Predicting of production rate for excavation operation

The objective of this study is the development of a suitable tool that can be effectively used to predict the production rates of excavation operation in building construction projects. For this purpose, field observations were conducted to collect realistic production rates over a period of twelve months (12/7/2009 to 17/7/2010) at Alexandria (Egypt). Production rate estimation is one of the most frequently discussed topics in the construction industry. Production rates of excavation operation in building construction are affected by several factors. Among these factors are: hauling distance, loading area layout, dumping area layout, pile foundation, excavator bucket capacity, size and number of hauling units. Consequently, its estimating accuracy could be challenged when the effects of these multiple factors are considered simultaneously. There are two objectives of this research. The first one is to develop two models by utilizing the artificial neural networks and statistical regression analysis for predicting the production rate of the excavation operation. The second objective is to develop an optimization system that integrates simulation with genetic algorithms to find the optimal fleet configuration that maximizes the production rate and minimizes the unit cost of the excavation operation in building construction projects.