Prediction Modeling of Construction Labor Production Rates Using ANN.

• Main Authors: Muqeem, Sana, Idrus, Arazi, Zakaria, Saiful, Khamidi, Mohd Faris
• Format: Conference or Workshop Item
• Published: 2011
• Subjects: TH Building construction; TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.utp.edu.my/4983/1/rp009_vol.2-F10086.pdf; http://www.icest.org/; http://eprints.utp.edu.my/4983/
• Institution: Universiti Teknologi Petronas

Construction productivity is the main indicator of the performance of construction industry. It is constantly declining over a decade due to the lack of standard productivity measurement system. The impact of the various factors influencing labor productivity is also neglected. Various labor productivity models developed have not been implemented successfully due to the availability of unreliable data. Also influencing factors which are subjective such as weather, site conditions etc are usually ignored by the estimators. Although there are various modeling techniques developed for predicting production rates for labor that incorporate the influence of various factors but neural networks are found to have strong pattern recognition and learning capabilities to get reliable estimates. Therefore the objective of this research study is to develop a neural network prediction model for estimating labor production rates. The developed model has also taken into account the subjective factors. Production rates data for concreting of columns of different high rise concrete building structures has been obtained through direct observation method.