Neural Networks for Estimating the Productivity of Concreting Activities

Abstract

To overcome the variability and the impact of subjective factors on the cost of concrete-related activities in developing countries, neural networks can offer a guiding tool. In this study, three neural networks were developed to estimate the productivity, within a developing market, for formwork assembly, steel fixing, and concrete pouring activities. Eighteen experts working in six projects were carefully selected to gather the data for the neural networks. Ninety-two data surveys were obtained and processed for use by the neural networks. Commercial software was used to perform the neural network calculations. The processed data were used to develop, train, and test the neural networks. The results of the developed framework of neural networks indicate adequate convergence and relatively strong generalization capabilities. When used to perform a sensitivity analysis on the input factors influencing the productivity of concreting activities, the framework has demonstrated a good potential in identifying trends of such factors.