Vehicle image classifier for bridge displacement correlation

Advanced computing brings opportunities for innovation in a broad gamma of applications. Traditional practices based on visual and manual methods tend to be replaced by cyber-physical systems to automate processes. The present work introduces an example of this, a machine vision system research based on deep learning to classify bridge load, to give support to an optical scanning system for structural health monitoring tasks. The optical scanning system monitors the health of structures, such as buildings, warehouses, water dams, etc. by the measurement of their coordinates to identify if a coordinate displacement befalls that could indicate an anomaly in the structure that can be related to structural damage. The use of this optical scanning system to monitor the structural health of bridges is a little more complicated due to the vehicle's transit over the bridge that causes a vehicle-bridge interaction which manifests as a bridge oscillation. Under this scheme, the bridge oscillation corresponds to their coordinate's displacement due to the vehicle-bridge interaction, but not necessarily due to bridge damage. So, a bridge load classifier is required to correlate the bridge coordinates measurements behavior with the bridge oscillation due to vehicle-bridge interaction to discriminate the normal behavior of the structure to abnormal behavior or identify tendencies that could indicate bridge deformation or discover if the bridge behavior due to loads is changing through the time.