This study presents a novel type of shape memory alloy (SMA) cable-restrained high damping rubber (SMA-HDR) bearing, which is particularly suited to nearfault (NF) regions where the pulsing effect potentially exists in the ground motions. The working mechanism of the bearing is first described, followed by an experimental investigation on a full-scale SMA-HDR bearing specimen. The test results confirm the efficient restraining effect offered by the SMA cables, which contribute to 65% and 24.4% of the lateral load resistance and total energy dissipation, respectively, prior to the initial fracture of the SMA cables. The failure of the cables is initiated near the end grip where moderate stress concentration exists at this region. Following the experimental study, the numerical modeling strategy for the bearing is discussed, and a case study is then presented, demonstrating the application of the SMA-HDR bearings in the Datianba #2 highway bridge, a real project that first adopts the proposed bearings in the world. A simplified design process is introduced for the bridge with novel SMA-HDR bearings to mitigate the potential damage during strong earthquakes especially the NF ones. The system-level analysis on the prototype bridge shows that the novel SMA-HDR bearings equipped with ten 7×7×1.2 SMA cables in each bearing could reduce the average maximum bearing displacement (MBD) by nearly 30% compared with the conventional bridge with HDR bearings. The application of the novel SMA-HDR bearing can significantly alleviate the pounding effect, especially under the NF earthquakes. The presence of the SMA cables tends to increase the maximum force response of the piers, but this effect is minor and under control.

In bridge health monitoring (BHM), crack identification and width measurement are two of the most important indices for evaluating the functionality of bridges. In order to reduce the labor cost in field detection, researchers have proposed a variety of deep learning (DL)-based detection techniques for crack recognition. However, some problems still exist in extending these techniques to practical applications, such as data annotation difficulty, limited model generalization ability, and inaccuracy of the DL identification of the actual crack width measurement. In this paper, an application-oriented multistage crack recognition framework is proposed, namely, Convolutional Active Learning Identification-Segmentation-Measurement (CAL-ISM). It includes four steps:(1) pretraining of the benchmark classification model, (2) retraining of the semisupervised active learning model, (3) pixel-level crack segmentation, and (4) crack width measurement. Beyond numerical experiments, the performance of the CAL-ISM is validated for practical applications: (i) bridge column test specimen and (ii) field BHM projects. In conclusion, the obtained results from these applications shed light on the high potential of CAL-ISM for BHM applications, which is recommended in future deployments for BHM. BACKGROUND AND MOTIVATIONSBy the end of 2020, there are 912,800 highway bridges in China, including 6444 long-span bridges and 119,935 normal bridges. The total mileage of the highway is about 5.19 million kilometers (km), of which the maintenance mileage of the highway is 5.14 million km, accounting for 99.0% of the total mileage (Ministry of transport of the People's Republic of China, 2020). The remaining 1% is for newly constructed highways with no need for immediate © 2022 Computer-Aided Civil and Infrastructure Engineering. maintenance. Similarly, there are more than 618,000 bridges in the United States, where nearly 36% of the bridges need repair work, and 7.3% of them are considered structurally deficient (American Road and Transportation Builders Association, 2021). Therefore, surging demands in the maintenance work of highway bridges in China, the United States, and many other countries are worldwide realities and it is a necessity to develop effective methods to evaluate the service functionalities of bridge structures. Because of cost-efficiency and easy-forming, reinforced