Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/1018
Title: Precast Concrete Installation: Automating the Process of Assessing and Controlling Labor Productivity
Authors: Mohammad D. Almutawa 
Supervisor: Dr. Ahmed Khalafallah
Degree Awarded: master degree in civil engineering
Keywords: Construction labor productivity, precast concrete construction, estimating, jobsite observations, automated system, relational database
Issue Date: 2019
Publisher:  Kuwait university - college of graduate studies
Abstract: Several construction projects suffer from substantial delays and cost overruns due to lower-than-expected labor productivity estimates. The problem is exacerbated by the marginal employment of information technology (IT) in the construction industry, leaving it trailing behind other industries in IT development and utilization. Although there have been many previous research studies on construction labor productivity (CLP), comparatively limited attention has been given to adequately investigate the discrepancy between actual and theoretical CLP, especially in precast concrete installation which is gradually spreading throughout the construction industry. This study seeks to fill these knowledge gaps through: (a) identifying the key factors that affect precast concrete CLP; (b) investigating the discrepancies between existing model estimates and actual CLP in precast concrete erection; and (c) developing an automated system for the assessment and control of CLP discrepancy. The system is envisioned to expedite the assessment of CLP discrepancy in order to support near real-time control. A comparative analysis of data estimates of existing models and corresponding actual CLP data is presented to illustrate the validity of the approach and the viability of the developed system. Three models have been used for estimating the productivity rates of specific structural and architectural precast concrete tasks. After conducting adequate jobsite observations among 10 building projects, the estimated values have been compared with the corresponding actual rates through calculating three relevant accuracy measures that indicated significant discrepancies. For the structural precast concrete tasks, the productivity estimates of Model 2 were found to be more accurate than those of Model 1 and Model 3. By contrast, Model 3 yielded slightly more accurate estimates than the other two models in case of the architectural precast concrete operations. As such, the study presents a methodological approach that can be used by contractors to effectively control productivity-related cost and schedule overruns in precast concrete construction.
URI: http://hdl.handle.net/123456789/1018
Appears in Programs:0620 Civil Engineering

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