Over the last decade, there has been an important growing trend towards the use of technology to perform building inspections within the Architecture, Engineering and Construction (AEC) industry. In recent years, researchers have shown an increased interest in Non-Destructive Testing (NDT) based on scanning and surveying diagnostics such as Ground Penetrating Radar (GPR), Light Detection and Ranging (LiDAR), Laser Scanning, and Close-Range Photogrammetry (CRP). To date however, not enough research has been developed to explore durability approach diagnostic inspection. This paper describes the design and implementation of an approach using Close-Range RGB Photogrammetry based on RPAS surveying for durability failure detection and inspection in heritage buildings. The RPAS utilizes CRP approach to capture a series of images and then develop an RGB processed orthophoto. The detection approach uses Deep Learning (DL) to Classify durability failures in the structure using MATLAB. Finally, the proposed approach was tested in one of the heritage bridges of the Camino Real way in Campeche, Mexico. Camino Real is the way followed by the Empress Charlotte of Habsburg in 1865. Results demonstrated a highly practical and low technological resources approach for durability diagnostic inspection.