IFC-based semantic modeling of damaged RC beams using 3D point clouds

It is important to assess existing reinforced concrete (RC) structures with damages. This task, however, is traditionally empirical and time-consuming. To improve assessment accuracy and efficiency, this study proposed an approach to semantic model damaged RC beams based on point clouds. A slice-based method for automatically modeling deformed beams and a color-based crack detection method were developed for generating building information modeling (BIM) and finite element (FE) models. Furthermore, to enhance structural assessment and decision-making by providing both damage and loading performance analysis data, a framework to extend the existing IFC standard was proposed for interoperability issues between FE models and BIM, aiming to integrate semantic-enrichment damages and FE analysis results in the as-is BIM model. Experiments were carried out on a simply supported RC beam subjected to a concentrated load. As a result, the improvement of the developed as-is BIM model for damage visualization and structural assessment was confirmed.

Automated summary

This study proposed an approach to semantic model damaged RC beams based on point clouds, aiming to improve assessment accuracy and efficiency. They developed methods for automatically modeling deformed beams and detecting cracks, generating BIM and FE models. They also proposed a framework to integrate damage and loading performance analysis data into the as-is BIM model. The experiments confirmed the improvement of the developed BIM model for damage visualization and structural assessment.