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.
Cracks are important signs of degradation in existing infrastructure systems. Automatic crack detection and segmentation plays a key role in developing smart infrastructure systems. However, this field has been challenging over the last decades due to irregular shape of the cracks and complex illumination conditions. This article proposes a novel deep-learning architecture for crack segmentation at pixel-level. In this architecture, one convolutional layer is densely connected to multiple other layers in a feed-forward fashion. Max pooling layers are used to reduce the dimensions of the features, and transposed convolution layers are used for multi-level feature fusion. A depth-first search–based algorithm is applied as post-processing tool to remove isolated pixels and improve the accuracy. The method is tested on two previously published data sets. It can reach 92.02%, 91.13%, and 91.58% for the first data set, and 92.17%, 91.61%, and 91.89% for the second data set for precision, recall, and F1 score, respectively. The performance of the proposed method outperforms other state-of-the-art methods. At the end of the article, the influence of feature fusion methods and transfer learning are also discussed.
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