Journal Article

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

Yaseen, Zaher Mundher, Salih, Sinan Q., Ali, Zainab Hasan, Al-Ansari, Nadhir

2020

Overview

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.

• Publisher: MDPI AG
• Subject: 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