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Alanjari, P, RazaviAlavi, S and AbouRizk, S (2015) Hybrid Genetic Algorithm-Simulation Optimization Method for Proactively Planning Layout of Material Yard Laydown. Journal of Construction Engineering and Management, 141(10).
de Athayde Prata, B, Pitombeira-Neto, A R and de Moraes Sales, C J (2015) An Integer Linear Programming Model for the Multiperiod Production Planning of Precast Concrete Beams. Journal of Construction Engineering and Management, 141(10).
de Oliveira, A L and Prudêncio, L R (2015) Evaluation of the Superficial Texture of Concrete Pavers Using Digital Image Processing. Journal of Construction Engineering and Management, 141(10).
Heravi, G and Eslamdoost, E (2015) Applying Artificial Neural Networks for Measuring and Predicting Construction-Labor Productivity. Journal of Construction Engineering and Management, 141(10).
- Type: Journal Article
- Keywords: Labor productivity; Power plant construction projects; Artificial neural networks; Early stopping; Bayesian regularization; Developing countries; Quantitative methods;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001006
- Abstract:
Variations in labor productivity are the result of multiple influential factors. This paper attempts to develop a labor productivity model based on multilayer feedforward neural networks trained with a backpropagation algorithm by which complex mapping of factors to labor productivity is performed. To prevent networks from overfitting and improve their generalization, early stopping and Bayesian regularization are implemented and compared. The results proved a better prediction performance for Bayesian regularization than early stopping. To demonstrate the prediction performance of the presented models, the developed models are implemented at two real power plant construction projects. Moreover, in order to extract the influence rate of each factor on the predictive behavior of the neural network models and to identify the most influential factors a sensitivity analysis is conducted. This paper focuses on the work involved in installing the concrete foundations of gas, steam, and combined cycle power plant construction projects in the developing country of Iran. This study contributes to the construction project management body of knowledge by investigating the influential factors on labor productivity and developing an artificial neural network to measure and predict labor productivity in developing countries using the Bayesian regularization and early stopping methods. This approach provides insight into better ways of modeling labor productivity.
Ma, G, Gu, L and Li, N (2015) Scenario-Based Proactive Robust Optimization for Critical-Chain Project Scheduling. Journal of Construction Engineering and Management, 141(10).
Shealy, T and Klotz, L (2015) Well-Endowed Rating Systems: How Modified Defaults Can Lead to More Sustainable Performance. Journal of Construction Engineering and Management, 141(10).
Song, J, Song, D and Zhang, D (2015) Modeling the Concession Period and Subsidy for BOT Waste-to-Energy Incineration Projects. Journal of Construction Engineering and Management, 141(10).