Vibration-based methods are promising for damage identification; however, their capabilities for damage identification under temperature variations are usually limited. In the paper, a vibration-based nondestructive global damage identification method based on a genetic algorithm (GA) is proposed to identify structural damage location and severity under the influence of temperature variation and noise. The proposed method is verified by a number of damage scenarios of a three-span continuous beam and a two-span steel grid and shows good robustness under random noise levels. First, considering that the material properties of a structure and boundary conditions of a system are generally temperature-dependent, the relation between temperature and elastic and geometric stiffness matrices is introduced, and damage parameters along with temperature are defined as variables of the numeric model. Second, a GA is introduced where a new objective function with different weight coefficients, combined with frequencies and mode shape, is proposed and developed. Third, damage identification of a three-span continuous beam and a two-span steel grid under temperature variation is carried out numerically, considering changes of material properties and boundary conditions, and damage existence, location, and severity are accurately identified. Finally, it is shown that the proposed method is very robust even when the data are polluted with artificial noise.

To avoid false alarms for vibration-based structural damage detection methods, temperature effects on damage-sensitive features should be eliminated. In this paper, a novel two-step damage identification method combining a multilayer neural network and novelty detection is developed to differentiate the changes in natural frequencies (one of the most commonly used damage features that can be obtained reliably and relatively easily) due to damage from those induced by temperature variations. In the first step, a multilayer artificial neural network, which resembles an auto-associative neural network but uses temperature variables in addition to the frequencies as the inputs, is explored to identify patterns in frequencies of undamaged structures under varying temperatures. Euclidean distance is then utilized as a novelty index to quantify the discordancy between patterns in undamaged cases and candidate cases. Numerical studies using a simply supported beam and finite element models based on an experimental grid structure, which simulate different levels of stiffness reductions under varying temperature conditions, are used to verify the detectability and robustness of the proposed approach. It is shown that the incorporation of the proposed artificial neural network with novelty detection enables one to robustly distinguish damage occurrence and severity regardless of temperature variations and noise perturbations. Using an unsupervised learning scheme, the proposed approach transforms a multivariate analysis using modal frequencies and temperature data into a straightforward univariate discordancy test using the novelty index. Given these competitive advantages, this approach is very attractive for the development of an automated continuous monitoring system in practical applications.

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.

This paper presents a crowdsensing framework employing smartphones in monitoring populations of bridges in future smart cities. In this regard, because there are a variety of vehicles with different features traveling over the bridge at different speeds, it is critical to investigate the robustness of indirect monitoring methods against vehicle features. Therefore, an experimental study was performed, including two lab-scale bridges with different boundary conditions and a robot car that was capable of maintaining a variety of speeds and suspension systems. The proposed framework focuses on the identification of frequencies of the bridge using acceleration signals recorded on the car. It was demonstrated that by using a large set of passing cars with different features, the fundamental frequency of the bridge was captured. Successful identification of the deviation of fundamental frequencies between two bridges with relatively close frequency values proved that the framework is capable of detecting damage induced frequency changes of the bridge. Since this framework relies on the use of smartphones, it provides the opportunity to efficiently monitor a plethora of bridges in a metropolitan area with minimum cost.