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

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 i...

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Vydáno v:Sustainability Ročník 12; číslo 4; s. 1514
Hlavní autoři: Yaseen, Zaher Mundher, Ali, Zainab Hasan, Salih, Sinan Q., Al-Ansari, Nadhir
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 2020
Témata:
Accuracy
Artificial intelligence
Civil engineering
computer aid
construction project
Contractors
Decision trees
delay sources
Genetic algorithms
Geoteknik
Labor productivity
Literature reviews
Neural networks
Questionnaires
random forest‐genetic algorithm
Research methodology
risk management
Soil Mechanics
ISSN:2071-1050, 2071-1050
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Shrnutí: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.
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ISSN:2071-1050
2071-1050
DOI:10.3390/su12041514