Construction labor productivity modeling with neural networks

1998-11-01
Sönmez, Rifat
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Construction labor productivity is affected by several factors. Modeling of construction labor productivity could be challenging when effects of multiple factors are considered simultaneously. In this paper a methodology based on the regression and neural network modeling techniques is presented for quantitative evaluation of the impact of multiple factors on productivity. The methodology is applied to develop productivity models for concrete pouring, formwork, and concrete finishing tasks, using data compiled from eight building projects. The predictive behaviors of the models are compared with the previous productivity studies. Model results, advantages of the methodology, and study limitations are discussed.
Strategy and Management, Industrial relations, Civil and Structural Engineering, Building and Construction
https://hdl.handle.net/11511/56297
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT-ASCE
Department of Civil Engineering, Article

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Citation Formats
R. Sönmez, “Construction labor productivity modeling with neural networks,” JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT-ASCE, pp. 498–504, 1998, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56297.
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