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This study presents a technology used for the prediction of economic losses to facilities in a given area during an earthquake, thereby enabling the efficient application of performance-based maintenance and seismic strengthening. We also propose an algorithm for the establishment of a reinforcement plan that minimizes earthquake-induced economic losses within a constrained budget. The algorithm incorporates fragility functions from prior research and utilizes an optimization technique for budget allocation, leveraging the target damage ratio concept and constrained optimization. Based on the fragility curve, the probability of occurrence for each damage state for a specific PGA value and the damage rate for each damage state are calculated. From these values, the expected damage ratio (EDR) is estimated. An optimization-based budget allocation algorithm is developed to find the elements that would result in the lowest damage rate for a limited cost. To validate the applicability of the model, we created a hypothetical city with a 30 km × 30 km area containing bridges, embankments, and buildings. The estimated pre- and post-reinforcement damage was assessed in two earthquake scenarios, allowing us to test the effectiveness of the optimization-based budget allocation model in reducing damage. These results suggest that the proposed model offers a viable strategy for efficient seismic strengthening within budgetary constraints. [ABSTRACT FROM AUTHOR]

Subwavelength defect imaging using guided waves has been known to be a difficult task mainly due to the diffraction limit and dispersion of guided waves. In this article, we present a noncontact super-resolution guided wave array imaging approach based on deep learning to visualize subwavelength defects in plate-like structures. The proposed approach is a novel hierarchical multiscale imaging approach that combines two distinct fully convolutional networks. The first fully convolutional network, the global detection network, globally detects subwavelength defects in a raw low-resolution guided wave beamforming image. Then, the subsequent second fully convolutional network, the local super-resolution network, locally resolves subwavelength-scale fine structural details of the detected defects. We conduct a series of numerical simulations and laboratory-scale experiments using a noncontact guided wave array enabled by a scanning laser Doppler vibrometer on aluminate plates with various subwavelength defects. The results demonstrate that the proposed super-resolution guided wave array imaging approach not only locates subwavelength defects but also visualizes super-resolution fine structural details of these defects, thus enabling further estimation of the size and shape of the detected subwavelength defects. We discuss several key aspects of the performance of our approach, compare with an existing super-resolution algorithm, and make recommendations for its successful implementations. [ABSTRACT FROM AUTHOR]