Machine learning-integrated 5D BIM informatics: building materials costs data classification and prototype development

Abstract

Non-informatics cost estimation is a tedious process and requires substantial amount of time and manual operations. However, BIM adoption approaches have attracted significant attention with this respect. Since BIM models are object-based with built-in parametric information, it is easier to capture the quantities of building elements and deliver more accurate estimates with less errors and omissions. As most of the current cost estimation standards are designed and developed based on old-fashioned construction project delivery systems, a lack of compatibility between their classification and BIM-based informatics is observed. This study, therefore, aims to develop an informatics framework to integrate a cost estimation standard with BIM in order to expedite the 5D BIM process and enhance the digital transformation practices in construction projects. The developed framework is considered to be a new approach which can automatically estimate the cost of building elements using machine learning-integrated algorithms and MATLAB engine for its effective implementation.