Central to structural health monitoring (SHM) is data modeling, manipulation, and interpretation on the basis of a sophisticated SHM system. Despite continuous evolution of SHM technology, the precise modeling and forecasting of SHM measurements under various uncertainties to extract structural condition-relevant knowledge remains a challenge. Aiming to resolve this problem, a novel application of a fully probabilistic and high-precision data modeling method was proposed in the context of an improved Sparse Bayesian Learning (iSBL) scheme. The proposed iSBL data modeling framework features the following merits. It can remove the need to specify the number of terms in the data-fitting function, and automatize sparsity of the Bayesian model based on the features of SHM monitoring data, which will enhance the generalization ability and then improve the data prediction accuracy. Embedded in a Bayesian framework which exhibits built-in protection against over-fitting problems, the proposed iSBL scheme has high robustness to data noise, especially for data forecasting. The model is verified to be effective on SHM vibration field monitoring data collected from a real-world large-scale cable-stayed bridge. The recorded acceleration data with two different vibration patterns, that is, stationary ambient vibration data and non-stationary decay vibration data, are investigated, returning accurate probabilistic predictions in both the time and frequency domains.

Summary Computer vision‐based crack detection is an effective technique for evaluating the structural safety of concrete building structures. Currently, the existing crack‐detection methods based on machine learning often require pre‐training or/and re‐training the model, which is an experiential and complex task. In this study, based on sparse representation, we cast the crack damage detection problem by determining the outlier in the sparse correlation coefficients between the selected crack and the testing image regions. Specifically, by dividing one concrete image to be detected, we can obtain multiple testing image regions. Then, the spatial variation features of these image region contents are computed via discrete cosine transformation and are further used as the dictionary set. Considering that only a fraction of the dictionary set belongs to the cracks, the correlation coefficients of the selected known crack regions in the dictionary set should be sparse. Furthermore, a fast iterative shrinkage‐thresholding algorithm (FISTA) was utilized to obtain the optimum sparse correlation coefficients. Finally, for the dictionary set, the atoms (i.e., regions) that have larger values in the sparse correlation coefficients are treated as outliers (i.e., cracks), and the 3δ principle is exploited to identify these outliers. Experiments on a practical concrete image set show that the proposed algorithm is more accurate and efficient than traditional crack‐detection methods.

Structural damage detection is usually an ill-posed inverse problem due to the contamination of measurement noise and model error in structural health monitoring. To deal with the ill-posed damage detection problem, l2-regularization is widely used. However, l2-regularization tends to provide nonsparse solutions and distribute identified damage to many undamaged elements, potentially leading to false alarms. Therefore, an adaptive sparse regularization method is proposed, which considers spatially sparse damage as a prior constraint since structural damage often occurs in some locations with stiffness reduction at the sparse elements out of the large total number of elements in an entire structure. First, a response covariance-based convex cost function is established by incorporating an l1-regularized term and an adaptive regularization factor to formulate the sparse regularization-based damage detection problem. Then, optimal sensor placement is conducted to determine the optimal measurement locations where the acceleration responses are adopted for computing the response covariance-based damage index and cost function. Further, the predictor-corrector primal-dual path-following approach, an efficient and robust convex optimization algorithm, is applied to search for solutions to the damage detection problem. Finally, a comparison study with the Tikhonov regularization-based damage detection method is conducted to examine the performance of the proposed adaptive sparse regularization-based method by using an overhanging beam model subjected to different damage scenarios and noise levels. The numerical study demonstrates that the proposed method can effectively and accurately identify damage under multiple damage scenarios with various noise levels, and it outperforms the Tikhonov regularization-based method in terms of high accuracy and few false alarms. The analyses on time consumption, adaptiveness of the sparse regularization factor, model-error resistance, and sensor number influence are conducted for further discussions of the proposed method.

Near-field earthquakes, characterized by long-period velocity pulses with large peak ground velocities and accelerations, have led to severe damage to many civil structures designed in accordance with the current seismic codes. A novel hydro-pneumatic semi-active resettable device (HSRD) is proposed for vibration suppression of building structures subjected to near-field earthquakes. The main portion of this device is a cylinder-piston assemblage comprising four separate chambers filled with two different materials, that is, magnetorheological (MR) fluid and pressurized air. The two sides of a bypass pipe are connected to the two adjacent MR fluid chambers in the middle of the cylinder, forming a closed-circulating loop. The bypass pipe has an on-off valve controlling the stiffness by adjusting its on-off threshold and a functional valve controlling the damping by changing the MR fluid's property. The initial stiffness of HSRD is set by adjusting the pressures or lengths of the two gas chambers. Simulation studies with a five-story and a 10-story building structures subjected to three near-field earthquakes are conducted to evaluate the performance of HSRD. Three semi-active control schemes are considered to create optimal hysteresis loops of HSRD for achieving prominent vibration mitigation. It is revealed that the device with all the control schemes is effective in vibration suppression of the structures suffering from near-field earthquakes, and the resetting control strategy has the best performance among them. The results validate the capability of HSRD assisted by a semi-active control strategy for vibration mitigation of buildings subjected to near-field earthquakes.

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