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Open AccessArticle

Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model

1
Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
2
Civil Engineering Department, College of Engineering, University of Diyala, Baquba 32001, Iraq
3
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
4
Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, Sweden
*
Authors to whom correspondence should be addressed.
Sustainability 2020, 12(4), 1514; https://doi.org/10.3390/su12041514
Received: 22 December 2019 / Revised: 5 February 2020 / Accepted: 14 February 2020 / Published: 18 February 2020
(This article belongs to the Special Issue Sustainable Construction Engineering and Management)
Project delays are the major problems tackled by the construction sector owing to the associated complexity and uncertainty in the construction activities. Artificial Intelligence (AI) models have evidenced their capacity to solve dynamic, uncertain and complex tasks. The aim of this current study is to develop a hybrid artificial intelligence model called integrative Random Forest classifier with Genetic Algorithm optimization (RF-GA) for delay problem prediction. At first, related sources and factors of delay problems are identified. A questionnaire is adopted to quantify the impact of delay sources on project performance. The developed hybrid model is trained using the collected data of the previous construction projects. The proposed RF-GA is validated against the classical version of an RF model using statistical performance measure indices. The achieved results of the developed hybrid RF-GA model revealed a good resultant performance in terms of accuracy, kappa and classification error. Based on the measured accuracy, kappa and classification error, RF-GA attained 91.67%, 87% and 8.33%, respectively. Overall, the proposed methodology indicated a robust and reliable technique for project delay prediction that is contributing to the construction project management monitoring and sustainability. View Full-Text
Keywords: delay sources; risk management; random forest-genetic algorithm; computer aid; construction project delay sources; risk management; random forest-genetic algorithm; computer aid; construction project
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MDPI and ACS Style

Yaseen, Z.M.; Ali, Z.H.; Salih, S.Q.; Al-Ansari, N. Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model. Sustainability 2020, 12, 1514. https://doi.org/10.3390/su12041514

AMA Style

Yaseen ZM, Ali ZH, Salih SQ, Al-Ansari N. Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model. Sustainability. 2020; 12(4):1514. https://doi.org/10.3390/su12041514

Chicago/Turabian Style

Yaseen, Zaher M.; Ali, Zainab H.; Salih, Sinan Q.; Al-Ansari, Nadhir. 2020. "Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model" Sustainability 12, no. 4: 1514. https://doi.org/10.3390/su12041514

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