Abstract:

: Surface damage detection, geometry measurement and monitoring are important for assessing the condition and risk of concrete structures. Therefore, to effectively assess the damage to a concrete structure, a 3D laser scanner accurately estimates the damage within a short timeframe and with less cost than the traditional inspection approaches. This study presents a framework for automated surface damage detection and structural health monitoring of a concrete structure using a X7 laser scanner (Trimble, Westminster, CO, USA). The methodology includes the use of 3D laser scanning technology to capture the 3D geometry of the concrete structure, followed by a detailed analysis of the data to identify any areas of damage or crack. The isodata and object-based image analysis (OBIA) techniques were applied to a 2D image generated from 3D cloud points. Overall accuracy (>89.6) and kappa statistics (>0.83) of both classification techniques exhibit good agreement between the classified and reference image. The OBIA technique was shown to be more effective in detecting minor cracks (<5 mm) and damage on a concrete structure. It was observed that the proposed approach is effective at identifying and monitoring the structural health of a concrete structure. The ability to continuously monitor the structure in this manner allows for early detection of damage and can aid in the maintenance and repair of the structure. Furthermore, this approach can robustly perform structural health monitoring and damage estimation.

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