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

Document identifier: oai:DiVA.org:ltu-77758
Access full text here:10.3390/su12041514
Keyword: Engineering and Technology, Civil Engineering, Geotechnical Engineering, Teknik och teknologier, Samhällsbyggnadsteknik, Geoteknik, Delay sources, Risk management, Random forest‐genetic algorithm, Computer aid, Construction project, Soil Mechanics
Publication year: 2020
Relevant Sustainable Development Goals (SDGs):
SDG 9 Industry, innovation and infrastructureSDG 11 Sustainable cities and communities
The SDG label(s) above have been assigned by OSDG.ai

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. 

Authors

Zaher Mundher Yaseen

Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Zainab Hasan Ali

Civil Engineering Department, College of Engineering, University of Diyala, Baquba, Iraq
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Sinan Q. Salih

Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
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Nadhir Al-Ansari

Luleå tekniska universitet; Geoteknologi
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