Prediction of Construction Project Performance using Regression Analysis and Artificial Neural Network

Abstract

Assessing project management (or mismanagement) has become an important part of our professional challenge in construction industry today. The unexpected cost overruns and implementation delays result cost overruns and schedule delays of the construction projects and it gives a new meaning to the word performance for project management. Project performance is predicted mainly based on the four performance metrics i.e., cost, schedule, quality, and satisfaction performance. The objective of this research is to develop regression models and neural network models to predict cost performance, schedule performance, quality performance and satisfaction level. Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) technique are used to construct the models to predict project performance, and these models are then compared and validated. This helps to understand the factors that must monitor closely in order for the project success and to forecast performance during the course of the project.