An Artificial neural networks (ANN) model for evaluating construction project performance based on coordination factors

References

• Adeli, H. , & Wu, M. (1998). Regularization neural network for construction cost estimation. Journal of Construction Engineering and Management , 124(1), 18–24.
   Web of Science ®Google Scholar
• Alaloul, W. S. , Liew, M. S. , & Zawawi, N. A. B. W. A. (2016a). A framework for coordination process into construction projects . Paper presented at the MATEC Web of Conferences.
   Google Scholar
• Alaloul, W. S. , Liew, M. S. , & Zawawi, N. A. W. A. (2016b). Identification of coordination factors affecting building projects performance. Alexandria Engineering Journal , 55(3), 2689–2698.
   Web of Science ®Google Scholar
• Attalla, M. , & Hegazy, T. (2003). Predicting cost deviation in reconstruction projects: Artificial neural networks versus regression. Journal of Construction Engineering and Management , 129(4), 405–411. doi:10.1061/(ASCE)0733-9364(2003)129:4(405)
   Web of Science ®Google Scholar
• Barlett, J. E. , Kotrlik, J. W. , & Higgins, C. C. (2001). Organizational research: Determining appropriate sample size in survey research. Information Technology, Learning, and Performance Journal , 19(1), 43.
   Google Scholar
• Beach, R. , Webster, M. , & Campbell, K. M. (2005). An evaluation of partnership development in the construction industry. International Journal of Project Management , 23(8), 611–621. doi:10.1016/j.ijproman.2005.04.001
   Google Scholar
• Chan, A. P. C. , & Chan, A. P. L. (2004). Key performance indicators for measuring construction success. Benchmarking: an International Journal , 11(2), 203–221. doi:10.1108/14635770410532624
   Google Scholar
• Chang, A. S. , & Shen, F.-Y. (2009). Coordination needs and supply of construction projects. Engineering Management Journal , 21(4), 44–57. doi:10.1080/10429247.2009.11431844
   Web of Science ®Google Scholar
• Dvir, D. , Ben-David, A. , Sadeh, A. , & Shenhar, A. J. (2006). Critical managerial factors affecting defense projects success: A comparison between neural network and regression analysis. Engineering Applications of Artificial Intelligence , 19(5), 535–543. doi:10.1016/j.engappai.2005.12.002
   Web of Science ®Google Scholar
• Gliem, J. A. , & Gliem, R. R. (2003). Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales . Midwest research to practice conference in adult, continuing, and community education, Columbus, Ohio: Ohio State University.
   Google Scholar
• Heravi, G. , & Eslamdoost, E. (2015). Applying artificial neural networks for measuring and predicting construction-labor productivity. Journal of Construction Engineering and Management , 141(10), 04015032. doi:10.1061/(ASCE)CO.1943-7862.0001006
   Web of Science ®Google Scholar
• Hossain, L. (2009). Communications and coordination in construction projects. Construction Management and Economics , 27(1), 25–39. doi:10.1080/01446190802558923
   Google Scholar
• Iyer, K. C. , & Jha, K. N. (2003). Analysis of critical coordination activities of Indian construction projects . Paper presented at the Proceedings of 19th annual conference of association of researchers in the construction management (ARCOM), University of Brighton, UK.
   Google Scholar
• Iyer, K. C. , & Jha, K. N. (2006). Critical factors affecting schedule performance: Evidence from Indian construction projects. Journal of Construction Engineering and Management , 132(8), 871–881. doi:10.1061/(ASCE)0733-9364(2006)132:8(871)
   Web of Science ®Google Scholar
• Jaffar, N. , Tharim, A. H. A. , & Shuib, M. N. (2011). Factors of conflict in construction industry: A literature review. Procedia Engineering , 20, 193–202. doi:10.1016/j.proeng.2011.11.156
   Google Scholar
• Jha, K. N. , & Chockalingam, C. T. (2009). Prediction of quality performance using artificial neural networks: Evidence from Indian construction projects. Journal of Advances in Management Research , 6(1), 70–86. doi:10.1108/09727980910972172
   Google Scholar
• Ling, F. , Yng, Y. , & Liu, M. (2004). Using neural network to predict performance of design-build projects in Singapore. Building and Environment , 39(10), 1263–1274. doi:10.1016/j.buildenv.2004.02.008
   Web of Science ®Google Scholar
• Love, P. E. D. , Irani, Z. , & Edwards, D. J. (2004). A rework reduction model for construction projects. IEEE Transactions on Engineering Management , 51(4), 426–440. doi:10.1109/TEM.2004.835092
   Web of Science ®Google Scholar
• Moselhi, O. , Hegazy, T. , & Fazio, P. (1991). Neural networks as tools in construction. Journal of Construction Engineering and Management , 117(4), 606–625. doi:10.1061/(ASCE)0733-9364(1991)117:4(606)
   Web of Science ®Google Scholar
• Ortiz, O. , Castells, F. , & Sonnemann, G. (2009). Sustainability in the construction industry: A review of recent developments based on LCA. Construction and Building Materials , 23(1), 28–39. doi:10.1016/j.conbuildmat.2007.11.012
   Web of Science ®Google Scholar
• Penda, S. V. B. , Djellout, H. , & Proïa, F. (2014). Moderate deviations for the Durbin–Watson statistic related to the first-order autoregressive process. ESAIM: Probability and Statistics , 18, 308–331. doi:10.1051/ps/2013038
   Web of Science ®Google Scholar
• Rafiq, M. Y. , Bugmann, G. , & Easterbrook, D. J. (2001). Neural network design for engineering applications. Computers & Structures , 79(17), 1541–1552. doi:10.1016/S0045-7949(01)00039-6
   Web of Science ®Google Scholar
• Rani, C. H. S. , Kumar, V. P. , & Togati, V. K. (2013). Artificial neural networks (ANNS) for prediction of engineering properties of soils. International Journal of Innovative Technology and Exploring Engineering , 3(1), 123–130.
   Google Scholar
• Saram, D. D. D. , & Ahmed, S. M. (2001). Construction coordination activities: What is important and what consumes time. Journal of Management in Engineering , 17(4), 202–213. doi:10.1061/(ASCE)0742-597X(2001)17:4(202)
   Web of Science ®Google Scholar
• Simatupang, T. M. , Wright, A. C. , & Sridharan, R. (2002). The knowledge of coordination for supply chain integration. Business Process Management Journal , 8(3), 289–308. doi:10.1108/14637150210428989
   Google Scholar
• Taylor, R. (1990). Interpretation of the correlation coefficient: A basic review. Journal of Diagnostic Medical Sonography , 6(1), 35–39. doi:10.1177/875647939000600106
   Web of Science ®Google Scholar
• Tu, J. V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology , 49(11), 1225–1231.
   PubMed Web of Science ®Google Scholar
• Ward, S. , & Chapman, C. (2008). Stakeholders and uncertainty management in projects. Construction Management and Economics , 26(6), 563–577. doi:10.1080/01446190801998708
   Google Scholar
• Wu, C. L. , Chau, K. W. , & Li, Y. S. (2009). Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques. Water Resources Research , 45, 8.
   Web of Science ®Google Scholar
• Yang, R. J. , & Shen, G. Q. P. (2014). Framework for stakeholder management in construction projects. Journal of Management in Engineering , 31(4), 04014064. doi:10.1061/(ASCE)ME.1943-5479.0000285
   Web of Science ®Google Scholar
• Yitmen, I. , & Soujeri, E. (2010). An artificial neural network model for estimating the influence of change orders on project performance and dispute resolution. Safety , 9, 3.
   Google Scholar
• Yu, C.-C. , & Liu, B.-D. (2002). A backpropagation algorithm with adaptive learning rate and momentum coefficient . Paper presented at the Neural Networks, IJCNN’02. Proceedings of the International Joint Conference on Neural Networks, IEEE, Honolulu, HI, USA.
   Google Scholar
• Zhang, G. , Hu, M. Y. , Patuwo, B. E. , & Indro, D. C. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Operational Research , 116(1), 16–32. doi:10.1016/S0377-2217(98)00051-4
   Web of Science ®Google Scholar