The leaks in roofs and cracks in walls of buildings are common and need immediate attention. The roof leaks or cracks lead to water seepage resulting in structural damage to the ceiling wall. In this work, the roof leaks or cracks are identified using the proposed thermal sensor-based decision support system. Further, the thermal camera is interfaced with a handy single on-board computer. The supervised machine learning algorithm is coded inside the single on-board computer and the thermal images captured using the thermal camera is utilized for the fault identification. Further, the trained network is tested using a new set of thermal images for identification of faults. Results demonstrate that the proposed system is efficient in locating and identification of faults. Since the single on-board has an inbuilt Wi-Fi, the decision support can be stored in the cloud server with a specific unique Uniform Resource Locator (URL) address. Also, by accessing the appropriate URL, the decision support system can be accessed from remote locations.
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