Predictive Models of Construction Project Success Rating Using Regression and Artificial Neural Network.

This research addresses the gap in comprehensive predictive models for construction project success rating by exploring the potential of regression models to evaluate project success rating. By analyzing 130 datasets from the National Capital Region, the study utilizes Support Vector Regression (SVR), Multiple Linear Regression (MLR), and Artificial Neural Network (ANN) with a 22-30-1 configuration (22 input neurons, 30 neurons in a single hidden layer, and 1 output neuron). The input variables represent critical success factors rated on a scale of 1-5, while the output variable represents the predicted project success percentage rating. Various statistical tools, including ANOVA, Lasso Regression, R², MAE, and MSE, are utilized for evaluation. The findings reveal that SVR achieved the highest accuracy (R² = 0.881, MAE = 2.172, MSE = 7.054), followed closely by MLR (R² = 0.874, MAE = 2.180, MSE = 7.470), while ANN (R² = 0.743, MAE = 3.076, MSE = 15.239) may require refinement. Lasso Regression identified 22 critical success factors, with Financial Condition, Effectiveness in Decision-Making, and Compliance to Quality Standards ranking as the top three. This research contributes to the advancement of construction predictive analytics, which can lead to improved decision-making and more efficient, effective, and ethical construction practices. [ABSTRACT FROM AUTHOR]