Predictive Models of Construction Project Success Rating Using Regression and Artificial Neural Network
Clyde L. Tamayo | Jerome Jordan F. Famadico
Discipline: Civil Engineering
Abstract:
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.
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