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Al Haj, R A and El-Sayegh, S M (2015) Time–Cost Optimization Model Considering Float-Consumption Impact. Journal of Construction Engineering and Management, 141(05).
Bijleveld, F R, Miller, S R and Dorée, A G (2015) Making Operational Strategies of Asphalt Teams Explicit to Reduce Process Variability. Journal of Construction Engineering and Management, 141(05).
Che Ibrahim, C K I, Costello, S B and Wilkinson, S (2015) A Fuzzy Approach to Developing Scales for Performance Levels of Alliance Team Integration Assessment. Journal of Construction Engineering and Management, 141(05).
Fitch, G J, Odeh, I and William Ibbs, C (2015) Economic Sustainability of DBO Water Based on Wastewater Projects in the U.S.: Three Case Studies. Journal of Construction Engineering and Management, 141(05).
Jablonowski, C J (2015) Quantitative Method to Model the Underreporting of Safety Incidents. Journal of Construction Engineering and Management, 141(05).
Jafarzadeh, R, Ingham, J M, Walsh, K Q, Hassani, N and Ghodrati Amiri, G R (2015) Using Statistical Regression Analysis to Establish Construction Cost Models for Seismic Retrofit of Confined Masonry Buildings. Journal of Construction Engineering and Management, 141(05).
Lin, S (2015) An Analysis for Construction Engineering Networks. Journal of Construction Engineering and Management, 141(05).
Rodríguez-Garzón, I, Lucas-Ruiz, V, Martínez-Fiestas, M and Delgado-Padial, A (2015) Association between Perceived Risk and Training in the Construction Industry. Journal of Construction Engineering and Management, 141(05).
Shahtaheri, M, Nasir, H and Haas, C T (2015) Setting Baseline Rates for On-Site Work Categories in the Construction Industry. Journal of Construction Engineering and Management, 141(05).
- Type: Journal Article
- Keywords: Activity analysis; Adaptive neurofuzzy interface system (ANFIS); Baseline rate; Labor performance; Direct-work rate; Productivity; Continuous improvement; Construction automation; Artificial intelligence; Project planning and design;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000959
- Abstract:
Labor performance drives construction project performance. Labor performance can be improved by increasing the direct-work rate, which is the time spent by workers on installing materials and equipment. However, setting baseline rates for direct-work rate and determining expectation levels during the construction phase requires further investigation. The focus of the research reported in this paper is to establish a methodology for setting a desirable and realistic baseline rate based on activity analysis, primarily for industrial projects. First, an adaptive neurofuzzy inference system (ANFIS)-based method was developed as a means of estimating baseline rates based on existing knowledge. The method was trained using 272 data points. Its flexibility and functionality validate its usefulness; however, three additional methods of defining baseline rates were also developed based on simpler concepts and demonstrated with data points available from 14 projects, and the experience associated with these projects. As a result, comprehensive methods and a valuable initial dataset for industrial construction projects to better establish baseline rates for direct work and supporting activities were contributed. This should help project managers to estimate appropriate baselines and set realistic goals for direct-work rate which ultimately may lead to improvement of labor performance.
Zhang, P, Lingard, H, Blismas, N, Wakefield, R and Kleiner, B (2015) Work-Health and Safety-Risk Perceptions of Construction-Industry Stakeholders Using Photograph-Based Q Methodology. Journal of Construction Engineering and Management, 141(05).