Relevant bibliographies by topics / Data-driven maintenance

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data-driven maintenance'

Author: Grafiati
Published: 25 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Data-driven maintenance"

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Rios, Pablo. "Data-Driven Maintenance." Manufacturing Management 2023, no. 1-2 (2023): 32–33. http://dx.doi.org/10.12968/s2514-9768(23)90381-9.

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Afful-Dadzie, Anthony, and Theodore T. Allen. "Data-Driven Cyber-Vulnerability Maintenance Policies." Journal of Quality Technology 46, no. 3 (2014): 234–50. http://dx.doi.org/10.1080/00224065.2014.11917967.

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Ostrowski, João, and József Menyhárt. "Enhancing maintenance with a data-driven approach." International Review of Applied Sciences and Engineering 10, no. 2 (2019): 135–40. http://dx.doi.org/10.1556/1848.2019.0016.

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Constant stream of data has been generated and stored as more devices are being connected to the internet and supported with cloud technologies. The price drop of such applications along with industry 4.0 trending, triggered an explosive growth and demand for many IT modern solutions. From an industrial point of view, sensorization practices are spreading through factories and warehouses where software is constantly adapting to provide actionable insights in a data-driven configuration. The fourth industrial revolution is empowering the manufacturers with solutions for cost reduction, which tr
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Ma, Zhiliang, Yuan Ren, Xinglei Xiang, and Ziga Turk. "Data-driven decision-making for equipment maintenance." Automation in Construction 112 (April 2020): 103103. http://dx.doi.org/10.1016/j.autcon.2020.103103.

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Wadzuk, Bridget, Bridget Gile, Virginia Smith, Ali Ebrahimian, Micah Strauss, and Robert Traver. "Moving Toward Dynamic and Data-Driven GSI Maintenance." Journal of Sustainable Water in the Built Environment 7, no. 4 (2021): 02521003. http://dx.doi.org/10.1061/jswbay.0000958.

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Lopes Gerum, Pedro Cesar, Ayca Altay, and Melike Baykal-Gürsoy. "Data-driven predictive maintenance scheduling policies for railways." Transportation Research Part C: Emerging Technologies 107 (October 2019): 137–54. http://dx.doi.org/10.1016/j.trc.2019.07.020.

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Coronel, Eduardo, Benjamín Barán, and Pedro Gardel. "A Survey on Data Mining for Data-Driven Industrial Assets Maintenance." Technologies 13, no. 2 (2025): 67. https://doi.org/10.3390/technologies13020067.

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This survey presents a comprehensive review of data-driven approaches for industrial asset maintenance, emphasizing the use of data mining and machine learning techniques, including deep learning, for condition-based and predictive maintenance. It examines 534 references from 1995 to 2023, along with three additional articles from 2024 on natural language processing and large language models in industrial maintenance. The study categorizes two main techniques, four specialized approaches, and 27 methodologies, resulting in over 100 variations of algorithms tailored to specific maintenance need
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Wolfartsberger, Josef, Jan Zenisek, and Norbert Wild. "Data-Driven Maintenance: Combining Predictive Maintenance and Mixed Reality-supported Remote Assistance." Procedia Manufacturing 45 (2020): 307–12. http://dx.doi.org/10.1016/j.promfg.2020.04.022.

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Devarajan, Vinodkumar. "Advancing Data Center Reliability Through AI-Driven Predictive Maintenance." European Journal of Computer Science and Information Technology 13, no. 14 (2025): 102–14. https://doi.org/10.37745/ejcsit.2013/vol13n14102114.

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The evolution of data center maintenance has undergone a transformative shift from traditional reactive and scheduled maintenance to AI-driven predictive maintenance strategies. The integration of artificial intelligence and machine learning technologies enables precise failure prediction, optimizes resource allocation, and enhances operational reliability. Advanced sensor networks and sophisticated analytics pipelines process vast amounts of operational data, while machine learning models, including neural networks, support vector machines, and decision trees, provide accurate predictions of
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Chen, Chuang, Cunsong Wang, Ningyun Lu, Bin Jiang, and Yin Xing. "A data-driven predictive maintenance strategy based on accurate failure prognostics." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 2 (2021): 387–94. http://dx.doi.org/10.17531/ein.2021.2.19.

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Maintenance is fundamental to ensure the safety, reliability and availability of engineering systems, and predictive maintenance is the leading one in maintenance technology. This paper aims to develop a novel data-driven predictive maintenance strategy that can make appropriate maintenance decisions for repairable complex engineering systems. The proposed strategy includes degradation feature selection and degradation prognostic modeling modules to achieve accurate failure prognostics. For maintenance decision-making, the perfect time for taking maintenance activities is determined by evaluat
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Dissertations / Theses on the topic "Data-driven maintenance"

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Sedghi, Mahdieh. "Data-driven predictive maintenance planning and scheduling." Licentiate thesis, Luleå tekniska universitet, Industriell Ekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80828.

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The railway track network is one of the major modes of transportation and among a country’s most valuable infrastructure assets. Maintenance and renewal of railway infrastructure have a vital role in safety performance, the quality of the ride, train punctuality, and the life cycle cost of assets. Due to the large proportion of maintenance costs, increasing the efficiency of maintenance through optimised planning can result in high amounts of cost-saving. Moreover, from a safety perspective, late maintenance intervention can result in defective track and rollingstock components, which in sever
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Decker, de Sousa Leticia <1981&gt. "Data-stream driven Fuzzy-granular approaches for system maintenance." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amsdottorato.unibo.it/10273/1/phdThesis_LeticiaDecker.pdf.

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Intelligent systems are currently inherent to the society, supporting a synergistic human-machine collaboration. Beyond economical and climate factors, energy consumption is strongly affected by the performance of computing systems. The quality of software functioning may invalidate any improvement attempt. In addition, data-driven machine learning algorithms are the basis for human-centered applications, being their interpretability one of the most important features of computational systems. Software maintenance is a critical discipline to support automatic and life-long system operation. As
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Roychowdhury, Sayak. "Data-Driven Policies for Manufacturing Systems and Cyber Vulnerability Maintenance." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1493905616531091.

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ANTOMARIONI, SARA. "Data-driven approaches to maintenance policy definition: general framework and applications." Doctoral thesis, Università Politecnica delle Marche, 2021. http://hdl.handle.net/11566/289660.

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La competitività dell’attuale scenario industriale richiede elevati livelli di affidabilità di processo, in particolare per impianti complessi: infatti un elevato numero di componenti li rende potenzialmente più soggetti a guasti. In questo contesto, la tesi mira a proporre un framework generale per supportare il processo di gestione della manutenzione. Si presentano quattro applicazioni basate sul caso di studio di una raffineria di petrolio. Nella prima applicazione, si adotta il framework per derivare le regole di associazione tra i guasti dei componenti dopo un arresto dell'impianto di raf
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Okwori, Emmanuel. "Data-driven approaches for proactive maintenance planning of sewer blockage management." Licentiate thesis, Luleå tekniska universitet, Arkitektur och vatten, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-83891.

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Blockages have been reported to account for a significant proportion of reported failures in sewer networks. The malfunctioning of the sewer network from blockages and the subsequent disruption to other public services and flooding may constitute a risk to the environment and human health. Due to the complex nature of underground sewer networks, a reactive approach to blockage maintenance is typically employed. However, although proactive maintenance strategies have been developed, both approaches could be expensive and highlight the need to address the problem with analytics-based methods. Al
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Jiang, Tianyu. "Data-Driven Cyber Vulnerability Maintenance of Network Vulnerabilities with Markov Decision Processes." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1494203777781845.

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Larsson, Olsson Christoffer, and Erik Svensson. "Early Warning Leakage Detection for Pneumatic Systems on Heavy Duty Vehicles : Evaluating Data Driven and Model Driven Approach." Thesis, KTH, Mekatronik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261207.

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Modern Heavy Duty Vehicles consist of a multitude of components and operate in various conditions. As there is value in goods transported, there is an incentive to avoid unplanned breakdowns. For this, condition based maintenance can be applied.\newline This thesis presents a study comparing the applicability of the data-driven Consensus SelfOrganizing Models (COSMO) method and the model-driven patent series introduced by Fogelstrom, applied on the air processing system for leakage detection on Scania Heavy Duty Vehicles. The comparison of the two methods is done using the Area Under Curve val
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Dinmohammadi, Fateme. "Data-driven risk-based modelling approaches to maintenance optimisation of railway transport assets." Thesis, Glasgow Caledonian University, 2018. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.743925.

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CIPOLLINI, FRANCESCA. "Data-Driven and Hybrid Methods for Naval Applications." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/989847.

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The goal of this PhD thesis is to study, design and develop data analysis methods for naval applications. Data analysis is improving our ways to understand complex phenomena by profitably taking advantage of the information laying behind a collection of data. In fact, by adopting algorithms coming from the world of statistics and machine learning it is possible to extract valuable information, without requiring specific domain knowledge of the system generating the data. The application of such methods to marine contexts opens new research scenarios, since typical naval problems can now be sol
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Lundgren, Andreas. "Data-Driven Engine Fault Classification and Severity Estimation Using Residuals and Data." Thesis, Linköpings universitet, Fordonssystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165736.

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Recent technological advances in the automotive industry have made vehicularsystems increasingly complex in terms of both hardware and software. As thecomplexity of the systems increase, so does the complexity of efficient monitoringof these system. With increasing computational power the field of diagnosticsis becoming evermore focused on software solutions for detecting and classifyinganomalies in the supervised systems. Model-based methods utilize knowledgeabout the physical system to device nominal models of the system to detect deviations,while data-driven methods uses historical data to
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Books on the topic "Data-driven maintenance"

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Gama, Joao, Sepideh Pashami, Albert Bifet, et al., eds. IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66770-2.

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Howell, Marvin T., and Fadi Alshakhshir. Data Driven Energy Centered Maintenance. River Publishers, 2021.

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Howell, Marvin T., and Fadi Alshakhshir. Data Driven Energy Centered Maintenance. River Publishers, 2021.

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Howell, Marvin T., and Fadi S. Alshakhshir. Data Driven Energy Centered Maintenance. Taylor & Francis Group, 2021.

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Data Driven Energy Centered Maintenance. River Publishers, 2021.

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Kiritsis, Dimitris, Melinda Hodkiewicz, Oscar Lazaro, Jay Lee, and Jun Ni, eds. Data-Driven Cognitive Manufacturing - Applications in Predictive Maintenance and Zero Defect Manufacturing. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88966-583-9.

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IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning: Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-Located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers. Springer International Publishing AG, 2021.

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Book chapters on the topic "Data-driven maintenance"

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Liu, Min, Ling Li, and Feng Yan. "Data-Driven Fault Diagnosis Methods." In Intelligent Predictive Maintenance. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2677-6_7.

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Alshakhshir, Fadi, and Marvin T. Howell. "Different Maintenance Types and the Need for Energy Centered Maintenance." In Data Driven Energy Centered Maintenance. River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-2.

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Alshakhshir, Fadi, and Marvin T. Howell. "ECM Process – Data Collection." In Data Driven Energy Centered Maintenance. River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-5.

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Liu, Min, Ling Li, and Feng Yan. "Data-Driven Fault Prediction Model and Methods." In Intelligent Predictive Maintenance. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2677-6_8.

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Alshakhshir, Fadi, and Marvin T. Howell. "Energy Centered Maintenance in Data Centers." In Data Driven Energy Centered Maintenance. River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-11.

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Alshakhshir, Fadi, and Marvin T. Howell. "ECM Process — Measuring Equipment Current Performance." In Data Driven Energy Centered Maintenance. River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-7.

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Alshakhshir, Fadi, and Marvin T. Howell. "Conclusion." In Data Driven Energy Centered Maintenance. River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-16.

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Alshakhshir, Fadi, and Marvin T. Howell. "Energy Centered Maintenance to avoid Low Delta T Syndrome in Chilled Water Systems." In Data Driven Energy Centered Maintenance. River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-10.

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Alshakhshir, Fadi, and Marvin T. Howell. "ECM Process — Updating Preventative Maintenance Plans." In Data Driven Energy Centered Maintenance. River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-9.

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Alshakhshir, Fadi, and Marvin T. Howell. "Building Energy Centered Behavior Leading to an Energy Centered Culture." In Data Driven Energy Centered Maintenance. River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-14.

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Conference papers on the topic "Data-driven maintenance"

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K, Anand, Ananthajothi K, Vinodkumar S, and N. Duraimurugan. "Machinelearnpro: Redefining Predictive Maintenance Through Data-Driven Approaches." In 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG). IEEE, 2024. https://doi.org/10.1109/icstsdg61998.2024.11026495.

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Moreira, Margarida, Eliseu Pereira, and Gil Gonçalves. "Data-Driven Predictive Maintenance for Component Life-Cycle Extension." In 21st International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0013014200003822.

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Li, Fengsheng, Yuwei Shang, Jinli Wang, Limei Zhou, Shuaitao Bai, and Wenke Shen. "Data-driven Condition-based Maintenance Schedules of Active Distribution Networks." In 2024 The 9th International Conference on Power and Renewable Energy (ICPRE). IEEE, 2024. https://doi.org/10.1109/icpre62586.2024.10768352.

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Nusser, Jeffrey K., Darryl J. Stimson, Eric Herzberg, and Charles A. Babish. "Data-Driven Corrosion Prevention and Control Decisions for the USAF." In SSPC 2017 Greencoat. SSPC, 2017. https://doi.org/10.5006/s2017-00041.

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Abstract Corrosion significantly impacts1 safety, availability and sustainment costs of U.S. Air Force (AF) systems and equipment. System downtime due to corrosion maintenance decreases the availability of systems to perform their National defense mission and drives the need for more aircraft and associated logistics tail. In addition, the AF spends about $5.5 Billion per year, about 21 percent of the annual AF maintenance budget, on corrosion maintenance. This cost exceeds the annual Pentagon budget for the campaign against the Islamic State.2 Because of these significant impacts, AF leaders
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Marangis, Demetris, Andreas Livera, George Makrides, and George E. Georgiou. "Data-driven Predictive Maintenance Alerting Routine for utility-scale Photovoltaic Systems." In 2024 3rd International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED). IEEE, 2024. https://doi.org/10.1109/synergymed62435.2024.10799487.

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Doorsamy, Wesley, and Pitshou Bokoro. "An Investigation into Unsupervised Anomaly Detection for Data-Driven Predictive Maintenance." In 2024 IEEE 12th International Conference on Intelligent Systems (IS). IEEE, 2024. http://dx.doi.org/10.1109/is61756.2024.10705190.

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Han, Ruoran, Xiaobing Ma, Heping Jia, and Li Yang. "Condition-Based Maintenance Scheduling Integrating Multi-Level Defect Information under Multivariate Environmental Affection." In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, 2024. http://dx.doi.org/10.1109/docs63458.2024.10704281.

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Muneeshwari, P., R. Suguna, G. Mary Valantina, M. Sasikala, and D. Lakshmi. "IoT-Driven Predictive Maintenance in Industrial Settings through a Data Analytics Lens." In 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT). IEEE, 2024. http://dx.doi.org/10.1109/tqcebt59414.2024.10545167.

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Lee, Hyeyoung, Sangkyun Lee, and Sungjoon Choi. "Perturbation-driven data augmentation for time series anomaly detection improvement in predictive maintenance." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825569.

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Mirkar, Sulalah, Samahit Juvekar, Arya Popat, and Heet Jain. "Smart Solar Panel Maintenance: A Data-Driven Approach Using IoT and Machine Learning." In 2025 International Conference on Sustainable Energy Technologies and Computational Intelligence (SETCOM). IEEE, 2025. https://doi.org/10.1109/setcom64758.2025.10932396.

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Reports on the topic "Data-driven maintenance"

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Stefanowicz, Brian, Burkhard Wandelt, Jonas Berge, et al. Smart maintenance architecture – data-driven approach to smarter maintenance. BioPhorum, 2023. http://dx.doi.org/10.46220/2023it001.

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Light, Ethan, Shang Sai, Yanfeng Ouyang, Will O’Brien, Jesus Osorio, and Yuhui Zhai. Investigating Statewide Transit Maintenance Needs in Illinois. Illinois Center for Transportation, 2023. http://dx.doi.org/10.36501/0197-9191/23-028.

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This study’s researchers investigated transit vehicle maintenance processes, maintenance needs, and potential opportunities associated with building additional bus maintenance facilities (e.g., regional maintenance centers) in Illinois. They collected information via three main tasks. First, they conducted a literature review to document practices on preventive and corrective transit vehicle maintenance processes and explored similar or comparable projects among peer states and regions. Second, they conducted a series of interviews with Illinois local transit agencies, nonprofit organizations,
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Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance hu
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Verma, Mithlesh. Indian Municipal Finance 2023. Indian Institute for Human Settlements, 2023. http://dx.doi.org/10.24943/imf11.2023.

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This study examines decadal time-series data from 33 Urban Local Bodies (ULBs) of varying sizes across India. The analysis reveals persistent challenges, including high dependence on Inter-Governmental Transfers (IGTs), fluctuations in tax revenue, and limited spending on Operations and Maintenance (O&amp;M). Capital expenditures are primarily driven by program-based IGTs, prompting a reevaluation of strategies to enhance own revenue mobilization. The study emphasizes the link between revenue efforts and O&amp;M, advocating for improved current spending to sustain existing infrastructure and s
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Mahlberg, Justin, Yaguang Zhang, Sneha Jha, et al. Development of an Intelligent Snowplow Truck that Integrates Telematics Technology, Roadway Sensors, and Connected Vehicle. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317355.

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The Indiana Department of Transportation (INDOT) manages and maintains over 28,000 miles of roadways. Maintenance of the roadways includes pavement repair in the summer as well as snow removal and de-icing in the winter. The prioritization of assets during winter storm events is crucial and impacts travel and safety. The objective of this project was to identify and develop tools INDOT could provide its operators to effectively perform winter operation de-icing activities. This project examined application methods and data to provide analytics and make data-driven decisions for state-wide depl
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Kim, Changmo, Ghazan Khan, Brent Nguyen, and Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, 2020. http://dx.doi.org/10.31979/mti.2020.1806.

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The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&amp;R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting
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Randrup, Thomas B., Agnes Pierre, Niel Sang, and Kjell Nilson. Equity in Green Space Planning and Management : synthesis study on data availability for the development of a socio-ecological index. SLU Movium Think Tank, 2025. https://doi.org/10.54612/a.7h5gdnod5n.

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As cities densify to meet environmental and economic goals, the equitable distribution of urban green spaces (UGS) becomes critical for fostering community well-being, promoting environmental justice, and enhancing climate resilience. This report presents a synthesis study conducted by the Swedish University of Agricultural Sciences (SLU) in collaboration with Nilsson Landscape, aimed at understanding the relationship between socio-economy and accessibility to UGS, to assess and enhance green equity in urban environments. The research focuses on Malmö specifically, and have involved Region Skå
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Seale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41282.

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Prognostics and health management (PHM) frameworks are widely used in engineered systems, such as manufacturing equipment, aircraft, and vehicles, to improve reliability, maintainability, and safety. Prognostic information for impending failures and remaining useful life is essential to inform decision-making by enabling cost versus risk estimates of maintenance actions. These estimates are generally provided by physics-based or data-driven models developed on historical information. Although current models provide some predictive capabilities, the ability to represent individualized dynamic f
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Bao, Jieyi, Xiaoqiang Hu, Cheng Peng, et al. Advancing INDOT’s Friction Test Program for Seamless Coverage of System: Pavement Markings, Typical Aggregates, Color Surface Treatment, and Horizontal Curves. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317734.

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Various highway projects, roadway safety, and maintenance all hinge on pavement friction. INDOT's pavement friction test program has played a crucial role in addressing issues like wet pavement crash reduction, durable pavements surface friction, and sustainable aggregates. However, changes in the transportation sector, allied industries, societal needs, and economics present unique challenges that require proactive solutions. First, the existing field friction testing method, which uses a locked wheel skid tester (LWST) is limited to straight, flat pavement sections and excludes crash-prone a
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Morsy, Amr, and Islam Ebo. Development of Physics-Based Deterioration Models for Reinforced Soil Retaining Structures. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2024.2360.

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Reinforced soil walls are key earth retention features in the transportation infrastructure. They are used to support and retain soil in a wide variety of crucial structures, such as highways, bridges, and railways, to ensure stability. They also provide solutions for constructing embankments and slopes in constrained spaces, allowing for efficient land use and improved infrastructure planning. This study used advanced numerical modeling to improve the understanding of the behavior and long-term performance of the aging reinforced soil walls from the 1970s for asset management purposes. An ass
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