects. In construction projects, duration and cost have management priorities. On the other hand, quality is considered a matter of achievement only when it reaches a certain level. Although the importance of quality control in construction management has been constantly discussed, it has still been sacrificed under the goal of shortening construction duration and reducing costs. This study presents a method for estimating the quantitative quality performance of construction operations in which the level of detail is breaking into the work task level for intuitive quality performance evaluation. For this purpose, quality weights of resources that have a proportional quality importance weight and quality performance indexes of resources are utilized for estimating the quantitative quality performance of construction operations. Quality performance estimation and the resource allocation optimization system is presented and validated using a construction simulation model.
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Jiang, Qinghua. "Estimation of construction project building cost by back-propagation neural network." Journal of Engineering, Design and Technology 18, no. 3 (November 16, 2019): 601–9. http://dx.doi.org/10.1108/jedt-08-2019-0195.
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Purpose Building cost is an important part of construction projects, and its correct estimation has important guiding significance for the follow-up decision-making of construction units. Design/methodology/approach This study focused on the application of back-propagation (BP) neural network in the estimation of building cost. First, the influencing factors of building cost were analyzed. Six factors were selected as input of the estimation model. Then, a BP neural network estimation model was established and trained by ten samples. Findings According to the experimental results, it was found that the estimation model converged at about 85 times; compared with radial basis function (RBF), the estimation accuracy of the model was higher, and the average error was 5.54 per cent, showing a good reliability in cost estimation. Originality/value The results of this study provide a reliable basis for investment decision-making in the construction industry and also contribute to the further application of BP neural network in cost estimation.