Construction costs forecasting: comparison of the accuracy of linear regression and support vector machine models

摘要

Each contract for a construction project has the costs as an essential element, so the accuracy of forecasting the construction costs can have an impact on the project realization, and also, on the project participants’ business. Data for structures (75) were used for modelling with two predictive models: linear regression model (LR) and support vector machine (SVM) model, using Bromilow’s model for cost and time relation and predictive modelling software DTREG. The mean absolute percentage error (MAPE) for the SVM model is 0.3% and for the linear regression model is 4.79%. Comparison of the models’ results pointed out that the forecasting with SVM was significantly more accurate.