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• Numerically represented a diagonal SMA-wire based bracing system. • Developed probabilistic bridge models for various uncertainties. • Proposed refined definition for failure for SMA-retrofitted bridges. • Exemplified risk-based design of SMA wire-based brace for bridges in California. In this paper, a risk-based seismic design method for diagonal self-centering braces (SCB) with shape memory alloy (SMA) wires implemented in multi-column bents is proposed to enhance the performance of older concrete bridge classes. An existing SMA-SCB system that improves the performance of older reinforced concrete building frames was adopted to numerically examine further applications to nonductile bridges. To this end, risk-based seismic design of the SMA-SCB to minimize the probability of collapse and demolition of bridge classes was developed by adopting a sophisticated finite element model that reflects the recentering behavior of an actual SMA-SCB; probabilistic bridge models considering uncertainties associated with the material and geometrical properties of bridges; refined failure condition; and the total probability theorem. A comparison of the results of the traditional seismic fragility-based and proposed designs indicated that the amount of SMA-SCB required by the former is nearly half of that required by the proposed approach because the traditional approach overemphasizes the demand at larger ground motion intensities. [ABSTRACT FROM AUTHOR]

• Assess the seismic reliability analysis of a bridge network using BC approach. • Propose a new computationally efficient BC concept to rank importance order of bridges. • Suggest the seismic retrofit plan of a bridge network based on the BC importance of bridges. • Present a BC-based approach that improves the network performance using new constructions. Although bridges are integral parts of transportation networks, they are vulnerable to natural hazards. The damage incurred during these natural hazards events leads to network disruptions and hinders post-event recovery. Therefore, improving the seismic performance of transportation networks is critical. This paper proposes an origin–destination pair betweenness centrality (BC)-based methodology to rank bridges according to their importance in a complex bridge network. The proposed methodology is an easy-to-use approach that considers user equilibrium and topology and significantly shorten the computational time. In addition, this study proposes two strategies including the retrofit of bridges and construction of new bridges based on the BC importance of bridges to mitigate the seismic risk of bride networks. A well-studied bridge network in California was used to demonstrate the application and the effect of different strategies was compared in this study. Bridges located at the shortest path significantly affect the seismic performance of bridge networks, indicating there is no need to rehabilitate or replace all bridges under budget constraints. Also, constructing five new bridges based on BC importance improved seismic performance of the bridge network much more than retrofitting 21 bridges. Moreover, the proposed methodology presents a significant reduction in computational time (only 1/30 compared with an existing study) in resolving the importance of bridges in a network and can be applied to provide useful information for disaster management teams to mitigate the seismic risk of bridge networks. [ABSTRACT FROM AUTHOR]

• Propose a novel deep learning model based on Runge-Kutta recurrent neural network (RKRNN) with prior knowledge to realize structural system identification and seismic response prediction. • Formulate a partition training strategy to train the proposed neural network to improve the efficiency of training. • Utilize three numerical examples to valid the feasibility of RKRNN model including a linear three degrees of freedom system, a nonlinear single degree of freedom system with Bouc-Wen hysteresis and a simply supported bridge. • Site monitoring data from a bridge located in California has been used to further validate the proposed approach. In the seismic analysis of structural systems, dynamic response prediction is an essential problem and is significant in every stage during the structural life cycle. Conventionally, response analysis is carried out by numerical analysis. However, when the structural parameter is unknown, the establishment of a numerical model will be difficult. Enlightened by the Runge-Kutta (RK) numerical algorithm, this paper proposes a novel recurrent neural network named Runge-Kutta recurrent neural network (RKRNN) to realize the seismic response prediction. A partition training strategy is formulated to train the proposed neural network and to improve the efficiency of training. The proposed model can be trained by using a limited number of samples. Three numerical examples are utilized to validate the feasibility of RKRNN model including a linear three degrees of freedom (DOFs) system, a nonlinear single DOF system with Bouc-Wen hysteresis, and a numerical reinforced concrete bridge model. Additionally, the site monitoring data from a real-world bridge is utilized to further validate the proposed network. The results show that the proposed RKRNN model can effectively and efficiently predict the structural response under seismic load and exhibits robustness to noise, with good potential for applications in engineering practice. [ABSTRACT FROM AUTHOR]

Machine-learning has recently gained considerable attention in the earthquake engineering community, as it can map the complex relationship between the expected damage and the input parameters. It is often necessary to understand the reasons for the behavior and predictions of the machine-learning model. This paper addresses this issue through interpretable machine-learning approaches such as partial dependence plots, accumulated local effects, and Shapely additive explanations. The evaluation of these approaches is carried out (1) at a component level by analyzing the shear strength predictions by a machine-learning model and (2) at a regional level through the machine-learning model for the regional damage assessment of bridges in California. The comparison helps to identify (1) the proper implementation of these approaches for the efficient use of machine-learning models and (2) key influential variables and thresholds that govern the prediction of the machine-learning models. [ABSTRACT FROM AUTHOR]

• New self-centering precast concrete frames with increasing post-decompression stiffness. • Seismic design method considers the changing stiffness of variable friction dampers. • A method to mitigate the concentration of story drifts was proposed. • A method to mitigate detrimental higher mode effects was proposed. This paper presents the numerical investigation of the seismic performance of post-tensioned self-centering precast concrete (SCPC) frames with variable friction dampers (VFDs) and hidden corbels (HCs). The VFDs are characterized by the combination of flat and grooved plates, where the friction forces change as the connection deformation increases, which enables designs to meet target seismic performance objectives at different seismic hazard levels. Theoretical and numerical models for the connection are proposed based on connection geometry, and a performance-based seismic design approach for the SCPC frames is outlined to meet target performance objectives at two common seismic hazard levels, the design basis earthquake (DBE) and the maximum considered earthquake (MCE). SCPC frames for a six-story prototype building located in a high seismic zone in California were designed using this procedure. Three frames with HC-VFD-SPSC connections were designed using various methods to mitigate concentration of drifts in the first story, and one frame was designed with conventional constant friction devices (CFDs) for comparison. Nonlinear time-history analyses were performed for these different designs using two sets of ground motions representing the DBE and MCE seismic hazard levels. The analysis results indicate that the frames with the HC-VFD-SCPC connections achieved the target performance objectives. The proposed approaches of modifying post-tensioning and VFD design parameters or column stiffness in the first story were effective in mitigating the concentration of drifts. Additionally, through comparison with the results of the frame with CFDs, it was found that the VFDs are effective in decreasing overall drift demands at the MCE level, as well as mitigating detrimental higher mode effects observed in SCPC frames with CFDs. [ABSTRACT FROM AUTHOR]