Predicting Success in Residential Construction Projects: A Review of Models and Methodologies

Abstract

Predicting the success of residential construction projects is a mind-boggling task that requires viable management of timetables, financial plans, and dangers. Traditional undertaking management strategies often fall short in accurately forecasting project results because of their inability to handle large datasets and distinguish stowed away patterns. This study addresses this issue by systematically reviewing the application of machine learning algorithms in construction project management. The research strategy involved an extensive literature review to distinguish various ML algorithms utilized in the field, including relapse, classification, clustering, neural organizations, and troupe techniques. Key stages in applying these algorithms, for example, data assortment, feature engineering, model training, and validation, were examined. The findings feature the significant potential of ML algorithms in enhancing prescient accuracy and dynamic in construction projects. Advanced feature engineering, integration of Explainable, and improvement of crossover models were distinguished as critical factors for improving model performance. Ethical considerations and the requirement for great data were also emphasized. This study infers that ML techniques can alter construction project management by making it more data-driven and prescient, ultimately leading to more successful undertaking results.