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

Construction projects are delivered in a multidisciplinary environment, which need continues coordination. The aim of this paper is to develop an ANN model to evaluate the influence of coordination factors on construction projects performance. For this purpose, the most effective 16 coordination fac...

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Bibliographic Details
Published in:Cogent engineering 2018-01, Vol.5 (1), p.1507657
Main Authors: Alaloul, Wesam Salah, Liew, Mohd Shahir, Wan Zawawi, Noor Amila, Mohammed, Bashar S, Adamu, Musa
Format: Article
Language:English
Subjects:
Artificial neural networks
Back propagation
Back propagation networks
construction projects
Multilayers
Neural networks
Performance evaluation
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Summary:Construction projects are delivered in a multidisciplinary environment, which need continues coordination. The aim of this paper is to develop an ANN model to evaluate the influence of coordination factors on construction projects performance. For this purpose, the most effective 16 coordination factors impacting the construction projects performance have been identified. After that, through a questionnaire survey, the extent of coordination factors application and the corresponding project's performance were collected. Three multilayer feed-forward networks with Back-Propagation and Elman-Propagation algorithms were adopted to train, validate, and test the cost, time and quality, as performance evaluation indicators. Consequently, the training process continues unit it reaches the pre-defined error or up to 1000 epochs. The results of Mean Square Error (MSE) confirmed the accuracy of the networks with an average value of 0.0231. Furthermore, the determination coefficient (R 2 ) for the three networks of cost, time, and quality were obtained to be 0.77, 0.76 and 0.75, respectively.
ISSN:2331-1916
2331-1916
DOI:10.1080/23311916.2018.1507657