Construction Productivity Estimation Model Using Artificial Neural Network for Founda-tions Works in Gaza Strip Construction Sites
( Vol-4,Issue-7,July 2017 )

Dr. Eyad Haddad


Artificial Intelligence, Neural Network, Construction Industry, Construction management, Construction projects, Gaza Strip.


Estimating the construction labor productivity con-sidering the effect of multiple factors is important for construction planning, scheduling and estimating. In planning and scheduling, it is important to maximize labor productivity and forecast activity durations to achieve lower labor cost and shorter project duration. In estimating, it is important to predict labor costs.The aim of this study is to develop a new technique for estimating labor productivity rate for foundation works in (m3/ day) for building projects in Gaza Strip, through developing a model that is able to help par-ties involved in construction projects (owner, contrac-tors, and others) especially contracting companies to estimating labor productivity rate for foundation works . This model build based on Artificial Neural Networks. In order to build this model, quantitative and qualitative techniques were utilized to identify the significant parameters for estimating labor productivity rate for foundation works. The data used in model development was collected using questioner survey as a tool to collect actual data from contrac-tors for many projects in Gaza Strip. These question-naires provided 111 examples.The ANN model consid-ered 16 significant parameters as independent input variables affected on one dependent output variable “labor productivity rate for foundation works in (m3/ day)". Neurosolution software was used to train the models. Many models were built but GFF model was found the best model, which structured from one input layer, included 16 input neurons, and included one hidden layer with 22 neurons. The accuracy perfor-mance of the adopted model recorded 98% where the model performed well and no significant difference was discerned between the estimated output and the actual productivity value.Sensitivity analysis was per-formed using Neurosolution tool to study the influ-ence of adopted factors on labor productivity. The performed sensitivity analysis was in general logically where the “Footings Volume” had the highest influ-ence, while the unexpected result was “Payment de-lay” factor which hadn’t any effect on productivity of foundation works.

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[1] NeuroDimension, Inc., 2012.
[2] Creedy, G. D., Skitmore, M. & Sidwell, T., 2006. Risk factors leading to cost overrun in the delivery of highway construction projects, Australia: Re-search Centre: School of Urban Development.
[3] Hsu, C., 2007. The Delphi Technique:Making Sense Of Consensus. Practical Assessment, Re-search & Evaluation electronic journal, 12(10).
[4] Sodikov, J., (2005). Cost estimation of highway projects in developing countries Artificial Neural Network Approach. Journal of the Eastern Asia Society for Transportation Studies, 6, PP. 1036 - 1047.
[5] Gunaydın, M. & Dogan, Z., (2004). A neural net-work approach for early cost estimation of struc-tural systems of buildings. International Journal of Project Management, Volume 22, PP. 595–602.
[6] Principe, J., Lefebvre, C., Lynn, G. & Wooten, D., (2010). NeuroSolutions - Documentation. Help. NeuroDimension, Inc.
[7] Hegazy, T., Moselhi, O. & Fazio, P., (1994). De-veloping practical neural network applications us-ing back- propagation. Micro compurers in Civil Engineering, 9, PP. 145- 159.
[8] Kshirsagar, P. & Rathod, N., 2012. Artificial Neu-ral Network. International Journal of Computer Applications
[9] Ghiassi, M., Saidane, H. & Zimbra, D., (2005). A dynamic artificial neural network model for fore-casting time series events. International Journal of Forecasting, 21, PP. 341-362.
[10] Arafa, M. & Alqedra, M., 2011. Early stage cost estimation of buildings construction projects using ANN. Journal of Artifical Intelligence, 4(1), pp. 63-75.
[11] Dindar, Z., 2004. Artificial Neural Networks Ap-plied To Option Pricing., s.l.: s.n.
[12] Willmott, C. & Matsuura, K., 2005. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, Volume 30, p. 79–82.
[13] Mady M., 2013. Prediction Model of Construc-tion Labor Production Rates in Gaza Strip using Artificial Neural Networks. Unpublished Msc The-sis. The Islamic university of Gaza (IUG).