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Shashidhara, Prajwal (2024) Predictive Modelling for Cost Estimation in Construction Projects Using Machine Learning Algorithms. Masters thesis, Dublin, National College of Ireland.
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Abstract
The construction industry is growing rapidly around the world, with an expectation to reach $10.5 trillion by 2023. The growth is driven by factors such as the urbanization of the population. The industry necessitates building accurate and reliable methods to forecast costs as compared to traditional methods that fall short and overrun the budget. This study explores the application of predictive modelling using various algorithms for cost estimation in construction projects. The research seeks to determine the best method for early-stage cost prediction by examining models that use statistics, machine learning, and deep learning. According to the study, random forest gives higher accuracy in estimating building costs. Floor space, CPI, lot size, and project time are some of the key factors that affect expenses. Machine learning models, according to the findings, can improve the timeliness and accuracy of cost estimations, which in turn helps with improved resource management and project planning. This study highlighted the use of standard data mining methodologies like CRISP-DM (Cross-Industry Standard Process for Data Mining) and the model explainability method SHAP (Shapely Additive exPlanations) to help in better understanding and decision making.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Agarwal, Bharat UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Construction Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 25 Aug 2025 15:07 |
| Last Modified: | 25 Aug 2025 15:07 |
| URI: | https://norma.ncirl.ie/id/eprint/8631 |
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