The bearing plate is an important part of a tendon–anchorage subsystem; however, its function and safety can be compromised by factors such as fatigue and corrosion. This paper explores the feasibility of the electromechanical impedance (EMI) technique for fatigue crack detection in the bearing plate of a prestressed anchorage. Firstly, the theory of the EMI technique is presented. Next, a well-established prestressed anchorage in the literature is selected as the target structure. Thirdly, a 3D finite element model of the PZT transducer–target anchorage subsystem is simulated, consisting of a concrete segment, a steel anchor head, and a steel bearing plate instrumented with a PZT transducer. The prestress load is applied to the anchorage via the anchor head. The EMI response of the target structure is numerically obtained under different simulated fatigue cracks in the bearing plate using the linear impedance analysis in the frequency domain. Finally, the resulting EMI response was quantified using two damage metrics: root-mean-square deviation and correlation coefficient deviation. These metrics are then compared with a threshold to identify the presence of cracks in the bearing plate. The results show that the simulated cracks in the bearing plate are successfully detected by tracking the shifts in the damage metrics. The numerical investigation demonstrates the potential of the EMI technique as a non-destructive testing method for assessing the structural integrity of prestressed structures.

The smart strand technique has been recently developed as a cost-effective prestress load monitoring solution for post-tensioned engineering systems. Nonetheless, during its lifetime under various operational and environmental conditions, the sensing element of the smart strand has the potential to fail, threatening its functionality and resulting in inaccurate prestress load estimation. This study analyzes the effect of potential failures in the smart strand on impedance characteristics and develops a 1D convolutional neural network (1D CNN) for automated fault diagnosis. Instead of using a realistic experimental structure for which transducer faults can be hard to control accurately, we adopt a well-established finite element model to conduct all experiments. The results show that the impedance characteristics of a damaged smart strand are relatively different from other piezoelectric active sensing devices. While the slope of the susceptance response is widely accepted as a promising fault indicator, this study shows that the resistance response is more favorable for the smart strand. The developed network can accurately diagnose the potential faults in a damaged smart strand with the highest testing accuracy of 94.1%. Since the network can autonomously learn damage-sensitive features without pre-processing, it shows great potential for embedding in impedance-based damage identification systems for real-time structural health monitoring.

This study examined the feasibility of the admittance-based method for detecting simulated damage in the bearing plate of a prestressed anchorage. The proposed method utilized the PZT (lead zirconate titanate) interface technique to acquire a strong admittance response from the anchorage. Firstly, the numerical feasibility of the method was demonstrated by detecting the presence of fatigue cracks and preload changes in a fixed–fixed beam-like structure. Next, the experimental verification was carried out using a lab-scale prestressed anchorage model. A PZT interface prototype was designed and surface-mounted on the bearing plate. The admittance response of the PZT interface was measured before and after the simulated damage cases of the bearing plate. Afterwards, a statistical damage metric, root-mean-square deviation (RMSD) was used to quantify the change in the admittance spectrum and identify the damage’s presence. It was shown that the experimental admittance response was consistent with the numerical simulation result in the same effective frequency band. Both the numerical and experimental results showed clear shifts in the admittance spectrum due to structural damage. The simulated damages in the bearing plate were successfully identified by the RMSD evaluation metric.

The catastrophic impact of Hurricane Helene proved that standard FEMA flood maps are often inadequate for assessing risk in complex mountainous terrain. Using Buncombe County, North Carolina, as a case study, this research introduces a replicable framework for siting emergency shelters based on a multi-dimensional Flood Risk Index. By synthesizing HAND-derived inundation data, land-use intensity, and a machine learning-based Socio-Economic Vulnerability Index (SEVI), we mapped the intersection of hazard and vulnerability. Our analysis reveals a significant misalignment—a large portion of the current shelter network sits in high-risk zones, while safer upland corridors in the north and west remain underutilized. This study delivers a data-driven roadmap for disaster preparedness, ensuring that future shelter placement is not only safe from terrain-driven floods but also strategically and equitably located.