Leveraging Point Cloud Data for Detecting Building Façade Deteriorations Caused by Neighboring Construction

Abstract

Building facade deterioration due to aging and nearby construction sites is a continuous public safety concern. Within the past half-decade, there have been more than twelve-thousand complaints regarding falling debris from building facades in NYC, where it has around 1 million aging buildings. Thus, an effective method for facade inspection is essential for property owners and inspection agencies. Nowadays, 3D laser scanners are widely utilized for their ability to capture as-is conditions. However, an appropriate scanning setting when facing distinct types of tasks, such as crack detection and progression of damages on building façades is essential to expedite the data collection process. The overarching goal of this research is to compare the datasets captured through different settings of laser scanners (i.e., resolution and scanning distance) for crack detection and to evaluate the measurement accuracy of the detected/progressed cracks. In this study, we report back on the analysis performed on point clouds obtained with terrestrial laser scanners for crack detection. The results provide a performance analysis of terrestrial scanning systems and corresponding settings for crack detection. Findings can provide an alternative way to comply with the laws (e.g., Local Law 11/98 in NYC) that require periodical evaluations of buildings in cities.