Hybrid neural based model for predicting the construction project performance of high rise building projects in Metro Manila, Philippines

Abstract

Assessing construction project performance through adapting an innovative approach can multiply the production of high-quality project outputs which is a catalyst to the socio-economic progress of a country. Preliminary data collection was employed through a meta-cognitive analysis of reviewing related literature which directs to the backbone of qualitative information that is relevant to the periodically experienced construction performance-influencing factors and to develop an assessment questionnaire about the influencing factors affecting the project performance. The IBM SPSS program was used to verify the reliability and consistency of the fundamental statistics of the questionnaire responses of cost, time and quality performance ratings. A predictive mathematical model was developed for forecasting cost, time, and quality performance rating employing Levenberg-Marquardt training algorithm with Hyperbolic Tangent Sigmoid function. The prediction model result shows a highly satisfying performance on its variance from the substantive values and suggests a high correlation between these values. The relative importance of the factors affecting the cost, time, and quality performance rating was calculated via sensitivity analysis through connection weights using Garson's Algorithm to view the order of influence of the parameters that have great significance to the success of a project.