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Chiang, Y and Cheng, E W L (2009) Perception of Financial Institutions toward Financing PFI Projects in Hong Kong. Journal of Construction Engineering and Management, 135(09), 833–40.
Chou, C, Caldas, C H, O’Connor, J T, Sroka, A W and Goldman, G K (2009) Identification of Decision Drivers for the Strategy of Incorporating Utility Relocations into Highway Construction Contracts. Journal of Construction Engineering and Management, 135(09), 812–8.
Choudhry, R M, Fang, D and Lingard, H (2009) Measuring Safety Climate of a Construction Company. Journal of Construction Engineering and Management, 135(09), 890–9.
De Marco, A, Briccarello, D and Rafele, C (2009) Cost and Schedule Monitoring of Industrial Building Projects: Case Study. Journal of Construction Engineering and Management, 135(09), 853–62.
Dhakal, S, Mrawira, D and Rankin, J (2009) Effect of Specifications Type on the Quality of Paving Contracts in New Brunswick. Journal of Construction Engineering and Management, 135(09), 801–11.
El-adaway, I H and Kandil, A A (2009) Contractors’ Claims Insurance: A Risk Retention Approach. Journal of Construction Engineering and Management, 135(09), 819–25.
Golden, S K and Skibniewski, M J (2009) Immigration and Construction: The Makeup of the Workforce in the Washington, D.C., Metropolitan Area. Journal of Construction Engineering and Management, 135(09), 874–80.
Hartmann, A, Ling, F Y Y and Tan, J S H (2009) Relative Importance of Subcontractor Selection Criteria: Evidence from Singapore. Journal of Construction Engineering and Management, 135(09), 826–32.
Kiziltas, S and Akinci, B (2009) Contextual Information Requirements of Cost Estimators from Past Construction Projects. Journal of Construction Engineering and Management, 135(09), 841–52.
Le, T, Caldas, C H, Gibson, G E and Thole, M (2009) Assessing Scope and Managing Risk in the Highway Project Development Process. Journal of Construction Engineering and Management, 135(09), 900–10.
Lu, M and Lam, H (2009) Transform Schemes Applied on Non-Finish-to-Start Logical Relationships in Project Network Diagrams. Journal of Construction Engineering and Management, 135(09), 863–73.
Minchin, R E (2009) Fall and Rise of the Largest Construction Manager-at-Risk Transportation Construction Project Ever. Journal of Construction Engineering and Management, 135(09), 930–8.
Mitropoulos, P, Cupido, G and Namboodiri, M (2009) Cognitive Approach to Construction Safety: Task Demand-Capability Model. Journal of Construction Engineering and Management, 135(09), 881–9.
Odeh, I, El-Rayes, K and Liu, L (2009) Field Experiments to Evaluate and Control Light Tower Glare in Nighttime Work Zones. Journal of Construction Engineering and Management, 135(09), 911–9.
Poveda, C A and Fayek, A R (2009) Predicting and Evaluating Construction Trades Foremen Performance: Fuzzy Logic Approach. Journal of Construction Engineering and Management, 135(09), 920–9.
- Type: Journal Article
- Keywords: Construction management; Performance characteristics; Labor; Supervision; Fuzzy sets; Expert systems;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000061
- Abstract:
This paper illustrates a fuzzy logic model for use in predicting and evaluating the performance of construction trades foremen. The model assists in measuring the effectiveness of a foreman, monitoring improvements in effectiveness over time, and identifying areas where a foreman requires training or mentoring to improve his/her performance. This paper also discusses the factors that affect the performance of a foreman in each area of responsibility. The structure of the model and the use of fuzzy logic are described. The model is validated using data collected from an actual construction company, illustrating its high level of linguistic accuracy. This model is relevant to researchers and makes a contribution to performance evaluation by developing a methodology for evaluating and predicting the performance of construction trades foremen. The model provides a complete approach for handling uncertainty inherent in performance evaluation by using fuzzy logic. The use of fuzzy logic in the model allows users to express themselves linguistically and to make assessments that are subjective in nature. It is relevant to construction industry practitioners since it provides them with a useful technique for evaluating the performance of foremen and identifying the factors that affect their performance on a daily basis. Last, the model offers the advantage of benchmarking foreman performance, allowing organizations to develop plans to improve the performance of their foremen over time.