Pile Construction Productivity Assessment

Abstract

Bored piles are vital elements for highway bridge foundation. A large number of factors oversees productivity and cost estimation processes for piles, which creates many problems for the time and cost estimators of such process. Therefore, current study is designed to diagnose these problems and assess productivity, cycle time, and cost for pile construction using the artificial neural network (ANN). Data were collected for this study through designated questionnaires, site interviews, and telephone calls to experts in different construction companies. Many variables have been considered to manage the piling construction process. Three-layer, feed forward, and fully connected ANNs were trained with an architecture of seven input neurons, five output neurons, and different hidden layer neurons. The ANN models were validated and proved their robustness in output assessments. Three sets of charts have been developed to assess productivity, cycle time, and cost. This research is relevant to both industry practitioners and researchers. It provides sets of charts for practitioners’ usage to schedule and price out pile construction projects. In addition, it provides researchers with a methodology of applying ANN to pile construction process, its limitation, and future suggestions.

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