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Candaş, A B and Tokdemir, O B (2022) Automated Identification of Vagueness in the FIDIC Silver Book Conditions of Contract. Journal of Construction Engineering and Management, 148(04).

Guevara, J, Herrera, L and Salazar, J (2022) Interorganizational Sponsor Networks in Road and Social Infrastructure PPP Equity Markets. Journal of Construction Engineering and Management, 148(04).

Han, S, Jiang, Y and Bai, Y (2022) Fast-PGMED: Fast and Dense Elevation Determination for Earthwork Using Drone and Deep Learning. Journal of Construction Engineering and Management, 148(04).

Hosseinian, S M, Arjomand, A, Li, C Q and Zhang, G (2022) Developing a Model for Assessing Project Completion Time Reliability during Construction Using Time-Dependent Reliability Theory. Journal of Construction Engineering and Management, 148(04).

Liu, Q, Ye, G, Yang, J, Xiang, Q and Liu, Q (2022) Construction Workers’ Representativeness Heuristic in Decision Making: The Impact of Demographic Factors. Journal of Construction Engineering and Management, 148(04).

Ma, L and Fu, H (2022) A Governance Framework for the Sustainable Delivery of Megaprojects: The Interaction of Megaproject Citizenship Behavior and Contracts. Journal of Construction Engineering and Management, 148(04).

Tiruneh, G G and Fayek, A R (2022) Hybrid GA-MANFIS Model for Organizational Competencies and Performance in Construction. Journal of Construction Engineering and Management, 148(04).

  • Type: Journal Article
  • Keywords: Artificial intelligence; Construction; Hybrid neuro-fuzzy systems; Organizational issues; Organizational competency; Performance;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0002250
  • Abstract:
    The majority of competency and performance modeling methods available in the literature are deterministic conceptual, statistical, and/or regression models that cannot capture the subjective uncertainty, complex, and nonlinear relationships inherent in construction, which makes accurate prediction difficult. Past studies utilized neuro-fuzzy system (NFS) models, such as adaptive neuro-fuzzy inference system (ANFIS), that combine the learning power of artificial neural networks and functionality of fuzzy systems to develop accurate predictive models. ANFIS is robust, fast, and effective in solving complex problems for a range of real-world construction engineering and management (CEM) applications. NFS models such as ANFIS have some limitations in handling multiple outputs common in construction industry problems, such as being prone to early convergence due to local minima entrapment. To address these limitations, this paper proposes a hybrid NFS combining the evolutionary optimization technique of a genetic algorithm (GA) with a multi-output adaptive neuro-fuzzy inference system (MANFIS) that can handle multi-input multi-output (MIMO) problems for CEM applications. The proposed modeling approach is demonstrated using a case study that showed good results in predicting multiple organizational performance metrics using organizational competencies. The contributions of this paper are threefold: It (1) proposes a novel methodology of integrating different computing techniques for developing a GA-based multi-output adaptive neuro-fuzzy inference system (GA-MANFIS) model that can handle complex and nonlinear MIMO problems inherent in construction processes and practices; (2) relates organizational competencies to performance and predicts multiple organizational performance metrics; and (3) provides a GA-based feature selection approach that reduces data dimensionality, enabling identification of organizational competencies that significantly influence organizational performance. By uniquely integrating these techniques, this model enables construction organizations to evaluate their competencies and predict multiple organizational performance metrics simultaneously, and researchers can adapt it for a variety of construction contexts.

Wang, P, Wang, K, Huang, Y, Fenn, P and Stewart, I (2022) Auditing Construction Cost from an In-Process Perspective Based on a Bayesian Predictive Model. Journal of Construction Engineering and Management, 148(04).

Xie, H, Hong, Y and Brilakis, I (2022) Analysis of User Needs in Time-Related Risk Management for Holistic Project Understanding. Journal of Construction Engineering and Management, 148(04).

Zhang, X and Liu, J (2022) Incentive Mechanism and Value-Added in PPP Projects Considering Financial Institutions’ Early Intervention. Journal of Construction Engineering and Management, 148(04).

Zhu, H, Hwang, B, Ngo, J and Tan, J P S (2022) Applications of Smart Technologies in Construction Project Management. Journal of Construction Engineering and Management, 148(04).