Adaptive predictive neural network for brickwall production rates

Abstract

The productivity of brickwork is an important factor in the construction industry, as it directly affects project timelines and costs. In this study, we investigated the relationship between the production rate of brickwork and various factors such as wall height, length, crew configuration and crew rates and area, wall length, height, area built, and the number of masons were the most influential factors. The data was collected from 30 different construction sites, and the production rate was measured in terms of square meters of wall constructed and also the square area of walls plastered per day. We used a Weibull distribution to model the data, as it is well-suited for handling censored data. The results showed a strong positive linear relationship between the production rate and wall height, length, and area, indicating that an increase in these factors leads to an increase in productivity. Furthermore, we developed an artificial neural network (ANN) model to predict the production rate of brickwork based on these factors, the R-squared value for training, validation, testing, and overall data instance mapping was 86.8%, 54.7%, 79.5%, and 84.9% respectively. The model architecture was composed of a neuron-hidden layer combination of 4 hidden layers and 9 neurons with an MSE of 0.053 signifying a 95% model accuracy The findings of this study can be used to improve the productivity of brickwork in construction projects and inform decision-making processes.