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Albert, A, Hallowell, M R and Kleiner, B M (2014) Enhancing Construction Hazard Recognition and Communication with Energy-Based Cognitive Mnemonics and Safety Meeting Maturity Model: Multiple Baseline Study. Journal of Construction Engineering and Management, 140(02).
Arashpour, M, Wakefield, R, Blismas, N and Lee, E W M (2014) Analysis of Disruptions Caused by Construction Field Rework on Productivity in Residential Projects. Journal of Construction Engineering and Management, 140(02).
Brockman, J L (2014) Interpersonal Conflict in Construction: Cost, Cause, and Consequence. Journal of Construction Engineering and Management, 140(02).
Chen, Z, Abdullah, A B, Anumba, C J and Li, H (2014) ANP Experiment for Demolition Plan Evaluation. Journal of Construction Engineering and Management, 140(02).
Chong, D, Wang, Y, Guo, H and Lu, Y (2014) Volatile Organic Compounds Generated in Asphalt Pavement Construction and Their Health Effects on Workers. Journal of Construction Engineering and Management, 140(02).
Dehghan, R and Ruwnapura, J Y (2014) Model of Trade-Off between Overlapping and Rework of Design Activities. Journal of Construction Engineering and Management, 140(02).
Deng, F and Smyth, H (2014) Nature of Firm Performance in Construction. Journal of Construction Engineering and Management, 140(02).
Jafarzadeh, R, Ingham, J M, Wilkinson, S, González, V and Aghakouchak, A A (2014) Application of Artificial Neural Network Methodology for Predicting Seismic Retrofit Construction Costs. Journal of Construction Engineering and Management, 140(02).
Kasapoğlu, E (2014) Leadership Styles in Architectural Design Offices in Turkey. Journal of Construction Engineering and Management, 140(02).
Khalili, A and Chua, D K (2014) Integrated Prefabrication Configuration and Component Grouping for Resource Optimization of Precast Production. Journal of Construction Engineering and Management, 140(02).
Lopez del Puerto, C, Clevenger, C M, Boremann, K and Gilkey, D P (2014) Exploratory Study to Identify Perceptions of Safety and Risk among Residential Latino Construction Workers as Distinct from Commercial and Heavy Civil Construction Workers. Journal of Construction Engineering and Management, 140(02).
Lu, W, Ye, K, Flanagan, R and Jewell, C (2014) Nexus between Contracting and Construction Professional Service Businesses: Empirical Evidence from International Market. Journal of Construction Engineering and Management, 140(02).
Martin, H and Lewis, T M (2014) Pinpointing Safety Leadership Factors for Safe Construction Sites in Trinidad and Tobago. Journal of Construction Engineering and Management, 140(02).
Rosenbaum, S, Toledo, M and González, V (2014) Improving Environmental and Production Performance in Construction Projects Using Value-Stream Mapping: Case Study. Journal of Construction Engineering and Management, 140(02).
Stamatiadis, N, Goodrum, P, Shocklee, E and Wang, C (2014) Quantitative Analysis of State Transportation Agency’s Experience with Constructability Reviews. Journal of Construction Engineering and Management, 140(02).
Zhao, T and Dungan, J M (2014) Improved Baseline Method to Calculate Lost Construction Productivity. Journal of Construction Engineering and Management, 140(02).
- Type: Journal Article
- Keywords: Claims; Productivity; Project management; Quantitative analysis; Construction management; Claims; Productivity; Project management; Quantitative analysis; Quantitative methods;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000800
- Abstract:
The measured mile/baseline method has been widely accepted to quantify labor productivity loss, which is demonstrated by comparing the impacted and unimpacted/lightly impacted portions of the work. Although the distinctions between these portions of a project can at times be identified through a cause and effect analysis, on many projects this distinction is not readily observable. For those projects, researchers and professionals have developed various procedures to implement the measured mile/baseline calculations, but shortcomings in those procedures can result in the failure to objectively identify the baseline. In this paper, a method based on basic statistical techniques is proposed to determine a baseline that represents the contractor’s normal operating performance, thus overcoming many of weaknesses in the existing methods. This paper will provide construction professionals and engineers with an objective approach to determine the productivity baseline, thus aiding in the resolution of labor productivity loss claims. Further, this new method avoids the arbitrary baseline sample size and the possibility of multiple competing solutions in existing methods. A numerical example is included to compare the results using different methods and demonstrate the advantages of the proposed method.