Mahmoodzadeh, A; Nejati, H R; Flaih, L R; Ibrahim, H H; Alenizi, F A and Alassaf, Y (2024) A rigorous examination of twelve cutting-edge machine-learning techniques for predicting time and cost in tunneling projects. Journal of Construction Engineering and Management, 150(12): 04024176, ISSN 0733-9364
Abstract
This study aimed to investigate and analyze the performance of twelve machine learning (ML) algorithms in estimating the construction time and cost of drill and blast tunnels. Thirteen tunnels located in different regions of Iran were selected, resulting in 900 data sets. Ten parameters were identified as influential factors affecting the construction time and cost of these tunnels. 80% of the data set was used for training, while the remaining 20% was reserved for testing. Additionally, 288 unseen data sets were utilized for evaluation purposes. All the algorithms demonstrated accurate performance on the test data sets, with R-squared values exceeding 0.93. However, only the Gaussian process regression algorithm achieved satisfactory results on the unseen data sets. Furthermore, a graphical user interface (GUI) was developed based on the trained ML models. This GUI allows real-time estimation of the time and cost of drill and blast tunnels and can be updated during construction.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | construction cost; construction time; drill and blast tunnels; graphical user interface; machine learning |
| Index terms: | time estimation, construction cost, tunnel, testing, construction time, machine learning, user interface, estimating, influential factor |
| Subjects: | professional practice, infrastructure and transport systems, human-computer interaction, financial and cost management, risk assessment, artificial intelligence, operations research, project controls |
| Topics: | Risk Management, Cost Management, Time Control, Digital Applications, Engineering Principles |
| Descriptive scope: | 2 PC |
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