Damage to skewed bridges in recent earthquakes has reinforced the potential vulnerability of these structures. The effect of skew angle on a bridge’s fragility could vary for different bridge types, ages, or geometric configurations. This paper conducts a probabilistic seismic assessment of skewed bridge performance, focusing on single-frame concrete box-girder bridge subclasses. The effect of skew angle on bridge seismic fragility is investigated for bridges with single- or two-column bents, integral or seat-type abutments, and minimal or significant levels of seismic design. Component and system-level damage states consistent with HAZUS-MH definitions are also explored in this study. The results reveal that older bridges, which are more likely to experience higher damage states, are particularly susceptible to column damage and are not sensitive to skew. Similarly, the presence of integral abutments in newer bridges significantly reduces the vulnerability and minimizes the impact of the skew ang...

Structural health monitoring (SHM) is a hot research topic with the main purpose of damage detection in a structure and assessing its health state. The major focus of SHM studies in recent years has been on developing vibration‐based damage detection algorithms and using machine learning, especially deep learning‐based approaches. Most of the deep learning‐based methods proposed for damage detection in civil structures are based on supervised algorithms that require data from the healthy state and different damaged states of the structure in the training phase. As it is not usually possible to collect data from damaged states of a large civil structure, using such algorithms for these structures may be impractical. This paper proposes a new unsupervised deep learning‐based method for structural damage detection based on convolutional autoencoders (CAEs). The main objective of the proposed method is to identify and quantify structural damage using a CAE network that employs raw vibration signals from the structure and is trained by the signals solely acquired from the healthy state of the structure. The CAE is chosen to take advantage of high feature extraction capability of convolution layers and at the same time use the advantages of an autoencoder as an unsupervised algorithm that does not need data from damaged states in the training phase. Applications on the two numerical models of IASC‐ASCE benchmark structure and a grid structure located at the University of Central Florida, as well as the full‐scale Tianjin Yonghe Bridge, prove the efficiency of the proposed algorithm in assessing the global health state of the structures and quantifying the damage.

In this paper, a novel approach is presented to detect damage in a cable-stayed bridge based on feature extraction and selection. In the first part of the paper, several features are used in time domain, frequency domain, and time-frequency domain to detect damage. Next, several spectral parameters are introduced as effective features to reduce false alarms. Support vector machine (SVM) technique is employed as a classifier to compare the detection ability of the proposed and conventional features. In the second part of the paper, several feature selection techniques including forward selection, backward elimination, the minimum redundancy and max relevancy, ReliefF and SVM approach based on recursive feature elimination (SVM-RFE) algorithm are employed to improve feature extraction accuracy through selecting and ranking the most discriminative feature as an optimal feature subset. The capability of the proposed approach to detect damage is investigated by a data set obtained from a health monitoring system of a real benchmark problem. The data consists of vibration acceleration of the Yonghe bridge in both healthy and damaged states. Results show that the proposed procedure can effectively reduce false alarms.