Prediction Modeling of Construction Labor Production Rates using Artificial Neural Network

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.