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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Vydané v:	Sustainability Ročník 12; číslo 4; s. 1514
Hlavní autori:	Yaseen, Zaher Mundher, Ali, Zainab Hasan, Salih, Sinan Q., Al-Ansari, Nadhir
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 2020
Predmet:	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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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.
AbstractList	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. 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.
Author	Yaseen, Zaher Mundher Salih, Sinan Q. Ali, Zainab Hasan Al-Ansari, Nadhir
Author_xml	– sequence: 1 givenname: Zaher Mundher orcidid: 0000-0003-3647-7137 surname: Yaseen fullname: Yaseen, Zaher Mundher – sequence: 2 givenname: Zainab Hasan orcidid: 0000-0003-1916-8164 surname: Ali fullname: Ali, Zainab Hasan – sequence: 3 givenname: Sinan Q. surname: Salih fullname: Salih, Sinan Q. – sequence: 4 givenname: Nadhir orcidid: 0000-0002-6790-2653 surname: Al-Ansari fullname: Al-Ansari, Nadhir
BackLink	https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-77758$$DView record from Swedish Publication Index
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Snippet	Project delays are the major problems tackled by the construction sector owing to the associated complexity and uncertainty in the construction activities....
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SubjectTerms	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
Title	Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model
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