Predicting Delays and Cost Overruns in Construction Projects: A Machine Learning Approach
Predicción de Retrasos y Sobrecostos en Proyectos de Construcción: Un Enfoque de Machine Learning.
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Resumen
Traditionally, at a global level, construction projects face challenges during the planning and execution stages due to poorly organized scheduling and inadequate allocation of roles and resources. This results in significant differences between the projected timeline and what is actually carried out during construction.
This research suggests implementing a Machine Learning (ML) model using earned value analysis and project cash flows to anticipate potential deviations and future progress in a construction project’s schedule, as well as to predict possible cost overruns. The study employs six regression-based machine learning models: Ordinary Least Squares (OLS), Theil-Sen regression (TheilSen), RANSAC regression (RANSAC), Huber regression (Huber), k-nearest neighbors regression (KNNR), and random forest regression (RFR). Two datasets from residential construction projects were used; the first dataset contains 102 records for time estimation, and the second dataset includes 81 records for cash flow estimation. Additionally, the results obtained were compared with a previously proposed model based on Markov chains.
The predictive performance of the implemented ML models showed improved accuracy, increasing the R² compared to previously proposed models. The models achieved mean deviations of 2.09% in predicting future progress and 11.05% in forecasting construction delays, as well as 0.39% in predicting future cash flow and 3.61% in estimating construction cost overruns. This implementation can contribute to improving control, defining strategies, and planning future actions for cost and time management in construction projects, offering a promising approach to enhancing overall project efficiency.
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