Toward predictive modelling of construction cost overruns using support vector machine techniques

The development of cost overrun prediction models using data mining techniques has considerably increased in recent years. Estimating the final cost of construction projects is essential during the contract award stage of the building process. Projects variables from archival data are important in developing prediction models. This research examines the effectiveness of support vector machines in predicting construction project cost overruns using data from archival records.The independent variables, like number of stories, gross floor area, change in scope, contract type, provisional sum, tendering type, and initial contract sum, were extracted from historical records. In this study, SVM models using linear, RBF, and polynomial kernel functions demonstrated that SVM using linear and polynomial kernel techniques were used in this research. This study looks at how well data mining tools forecast cost overruns in building projects using information from historical records.The results revealed that the linear kernel SVM model could produce accurate construction cost predictions with 0.99 R2, 0.099 RMSE, 0.05 MAE, 0.278 MAPE, and 0.01 MSE on the accuracy test data. When considered collectively, it is clear that gross floor space, story count, tendering method, and scope modification are reliable indicators of cost overruns in the construction sector.The created SVM model can be applied as a cost-estimating tool to predict potential cost overruns for Ghanaian construction projects.