Modelling masonry crew productivity using two artificial neural network techniques

Abstract

Artificial neural networks have been effectively used in various civil engineering fields, including construction management and labour productivity. In this study, the performance of the feed forward neural network (FFNN) was compared with radial basis neural network (RBNN) in modelling the productivity of masonry crews. A variety of input factors were incorporated and analysed. Mean absolute percentage error (MAPE) and correlation coefficient (R) were used to evaluate model performance. Research results indicated that the neural computing techniques could be successfully employed in modelling crew productivity. It was also found that successful models could be developed with different combinations of input factors, and several of the models which excluded one or more input factors turned out to be better than the baseline models. Based on the MAPE values obtained for the models, the RBNN technique was found to be better than the FFNN technique, although both slightly overestimated the masons’ productivity.

Keywords:

• productivity modelling
• crew productivity
• construction industry
• masonry
• artificial neural networks

Additional information

Notes on contributors

İbrahim Halil Gerek

İbrahim Halil GEREK. He is an Assistant Professor of Civil Engineering in the Engineering and Natural Sciences Faculty at Adana Science and Technology University. He is a member of Chamber of Civil Engineers of Turkey, and is actively involved in construction management areas. He received his MS and PhD degrees in Civil Engineering at the Cukurova University between 2001 and 2010 years, respectively.

Ercan Erdis

Ercan ERDIS. He is an Assistant Professor of Civil Engineering in the Faculty of Engineering at Mustafa Kemal University. He is a member of Chamber of Civil Engineers of Turkey, and is actively involved in construction management areas. He received his MS and PhD degrees in Civil Engineering at the Mustafa Kemal University in 2001 and Cukurova University in 2004, respectively.

Gulgun Mistikoglu

Gulgun MISTIKOGLU. He is an Assistant Professor of Civil Engineering in the Antakya Vocational College at Mustafa Kemal University. He is a member of Chamber of Civil Engineers of Turkey, and is actively involved in construction management areas. He received his MS and PhD degrees in Civil Engineering at the Cukurova University between 1987 and 2001 years, respectively.

Mumtaz Usmen

Mumtaz USMEN. Currently, he is a Professor at the College of Engineering at Wayne State University. He served as Interim Dean and Chairman of the Civil and Environmental Engineering Department at Wayne State University from 1989 to 2009. Prior to coming to Wayne State, he was a Professor of Civil Engineering at West Virginia University, Morgantown, WV, where he established and directed a NIOSH sponsored Center for Excellence on Construction Safety. He specializes in construction engineering and management with a focus on site safety, quality, ethics, leadership, and organizational improvement.