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Predicting Construction Labor Productivity Using Artificial Neural Network

Muqeem, Sana and Idrus, Arazi and Khamidi, M. Faris (2012) Predicting Construction Labor Productivity Using Artificial Neural Network. In: International Conference on Civil, Offshore and Environmental Engineering (ICCOEE 2012), 12-14 June 2012, Kuala Lumpur, MALAYSIA.

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Abstract

Construction labor productivity is declining continuously all over the world due to the absence of systematic data on production rates and; their influential factors and inadequate prediction techniques. Artificial Neural Network (ANN) has found to have dynamic learning and recognition capabilities to effectively predict the labor productivity in the field of construction management. Therefore, in this study ANN model has been developed for predicting the labor productivity focussing on concreting of floor beams. Selection of relevant influential factors is being carried out through questionnaire survey. The selected factors include rainfall, availability of material, maintenance of equipments, location of project, working space, workforce skill, level of communication, number of workers, quantity of concrete, and floor height. Data on production rates and selected influential factors have been collected from forty one projects from another questionnaire survey. Performance of the model has been determined through calculating the Mean Square Error (MSE). Later, the performance of ANN model was compared with the result of Multiple Linear Regressions (MLR). Since it has been found that ANN predicted the rates more efficiently with least MSE as compare to MLR. Hence, the influence of rainfall, availability of material, maintenance of equipments, location of project, working space, workforce skill, level of communication, number of workers, quantity of concrete, and floor height has been effectively incorporated in the production rates of concreting of floor beams by ANN. For future projects, production rates for concreting of floor beams can be effectively predicted by ANN in the presence of these influential factors.

Item Type:Conference or Workshop Item (Paper)
Subjects:T Technology > TA Engineering (General). Civil engineering (General)
Departments / MOR / COE:Departments > Civil Engineering
ID Code:8179
Deposited By: Dr M Faris Khamidi
Deposited On:18 Sep 2012 01:31
Last Modified:19 Jan 2017 08:21

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