Prediction of quality performance using artificial neural networks

Purpose - The purpose of this paper is to enable construction project team members to understand the factors that they must closely monitor to complete projects with a desired quality and also to predict quality performance during the course of a project. With quality being one of the prime concerns of clients in their construction projects, there is a definite need to monitor its performance. Design/methodology/approach - The study discussed here is an extension of past research in which 55 project performance attributes were identified based on expert's opinion and literature survey which after analysis resulted in 20 factors (11 success and nine failure). The results of the second stage questionnaire survey conducted have been used to develop the quality performance prediction model based on artificial neural networks (ANN). Findings - The analyses of the responses led to the conclusion that factors such as project manager's competence, monitoring and feedback by project participants, commitment of all project participants, good coordination between project participants and availability of trained resources significantly affect the quality performance criterion. The best prediction model was found to be a 5‐5‐1 feed forward neural network based on back propagation algorithm with a mean absolute percentage deviation (MAPD) of 8.044 percent. Practical implications - Project professionals can concentrate on certain factors instead of handling all the factors at the same time to achieve the desired quality performance. Also the study may be helpful for the project manager and his/her team to predict the quality performance of the project during its course. Originality/value - The present study resulted in a model to predict the quality performance based on the factors identified as critical using ANN. With the control of the identified critical factors and usage of the prediction model, the desired quality performance can be achieved in construction projects.