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Bernold, L E (2005) Automatic As-Built Generation with Utility Trenchers. Journal of Construction Engineering and Management, 131(06), 740–7.

Chan, W T, Chen, C, Messner, J I and Chua, D K (2005) Interface Management for China’s Build–Operate–Transfer Projects. Journal of Construction Engineering and Management, 131(06), 645–55.

Chen, H, O’Brien, W J and Herbsman, Z J (2005) Assessing the Accuracy of Cash Flow Models: The Significance of Payment Conditions. Journal of Construction Engineering and Management, 131(06), 669–76.

Chua, D K and Goh, Y M (2005) Poisson Model of Construction Incident Occurrence. Journal of Construction Engineering and Management, 131(06), 715–22.

Hanna, A S, Taylor, C S and Sullivan, K T (2005) Impact of Extended Overtime on Construction Labor Productivity. Journal of Construction Engineering and Management, 131(06), 734–9.

Song, J, Fagerlund, W R, Haas, C T, Tatum, C B and Vanegas, J A (2005) Considering Prework on Industrial Projects. Journal of Construction Engineering and Management, 131(06), 723–33.

Sturts, C S and (Bud) Griffis, F H (2005) Addressing Pricing: Value Bidding for Engineers and Consultants. Journal of Construction Engineering and Management, 131(06), 621–30.

Tamate, S, Suemasa, N and Katada, T (2005) Analyses of Instability in Mobile Cranes due to Ground Penetration by Outriggers. Journal of Construction Engineering and Management, 131(06), 689–704.

Zayed, T M (2005) Productivity and Cost Assessment for Continuous Flight Auger Piles. Journal of Construction Engineering and Management, 131(06), 677–88.

Zayed, T M and Halpin, D W (2005) Pile Construction Productivity Assessment. Journal of Construction Engineering and Management, 131(06), 705–14.

  • Type: Journal Article
  • Keywords: Pile foundations; Bored piles; Construction; Productivity; Costs; Neural networks;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2005)131:6(705)
  • 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.

Zhang, X (2005) Criteria for Selecting the Private-Sector Partner in Public–Private Partnerships. Journal of Construction Engineering and Management, 131(06), 631–44.

Zhang, X (2005) Financial Viability Analysis and Capital Structure Optimization in Privatized Public Infrastructure Projects. Journal of Construction Engineering and Management, 131(06), 656–68.