Modeling the Completion Time of Public School Building Projects Using Neural Networks
The Ministry of Education in Iraq is confronting a colossal deficiency in school buildings while stakeholders of government funded school buildings projects are experiencing the ill effects of extreme delays caused by many reasons. Those stakeholders are particularly worried to know ahead of time (at contract assignment) the expected completion time of any new school building project. As indicated by a previous research conducted by the authors, taking into account the opinions of Iraqi experts involved with government funded school building projects, nine major causes of delay in school building projects were affirmed through a questionnaire survey specifically are; the contractor's financial status, delay in interim payments, change orders, the contractor rank, work stoppages, the contract value, experience of the supervising engineers, the contract duration and delay penalty. In this research, two prediction models (A and B) were produced to help the concerned decision makers to foresee the expected completion time of typically designed school building projects having (12) and (18) classes separately. The ANN multi-layer feed forward with back-propagation algorithm was utilized to build up the mathematical equations. The created prediction equations demonstrated a high degree of average accuracy of (96.43%) and (96.79%) for schools having (12) and (18) classes, with (R2) for both ANN models of (79.60%) and (85.30%) respectively. It was found that the most influential parameters of both models were the ratio of the sum of work stoppages to the contract duration, the ratio of contractor's financial status to the contract value, the ratio of delay penalty to the total value of contract and the ratio of mean interim payments duration to the contract duration.
MOEDU, “Statistics”, Ministry of Education - Department of Educational Planning in cooperation with the Central Statistical Organization (CSO), Iraq, 2012, (In Arabic).
MOP, “Annual statistical report”, Ministry of Planning, Iraq, 2013, (In Arabic).
Khaled, Z. S. M., Ali, R. S. A. and Hassan, M. F. “Predicting the Delivery Time of Public School Building Projects Using Nonlinear Regression”, Engineering and Technology Journal, Vol. 34, Part (A), No. 8, 2016, pp. 1538-1548.
Bhokha, S. and Ogunlana, S., “Application of Artificial Neural Network to Forecast Construction Duration of Buildings at the Predesign Stage”, Engineering Construction & Architectural Management, Vol.6, No.2, 1999. pp. 133-144. https://doi.org/10.1108/eb021106.
Attal, A., “Development of Neural Network Models for Prediction of Highway Construction Cost and Project Duration”, MSc Thesis, Ohio University, USA, 2010.
Yahia, H., Hosny, H. and Abdel Razik, M., “Time Contingency Assessment in Construction Projects in Egypt using Artificial Neural Networks Model”, International Journal of Computer Science Issues (IJCSI), Vol.8, No.2, 2011, pp. 523-531.
Petruseva, S., Zujo, V. and Zileska-Pancovska, V., “Neural Network Prediction Model for Construction Project Duration”, International Journal of Engineering Research & Technology (IJERT), Vol. 2, No.11, 2013, pp. 1646-1654.
Gab-Allah, A. A., Ibrahim, A. H. and Hagras, O. A. , “Predicting the Construction Duration of Building Projects using Artificial Neural Networks”, International Journal of Applied Management Science, Vol.7, No.2, 2015, pp. 123-141. https://doi.org/10.1504/ijams.2015.069259.
Shahin, M. A., “Use of Artificial Neural Networks for Predicting Settlement of Shallow Foundations on Cohesionless Soils”, PhD Thesis, Department of Civil and Environmental Eng., 2003, University of Adelaide.
Shahin, M., Jaksa, M. and Maier, H., “State of the Art of Artificial Neural Networks in Geotechnical Engineering”, The Electronic Journal of Geotechnical Engineering, No.8, 2008, pp.1-26.
Ipsic, I., “Speech Technologies”, 1st ed., InTech, Croatia, 2011.
Al-saffar, R. Z., Khattab, S. I., Yousif, S. T., “Prediction of Soil's Compaction Parameter Using Artificial Neural Network”, Al-Rafidain Engineering Journal, Vol.21, No.3, 2013, pp.15-27.
Mahmood, K. and Aziz, J., “Using Artificial Neural Networks for Evaluation of Collapse Potential of Some Iraqi Gypseous Soils”, Iraqi Journal of Civil Engineering, Vol.7, No.1, 2011, pp. 21-28.
Joarder, K., Rezaul K. and Ruhul A., “Artificial Neural Networks in Finance and Manufacturing”1st ed., 2006, Idea Group Publishing.
Al-Janabi, K., “Laboratory Leaching Process Modeling in Gypseous Soils using Artificial Neural Networks (ANN)”, PhD Thesis, 2006, Building and Construction Engineering Department, University of Technology.
Khaled, Z. S. M., Frayyeh. Q. J. and Aswed, G. K., “Modeling Final Costs of Iraqi Public School Projects Using Neural Networks”, International Journal of Civil Engineering and Technology, Vol.5, No.7, 2014. pp. 42-54.
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