eprintid: 2071
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/20/71
datestamp: 2023-11-09 15:50:15
lastmod: 2023-11-09 15:50:15
status_changed: 2023-11-09 15:41:57
type: article
metadata_visibility: show
creators_name: Muqeem, S.
creators_name: Idrus, A.
creators_name: Khamidi, M.F.
creators_name: Bin Ahmad, J.
creators_name: Bin Zakaria, S.
title: Construction labor production rates modeling using artificial neural network
ispublished: pub
keywords: Artificial Neural Network; Average values; Concrete building structures; Construction productivity; Formwork; High rise; Influencing factor; Labor productivity; Learning capabilities; Modeling technique; Neural network prediction model; Prediction model; Production rates; Site conditions; Work sampling, Concrete construction; Database systems; Engineering research; Forecasting; Mathematical models; Mean square error; Pattern recognition; Productivity; Sampling, Neural networks
note: cited By 13
abstract: Construction productivity is constantly declining over a decade due to the lack of standard productivity database system and the ignorance of impact of various factors influencing labor productivity. Prediction models developed earlier usually neglect the influencing factors which are subjective in nature such as weather, site conditions etc. Many modeling techniques have been developed for predicting production rates for labor that incorporate the influence of various factors but artificial neural network (ANN) has been found to have strong pattern recognition and learning capabilities to get reliable results. Therefore the objective of this research is to develop a neural network prediction model for predicting labor production rates that takes into account the factors which are in qualitative form. The objectives of the research have been achieved by collecting production rates data for formwork of beams from different high rise concrete building structures by direct observation. Reliable values of production rates have been successfully predicted by ANN. The average value of 1.45xE-04 has been obtained for Mean Square Error (MSE) after testing the network. These results indicate that the ANN has predicted production rates values for beam formwork successfully with least range of errors. © 2011 The authors.
date: 2011
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960088741&partnerID=40&md5=fd4ca9d16fdc214196ff5a5f9668b667
full_text_status: none
publication: Electronic Journal of Information Technology in Construction
volume: 16
pagerange: 713-726
refereed: TRUE
issn: 14036835
citation:   Muqeem, S. and Idrus, A. and Khamidi, M.F. and Bin Ahmad, J. and Bin Zakaria, S.  (2011) Construction labor production rates modeling using artificial neural network.  Electronic Journal of Information Technology in Construction, 16.  pp. 713-726.  ISSN 14036835