Machine Learning Algorithms for Construction Projects Delay Risk Prediction

Gondia, Ahmed; Siam, Ahmad; El‐Dakhakhni, Wael; Nassar, Ayman H.

doi:10.1061/(asce)co.1943-7862.0001736

Cited by 189 publications

(115 citation statements)

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“…The prediction of project delay based on internal and external sources can help project managers to provide an accurate forecast of the project schedule, and this can assist a proactive management approach in the construction project [11]. Construction projects are dynamic and complex, included a huge number of project stockholders, feedback processes and non-linear relationships [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…The model performed with good accuracy in the prediction process and caused an improvement in the project performance. Gondia et al (2020) utilized Naive Bayes and decision tree models to predict the delay risk in construction projects. The study revealed the power of AI models in delay risk prediction and improving risk management strategies [11].…”

Section: Research Backgroundmentioning

confidence: 99%

“…Gondia et al (2020) utilized Naive Bayes and decision tree models to predict the delay risk in construction projects. The study revealed the power of AI models in delay risk prediction and improving risk management strategies [11]. Based on the reported studies in the literature, the current research is established with the aim of providing a reliable methodology for delay risk prediction that will contribute to the baseline knowledge of construction management.…”

Section: Research Backgroundmentioning

confidence: 99%

“…The existence of a delay problem is related to interdependent factors that affect the construction project and the complexity and uncertainty of construction activities. Thus, providing of an efficient tool for analyzing delay factors is key for estimating an accurate duration in construction projects [11].By recalling previous studies, Chan (2001) used regression analysis to identify time-cost relationships for building projects in Malaysia [2]. This approach was developed for managers and owners to estimate the average time that is required for project delivery.…”

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confidence: 99%

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

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2020

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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.Sustainability 2020, 12, 1514 2 of 14 in terms of litigation, dispute and arbitration [5]. Delays are caused by many sources and factors such as the owner [6,7], designer [3,8], contractor [4,7], materials [4,7], project [7,8], labor [9] and external factors [3,10]. Literature ReviewThe prediction of project delay based on internal and external sources can help project managers to provide an accurate forecast of the project schedule, and this can assist a proactive management approach in the construction project [11]. Construction projects are dynamic and complex, included a huge number of project stockholders, feedback processes and non-linear relationships [12]. The existence of a delay problem is related to interdependent factors that affect the construction project and the complexity and uncertainty of construction activities. Thus, providing of an efficient tool for analyzing delay factors is key for estimating an accurate duration in construction projects [11].By recalling previous studies, Chan (2001) used regression analysis to identify time-cost relationships for building projects in Malaysia [2]. This approach was developed for managers and owners to estimate the average time that is required for project delivery. Chan and Chan (2004) performed multiple regression exercises to analyze data related to the time performance of construction projects [13]. The results indicated that multiple regression was used as a useful method to predict time performance in construction projects. Rezaie et al. (2007) used Monte Carlo analysis to investigate the effects of uncertainties on the schedule performance [14]. The results revealed that this method is a good tool to simulate the relationship of uncertainties of construct...

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

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

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2020

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Resilient Project Scheduling Using Artificial Intelligence: A Conceptual Framework

Dahmani

Ben-Ammar

Jebali

2021

Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems

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This paper explores the role that Artificial Intelligence (AI) can play in building resilient project schedules. Based on a literature review and brainstorming sessions, we introduce a conceptual framework that details how AI-enabled predictive and prescriptive analytics can be leveraged to improve project schedule resilience. The latter specifies the potential of AI to make use of historical and real-time data to better contain the effect of disruptions on project schedules.

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