Machine learning multilayer perceptron method for building information modeling application in engineering performance prediction

Abstract

ABSTRACT The engineering design process has fundamentally impacted the life cycle of construction projects and notably, the engineering performance is significantly measured in delivering projects. Previous studies on engineering performance have established the cause–effect relationships between project variables and performance measures. Recently, the building information modeling (BIM) application has reformed how owners execute the engineering, construction, commissioning, and operation in the industry. There has been an increasing focus in finding the benefits of BIM on project performance, however, a minor focus has been given to engineering performance. This paper proposes an artificial neural network (ANN) machine learning multilayer perceptron (MLMP) method and linear regression (LR) that correlates the use of BIM with engineering performance for better construction project assessment. The conclusions reveal a high-level correlation measure between BIM use inputs and engineering performance outputs and further methods for evaluating the engineering performance. Furthermore, we achieved and validated the best prediction by leveraging data from 60 samples using the MLMP and LR models.