Evaluation of plastering crew performance in building projects using data envelopment analysis

The research question addressed in this study was how the performance of construction crews working in a certain project or locality could be evaluated, ranked and improved. To develop and demonstrate the relevant framework, data envelopment analysis (DEA) was applied to establish the relative efficiency of plastering crews working in building projects located in different cities around Turkey. Data were collected from 40 crews of varying characteristics, and their technical efficiency scores were computed using the Banker, Charnes and Cooper (BCC) model, which is based on variable returns-to-scale (VRS). The model yields efficiency scores that range between 0 and 1, and a company or crew is considered efficient if its score is 1.0 (100%). Efficient and inefficient crews were identified and ranked on this basis in the study. Cross tabulation analyses were subsequently conducted to gain further insights into the relationships between the efficiency scores and input factors of numbers of skilled and unskilled laborers, daily labor unit costs, work hours, average age of crew members, total crew experience, plastering location, plastering technique, and plaster type. No discernible relationship could be identified between the efficiency scores and productivity outputs of the crews. It was found that plastering technique, plastering location, and total crew experience had a significant association with crew efficiency. Efficiency improvement strategies identified included training, hiring experienced plasterers, adopting more advanced plastering technology, implementing better jobsite management practices, and enhancing workers’ knowledge, skills and attitude towards productivity and quality. First published online: 25 Sep 201

Modelling masonry crew productivity using two artificial neural network techniques

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. First published online: 22 Oct 201