PREDICTING CONSTRUCTION CREW PRODUCTIVITY FOR CONCRETE POURING OPERATIONS OF RC COLUMNS USING ANN

Abstract

In current practice, construction crew productivity (CCP) assessment solely relies on traditional methods like personal judgements or past published data. However, a systematic approach is required to measure and predict the CCP. Modelling with artificial neural networks (ANNs) can be useful for examining interrelationships between factors affecting CCP. The present study aims to predict CCP for concrete pouring operation of reinforced concrete (RC) columns using ANN. To achieve this, 14 factors like crew size, average age of crew, average experience of crew, extent of supervision, working hours, lead distance, working height, location of columns, number of concrete bucket carriers, method of concrete supply, temperature, wind speed, column length, and column width were identified from literature study and industry experts. These identified factors were considered as inputs, while CCP was the output variable for ANN model. The study collected 187 samples from 11 residential and commercial construction sites located in four cities of India. A single-layer feed-forward back propagation neural network was used for prediction. The developed model can estimate productivity rates reasonably with least range of errors. Outcomes of study can be useful to improve the crew’s productivity at construction sites for concrete pouring works of RC columns.