Comprehensive Analysis of Structural Defects in Various Structures Using TLS Data and Machine Learning

Structural integrity is the foundation for both safety and durability and demands constant care and vigilance. In lieu of the traditionally adopted methods for defect detection in manufacturing and structural deformation, advanced techniques with automated methodology have been presented in this paper for identification and analysis with the help of Terrestrial Laser Scanning (TLS) data. This methodology will be flexible, able to treat different types of geometrical structures; thus, it can be found in real-life applications by increasing the scope. It involves capturing high-resolution TLS with fine surface details of the structure. Advanced algorithms fit idealized geometric models-cylinders or planes-such that it matches the data to those that have been scanned. Highly accurate virtual models quantify deviations between the actual structure and the virtual one to identify areas where defects in manufacturing or deformations exceed thresholds set in advance. The nature and extent of these defects are analyzed and visualized clearly across the structure's height. This reduces the actual time spent on structural analysis drastically, as automation increases the speed with great improvement in accuracy. Our results prove the efficiency of this approach in finding even slight deformations-a reliable tool for structural health monitoring. This methodology provides enormous opportunities for engineers and inspectors in charge of maintaining the safety and integrity of new and existing structures.