MULTI-SENSORY DATA FUSION FOR MÉTRO INSPECTION

本研究项目的主要目的是探讨利用遥感设备为蒙特利尔运输公司（STM）检测地铁混凝土结构的可行性。为了实现这一目标，将探索多传感器数据源。具体而言，从地质雷达系统的剖面进行分析，以获得结构内部恶化。同时，对热像和彩色图像进行处理，检测结构外观劣化。将内部和外部劣化条件投影到激光扫描的3D点云上，以产生具有详细和准确劣化信息的三维（3D）结构状态图。这样，STM工程师就可以依靠这些状态图来制定适当的维护策略。本文提出的方法将在Matlab中实现，并在蒙特利尔市的地铁混凝土结构检测现场试验中进行评估。该研究预计将减少目前手动检查地铁混凝土结构过程中所需的时间，成本和精力。因此，地铁工程师可以做出明智和明智的维护决策，以保持地铁网络健康地服务于公众，在它们出现结构缺陷或功能过时之前。

The main objective of this research project is to investigate the feasibility of utilizing remote sensingequipment to facilitate the inspection of Métro concrete structures for Société de Transport de Montréal (STM).In order to achieve this objective, multi-sensory data sources will be explored. Specifically, the profiles fromthe ground penetrating radar system are analyzed to obtain the structure interior deterioration. Also, the thermaland color images are processed to detect the structure exterior deterioration. Both interior and exteriordeterioration conditions are projected to the 3D point cloud from laser scanning to produce a three-dimensional(3D) structure condition map with the detailed and accurate deterioration information. This way, the STMengineers could rely on such condition maps to make an appropriate maintenance strategy. The proposedmethod in this research project will be implemented in Matlab and evaluated in the field trials of inspectingMétro concrete structures in the city of Montreal. The research is expected to reduce the time, cost, and effortrequired in the current process of manually inspecting Métro concrete structures. As a result, the Métroengineers could make wise and informed maintenance decisions to keep the Métro network healthy in serviceto the public, before they are structurally deficient or functionally obsolete.