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Ballesteros-Pérez, P, Sanz-Ablanedo, E, Soetanto, R, González-Cruz, M C, Larsen, G D and Cerezo-Narváez, A (2020) Duration and Cost Variability of Construction Activities: An Empirical Study. Journal of Construction Engineering and Management, 146(01).

Davila Delgado, J M, Oyedele, L, Bilal, M, Ajayi, A, Akanbi, L and Akinade, O (2020) Big Data Analytics System for Costing Power Transmission Projects. Journal of Construction Engineering and Management, 146(01).

Deng, H, Hong, H, Luo, D, Deng, Y and Su, C (2020) Automatic Indoor Construction Process Monitoring for Tiles Based on BIM and Computer Vision. Journal of Construction Engineering and Management, 146(01).

El-adaway, I H, Ali, G G, Abotaleb, I S and Barber, H M (2020) Studying the Relationship between Stock Prices of Publicly Traded US Construction Companies and Gross Domestic Product: Preliminary Step toward Construction–Economy Nexus. Journal of Construction Engineering and Management, 146(01).

Elmousalami, H H (2020) Artificial Intelligence and Parametric Construction Cost Estimate Modeling: State-of-the-Art Review. Journal of Construction Engineering and Management, 146(01).

  • Type: Journal Article
  • Keywords: Artificial intelligence; Feature engineering; Ensemble methods; Hybrid intelligent systems; Fuzzy analytic hierarchy process; Genetic algorithm; Factor analysis; Fuzzy logic; XGBoost; Project cost modeling;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001678
  • Abstract:
    This study reviews the common practices and procedures conducted to identify the cost drivers that the past literature has classified into two main categories: qualitative and quantitative procedures. In addition, the study reviews different computational intelligence (CI) techniques and ensemble methods conducted to develop practical cost prediction models. This study discusses the hybridization of these modeling techniques and the future trends for cost model development, limitations, and recommendations. The study focuses on reviewing the most common artificial intelligence (AI) techniques for cost modeling such as fuzzy logic (FL) models, artificial neural networks (ANNs), regression models, case-based reasoning (CBR), hybrid models, diction tree (DT), random forest (RF), supportive vector machine (SVM), AdaBoost, scalable boosting trees (XGBoost), and evolutionary computing (EC) such as genetic algorithm (GA). Moreover, this paper provides the comprehensive knowledge needed to develop a reliable parametric cost model at the conceptual stage of the project. Additionally, field canals improvement projects (FCIPs) are used as an actual case study to analyze the performance of the ML models. Out of 20 AI techniques, the results showed that the most accurate and suitable method is XGBoost with 9.091% and 0.929 based on mean absolute percentage error (MAPE) and adjusted R2, respectively. Nonlinear adaptability, handling missing values and outliers, model interpretation, and uncertainty are discussed for the 20 developed AI models. In addition, this study presents a publicly open data set for FCIPs to be used for future model validation and analysis.

Gondia, A, Siam, A, El-Dakhakhni, W and Nassar, A H (2020) Machine Learning Algorithms for Construction Projects Delay Risk Prediction. Journal of Construction Engineering and Management, 146(01).

Halabya, A and El-Rayes, K (2020) Optimizing the Planning of Pedestrian Facilities Upgrade Projects to Maximize Accessibility for People with Disabilities. Journal of Construction Engineering and Management, 146(01).

He, C, McCabe, B, Jia, G and Sun, J (2020) Effects of Safety Climate and Safety Behavior on Safety Outcomes between Supervisors and Construction Workers. Journal of Construction Engineering and Management, 146(01).

Li, Y, Cao, L, Han, Y and Wei, J (2020) Development of a Conceptual Benchmarking Framework for Healthcare Facilities Management: Case Study of Shanghai Municipal Hospitals. Journal of Construction Engineering and Management, 146(01).

Maqsoom, A, Wazir, S J, Choudhry, R M, Thaheem, M J and Zahoor, H (2020) Influence of Perceived Fairness on Contractors’ Potential to Dispute: Moderating Effect of Engineering Ethics. Journal of Construction Engineering and Management, 146(01).

Newaz, M T, Davis, P, Jefferies, M and Pillay, M (2020) Examining the Psychological Contract as Mediator between the Safety Behavior of Supervisors and Workers on Construction Sites. Journal of Construction Engineering and Management, 146(01).

Pereira, E, Ali, M, Wu, L and Abourizk, S (2020) Distributed Simulation–Based Analytics Approach for Enhancing Safety Management Systems in Industrial Construction. Journal of Construction Engineering and Management, 146(01).

Signor, R, Love, P E D, Belarmino, A T N and Alfred Olatunji, O (2020) Detection of Collusive Tenders in Infrastructure Projects: Learning from Operation Car Wash. Journal of Construction Engineering and Management, 146(01).

Tawalare, A, Laishram, B and Thottathil, F (2020) Relational Partnership in Public Construction Organizations: Front-Line Employee Perspective. Journal of Construction Engineering and Management, 146(01).

Yuan, H and Yang, Y (2020) BIM Adoption under Government Subsidy: Technology Diffusion Perspective. Journal of Construction Engineering and Management, 146(01).