Sohrabi, H and Noorzai, E (2024) Risk-supported case-based reasoning approach for cost overrun estimation of water-related projects using machine learning. Engineering, Construction and Architectural Management, 31(2), pp. 544-570. ISSN 0969-9988
Abstract
Purpose: The present study aims to develop a risk-supported case-based reasoning (RS-CBR) approach for water-related projects by incorporating various uncertainties and risks in the revision step. Design/methodology/approach: The cases were extracted by studying 68 water-related projects. This research employs earned value management (EVM) factors to consider time and cost features and economic, natural, technical, and project risks to account for uncertainties and supervised learning models to estimate cost overrun. Time-series algorithms were also used to predict construction cost indexes (CCI) and model improvements in future forecasts. Outliers were deleted by the pre-processing process. Next, datasets were split into testing and training sets, and algorithms were implemented. The accuracy of different models was measured with the mean absolute percentage error (MAPE) and the normalized root mean square error (NRSME) criteria. Findings: The findings show an improvement in the accuracy of predictions using datasets that consider uncertainties, and ensemble algorithms such as Random Forest and AdaBoost had higher accuracy. Also, among the single algorithms, the support vector regressor (SVR) with the sigmoid kernel outperformed the others. Originality/value: This research is the first attempt to develop a case-based reasoning model based on various risks and uncertainties. The developed model has provided an approving overlap with machine learning models to predict cost overruns. The model has been implemented in collected water-related projects and results have been reported.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | construction planning; estimating; project management; risk management |
| Index terms: | cost overrun, estimation, risk management, estimate, testing, project management, construction planning, forest, case-based reasoning, overrun, accuracy, earned value management, methodology, construction cost index, dataset, mean square error, machine learning, estimating |
| Subjects: | cost indicators, financial and cost management, construction planning, artificial intelligence, professional practice, environmental science, risk assessment, control systems, cognitive psychology, data management, project controls, research methods, probability and distributions, professional development, project management theory and practice |
| Topics: | Sustainability, Risk Management, Engineering Principles, Information Management, Research Practice, Project Management, Cost Management, Time Control, Site Management, Digital Applications, Design Practice |
| Descriptive scope: | 4 PCTA |
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