Neuromodex—Neural Network System for Modular Construction Decision Making

Abstract

This paper presents an approach for decision making about construction modularization using neural networks. The model helps make a decision whether to use a conventional “stick‐built” method or to use some degree of modularization when building an industrial process plant. This decision is based on several decision attributes which are divided into following five categories: plant location, environmental and organizational, labor‐related, plant characteristics, and project risks. The neural network is trained using cases collected from several engineering and construction firms and owner firms of industrial process plants. In this paper, an overview of modular construction is provided and the reasons for using a neural network are also discussed. The architecture, representation, and training procedure for the selected neural network paradigms are described. The performance of the trained neural network system is compared with the recommendations provided by human experts. The results of statistical tests performed to validate the system are also presented.