A crowdsourcing-based methodology using smartphones for bridge health monitoring

This article presents a novel framework for monitoring and evaluation of a population of bridges using smartphones in a large number of moving vehicles as mobile sensors. Within this framework, a damage detection methodology based on Mel-frequency cepstral coefficients and Kullback–Leibler divergence is developed. For this method, Mel-frequency cepstral coefficients of the vibration data collected from smartphones in vehicles crossing bridges are first extracted as features. Then, Kullback–Leibler divergence is used to compare the distributions of features. The damage in a bridge can be identified by quantifying the difference of the distributions obtained for the same bridge. Both numerical and lab experiments are conducted to verify the proposed framework and methodology. In lab experiments, a smartphone and two wireless accelerometers are used for data collection. From our results, it is concluded that the damage existence can be successfully identified using smartphones in a large number of vehicles. Also, it is observed that there is a significant correlation between the magnitude of the damage features and the severity of damage. The results show that the method has the potential to monitor a population of bridges simultaneously and in almost real time.