A Computational Procedure for Generating Specimens of BIM and Point Cloud Data for Building Change Detection

Abstract

The potential for automated construction quality inspection, construction progress tracking and post-earthquake damage assessment drives research in interpretation of remote sensing data and compilation of semantic models of buildings in different states. However, research efforts are often hampered by a lack of full-scale datasets. This is particularly the case for earthquake damage assessment research, where acquisition of scans is restricted by scarcity of access to post-earthquake sites. To solve this problem, we have developed a procedure for compiling digital specimens in both pre-and post-event states and for generating synthetic data equivalent to which would result from laser scanning in the field. The procedure is validated by comparing the physical and synthetic scans of a damaged beam. Interpretation of the beam damage from the synthetic data demonstrates the feasibility of using this procedure to replace physical specimens with digital models for experimentation and for other civil engineering applications.

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

“…Although these methods have just recently emerged, they have shown satisfactory results on various tasks related to computer vision and graphics (Yang et al 2018a). Ma et al (2015) and Zeibakshini et al (2015) used as-built BIM and 3D point clouds, to create the time reproduction procedure of damaged components and structures, these works can only reproduce the surface of catastrophe features with the data acquisition time and are not good at including component overlap, out-of-plane deformation, cracks, and catastrophic process. 3D Reconstruction plays an important role in many fields, such as digital entertainment, social media, emotional analysis, and personal identification (Zhang et al 2021).…”

“…Currently, the common approaches for data analysis are gradually transformed into intelligent technologies (Yang et al 2002 ; Jiang 2009 ), and there are many related applications in infrastructure design, disaster prevention, disaster reduction, and recovery (Yang et al 2017 ; Bao and Li 2019 ; Mariappan et al 2015 ). Learning system extended Reality (ER) Internet of Thing (IoT) and other technologies or software have also been introduced to support catastrophe virtual recurrence, which can achieve multidimensional visualization of performance and behavior using the reverse modeling method (Ma et al 2015 ; Lin et al 2020 ; Kawai et al 2016 ). These applications provide a novel perspective for the development of disaster prevention and mitigation for infrastructure (DPMI).…”

A Cloud-Based BIM Platform for Information Collaboration

“…Specifically, we use the example of reconstruction of a reinforced concrete beam model taken from a research effort at the Technion-Israel Institute of Technology to develop a system for reconstruction of BIM models for earthquake damaged buildings [21][22][23][24] using terrestrial laser scanning.…”