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Labor productivity plays an important role in the successful delivery of construction projects. It is substantially important to understand the nature and extent to which certain factors affect productivity. A field study conducted to examine a set of parameters that impact labor productivity on building construction jobsites is presented. These parameters include temperature, relative humidity, wind speed, precipitation, gang size, crew composition, height of work, type of work and the method employed. Techniques from inferential statistics and artificial intelligence such as Fuzzy subtractive clustering, neural network modeling and stepwise variable selection procedure are used to analyze and determine the relative importance and contribution of each parameter towards productivity estimates. Required data was collected over a period of ten months directly from construction jobsites. For further insight on the impact of these factors on productivity, a set of neural network models were developed. Productivity is expressed as a function of one parameter at a time. The trends obtained in this study are compared with related material in literature and the findings are reported.

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Zafar Khan graduated in Civil Engineering from NED University Pakistan. He has worked in the areas of highway design, sewage treatment processes and construction project management. Subsequent to this Masters research, he is presently working as a PhD candidate at Concordia University Canada with a number of technical publications at his credit.

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