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

Zaher Mundher Yaseen, Zainab Hasan Ali, Sinan Q. Salih and Nadhir Al-Ansari
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Zaher Mundher Yaseen: Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Zainab Hasan Ali: Civil Engineering Department, College of Engineering, University of Diyala, Baquba 32001, Iraq
Sinan Q. Salih: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Nadhir Al-Ansari: Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, Sweden

Sustainability, 2020, vol. 12, issue 4, 1-14

Abstract: 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.

Keywords: delay sources; risk management; random forest-genetic algorithm; computer aid; construction project (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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