Project Success Prediction through Evaluating Parameters Affecting Productivity

Abstract

Construction productivity is of remarkable significance to the economic growth of the countries. Many external and internal factors will affect the project productivity, and it is difficult to predict their impact. Although many factors have been identified in the previous studies, how these factors specifically influence the productivity in different conditions remained unclear. The main objectives of this paper are to identify and analyses factors affecting project productivity in construction projects and construct a model for predicting the project success rate through evaluating the project conditions. Questionnaires and interviews are planned to collect data about the rate of different parameters and the project success rate. Questionnaires are filled by experts within the construction sector including site supervisors, project managers, construction engineers, foremen and academics. The analytical hierarchy process (AHP) and the artificial neural network (ANN) methods are used to analyses data. Through AHP analysis, overall factors affecting productivity are ranked according to their significance. The ANN networks are developed based on influencing factors rating, sub-categories rating and the project success rates using training algorithm. With the developed networks, the project success rate of a construction project in China is predicted as the case study.