Fuzzy Agent–Based Modeling of Competency and Performance Measures in Construction
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 12
Abstract
Construction organizations are project-based organizations in which both competencies and performance develop through project execution. A competency measure is a combination of knowledge, skill, processes, practices, and/or technology possessed by a construction organization. Performance measures are used to assess an organization’s competitiveness within its industry. An organization can analyze competencies to predict and improve performance. Previous studies investigated project- and organization-level competencies with respect to performance measures. However, multilevel assessment is needed, although it is challenging because the nature of construction competency and performance measures is complex, dynamic, and subjectively uncertain. To address these challenges, this paper presents a methodology for developing a fuzzy agent–based model (FABM) of competency and performance that models a set of organizational- and project-level competency measures as decision-making entities (i.e., agents) and predicts multiple performance measures as their emergent behavior. A case study was implemented to demonstrate, verify, and validate the proposed FABM model, with encouraging results in performance prediction. The academic contribution of this paper is providing a novel systematic, bottom-up modeling approach for measuring and assessing competencies at the project and organization levels and mapping these multilevel competencies to construction performance measures. Furthermore, the outcomes of this study are expected to support construction practitioners by providing a set of comprehensive hierarchical competency and performance metrics at the project and organization levels for managerial actions taken to identify, construct, and develop competency models to assess performance at both levels.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This work was funded by the Natural Sciences and Engineering Research Council of Canada Industrial Research Chair in Strategic Construction Modeling and Delivery (NSERC IRCPJ 428226–15) held by Dr. Aminah Robinson Fayek. The authors gratefully acknowledge the support of Dr. Phuong Nguyen, Dr. Nebiyu Kedir, the IRC’s industry partners, and all personnel who participated in this study.
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Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 149 • Issue 12 • December 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Feb 14, 2023
Accepted: Aug 18, 2023
Published online: Oct 9, 2023
Published in print: Dec 1, 2023
Discussion open until: Mar 9, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Business management
- Case studies
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction management
- Decision making
- Engineering fundamentals
- Fuzzy logic
- Industries
- Management methods
- Methodology (by type)
- Organizations
- Practice and Profession
- Project management
- Quality control
- Research methods (by type)
Authors
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