2018
DOI: 10.28991/cej-0309151
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Evaluation of Legislation Adequacy in Managing Time and Quality Performance in Iraqi Construction Projects- a Bayesian Decision Tree Approach

Abstract: Delay and quality defects are significant problems in Iraqi construction projects. During the period from 2003-2014, legislation has been changed to enhance the performance of construction project. This change is done by modifying some clauses of legislation and adding or deleting the others. The aim of this study is to evaluate the adequacy of these changes by using questionnaire and Bayesian decision tree model. 30 projects were taken for the period from 2003-2014. Performance of construction project was ass… Show more

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Cited by 10 publications
(8 citation statements)
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“…Methods Results [30] Questionnaire survey, decision tree and Naive Bayes Accuracy of decision tree 79.41% is higher than Naive Bayes by 5.81% [31] Questionnaire and Bayesian decision tree…”
Section: Authormentioning
confidence: 99%
See 1 more Smart Citation
“…Methods Results [30] Questionnaire survey, decision tree and Naive Bayes Accuracy of decision tree 79.41% is higher than Naive Bayes by 5.81% [31] Questionnaire and Bayesian decision tree…”
Section: Authormentioning
confidence: 99%
“…The authors evidenced the capacity of the decision tree has higher accuracy by 79.41% over the Naive Bayes model, which showed a lower accuracy value of 73.52% [30]. Naji et al (2018) used a Bayesian decision tree model to predict the impact of contract changes on the time and quality performance of construction projects [31]. The model performed with good accuracy in the prediction process and caused an improvement in the project performance.…”
Section: Research Backgroundmentioning
confidence: 99%
“…Several machine learning methods were then implemented afterward to predict various measurable units such as decision tree for binary delay prediction (Soibelman and Kim, 2002), multiple linear regression for time overrun prediction (Asiedu et al, 2016) and Bayesian Belief networks for time performance prediction (Joko Wahyu Adi et al, 2016). Recently, the utilization of prediction tools has been expanded both in terms of the number of studies and machine learning tools such as Gradient Boosting Trees (Hassan et al, 2018), Naı €ve Bayes (Hassan et al, 2018), Support Vector Machine (Mahmoodzadeh et al, 2020), Random Forest (Yaseen et al, 2020) and K-Nearest Neighbor (Sanni-Anibire et al, 2020). Overall, machine learning methods have been used widely for the early detection of conditions that might cause time overruns (Kassem et al, 2021).…”
Section: The Adoption Of Technologymentioning
confidence: 99%
“…However, the consultant's feeling of superiority over the contractor may prevent the consultant from giving attention to the contractor's requests [6]. According to Hassan et al, 2018, Contractors may discover discrepancies, omissions, errors, and conflicts in the documents and request a consultant's opinion regarding the problem. A variation order will then be issued with additional costs to solve the problem [7].…”
Section: Introductionmentioning
confidence: 99%
“…According to Hassan et al, 2018, Contractors may discover discrepancies, omissions, errors, and conflicts in the documents and request a consultant's opinion regarding the problem. A variation order will then be issued with additional costs to solve the problem [7]. Enshassi et al, 2010 stated that variation order affected the project performance as it will affect productivity and the project costs [8].…”
Section: Introductionmentioning
confidence: 99%