How to predict the rebar labours' production rate by using ANN model

Production rates are considered as an essential aspect of construction industry because they are the indicators of the productivity efficiency of construction sector. However, there is a gap in identifying factors affecting rebar workers and their production rate. This study tries to bridge this gap by developing a neural network model for estimating rebar labour’s production rates. A questionnaire has been distributed and statistical software program was used to analyze the collected data. Two methods were used for analysis. The first depends on relative importance index, whereas the second depends on the probability and the impact. The results indicate that 'project type' is the most important factor affecting the productivity. A model was estimated to predict rebar labours' productivity. Reliable values have been successfully predicted by artificial neural network. This article presents a software program, which is used to measure the production rate (Output) based on the data provided in the form of factors affecting the rebar labour (Input). This helps to measure productivity growth in a low-cost residential building. In addition, it supports fundamentals building by predicting the production rate of rebar labour, to establish a database for executed projects in the future to develop productivity estimation process.