JAMR 6,1 70 Journal of Advances in Management Research Vol. 6 No. 1, 2009 pp. 70-86 # Emerald Group Publishing Limited 0972-7981 DOI 10.1108/09727980910972172 Prediction of quality performance using artificial neural networks Evidence from Indian construction projects K.N. Jha and C.T. Chockalingam Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi, India Abstract 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. Keywords Construction works, India, Neural nets, Operations management Paper type Research paper Introduction Management of engineering and/or construction projects is a complex undertaking (Anderson, 1992). The participation of the owners, designers, contractors, sub contractors, specialists, consultants etc. indicates the multidisciplinary nature of the constructions projects. With the increase in size of the project, the number of participants in the project is also bound to increase. Though measurement of project performance is a debatable issue in most cases it has been evaluated based on schedule, cost and quality also known as the ‘‘iron triangle’’ (Atkinson, 1999). As such most research work that has been carried out is based on the construction projects in developed countries. With the arrival of the megaprojects for infrastructure development, there has been a boom in the Indian construction industry. The construction sector alone is said to account for 11 percent of India’s GDP and is expected to be on the rise. Infrastructure, airport, metro rail and power sector projects constitute a significant portion in this sector. Fresh opportunities and challenging The current issue and full text archive of this journal is available at www.emeraldinsight.com/0972-7981.htm