To address the issue of brittle failure induced by welding residual stress and inherent buckling failure of thin steel plates in conventional shear panel dampers (SPD), this paper proposes a novel prefabricated angle steel-constrained shear panel damper (PASPD). The structural configuration, operational mechanism, and distinctive features of the PASPD are comprehensively elucidated. Two prototype specimens were designed and manufactured for experimental investigation. Quasi-static low-cycle reversed loading tests were performed to evaluate the hysteretic behavior, energy dissipation capacity, and failure characteristics of the PASPD. Experimental results demonstrate that the PASPD exhibits stable hysteretic performance and superior energy dissipation characteristics. The study reveals that increasing the web plate thickness can delay the initiation of corner cracks but does not alter the fundamental failure mode of the PASPD. Appropriate boundary constraints using angle steel significantly regulate the stress and strain distribution within the PASPD, concentrating the deformation and energy dissipation in the central region. Compared with the traditional steel plate shear dampers, PASPD exhibits superior ductility performance and higher cumulative energy dissipation. The finite element simulation analysis demonstrates that the synergy between the flange connection plate and the side angle steel can optimize the shear force transmission path of the PASPD, guide the stress and strain distribution of its web to be more uniform, and form an ideal web shear energy dissipation mechanism, thereby enhancing the stability and hysteretic energy dissipation performance of PASPD.

Seismic resistance qualification is mandatory for telecommunication equipment prior to network deployment. While sinusoidal resonance beat waves (SRBWs) serve as optional excitations in seismic testing, practical applications have revealed operational limitations. This study comparatively investigated the seismic responses of typical telecommunication cabinets through shaking table tests using both SRBWs and artificial ground motions (AGMs). A comprehensive analysis of damage patterns, natural frequencies, deformations, and acceleration responses demonstrated SRBWs’ superior efficacy in exciting seismic reactions for equipment with natural frequencies exceeding 3 Hz. The findings substantiate the necessity of employing SRBW excitations for such equipment and the critical infrastructure categorized as “essential” or “important”. Furthermore, critical examination of the current Specifications for Seismic Test of Telecommunication Equipment (YD 5083—2005) reveals technical inconsistencies in SRBW implementation, prompting proposed revisions to enhance testing protocol reliability.

In the event of a continuous earthquake, strong aftershocks pose a significant threat to the bridge structures. In the analysis of seismic vulnerability, in order to consider the influence of strong aftershocks, a structural vulnerability analysis method based on spatial fitting is proposed. Taking a three-span continuous girder bridge as the object, a piecewise binary linear function is used to construct the probabilistic seismic demand model. The fitting effect and reliability of the probabilistic seismic demand model are compared and analyzed when the peak ground acceleration (PGA), peak ground velocity (PGV) and spectral acceleration (Sa) of ground motion were taken as the seismic intensity measure, and the vulnerability of the bridge in mainshock-aftershock (MS-AS) sequences based on spatial and single-sided fitting is analyzed respectively. The results show that the results of vulnerability of the bridge based on spatial fitting reflect the damage of strong aftershocks to bridges, which can effectively avoid underestimating the exceedance probability of bridges under main aftershocks. Furthermore, the probabilistic seismic demand model obtained by spatial fitting method can more accurately explain the relationship between seismic demand and structural damage. When the spectral acceleration is selected as the seismic intensity parameter, the fitting effect of the model under MS-AS sequences is the best. Additionally, the growth rate of the exceeding probability of the limit state of the bridge is dominated by the mainshock in the MS-AS sequences. The growth of the aftershock intensity has a greater impact on the exceeding probability in the vulnerability analysis based on the spatial fitting, which is conducive to the conservative estimation of the seismic performance of the bridge. The vulnerability assessment method of the bridge can provide reference for the design of highway bridge.

To study the impact of floor slab construction on metro-induced vibration responses and to explore the feasibility of reducing structural responses by optimizing floor construction, the conventional floor slab, the thickened floor slab and the additional sub-beam floor slab were designed and manufactured. Field model tests were carried out on the three test floor slabs respectively to study their dynamic response under on-site metro vibrations. Taking conventional floor slab as the test control group, the effects of increasing floor slab thickness or adding sub-beam on the floor slabs' characteristics and metro-induced vibration responses were compared and analyzed. The results indicate that the resonance effect is the main reason of metro-induced vibration responses of the floor slab. Increasing the floor slab thickness or adding sub-beam increases the vertical modal frequency of the floor slab, alleviate the resonance effect, and reduced the time-domain acceleration responses of the floor slab. Both increasing floor slab thickness and adding sub-beam can reduce the vibration acceleration level of the floor slab in a wide frequency band, and can reduce the weighted vibration level by 6 dB and 4 dB respectively, thus improving the vibration comfort performance of the floor slab. Adding secondary beams achieves a vibration reduction effect similar to doubling the floor slab thickness without significantly increasing the floor slab's weight and engineering cost. It is recommended that the addition of sub-beams as a floor construction measure be priority in the design of new metro adjacent structures to mitigate metro-induced vibration responses and improve vibration comfort.

Vertical ground motions have a significant impact on the seismic response of engineering structures, making the development of reliable vertical ground motion prediction models an important topic in the field of earthquake engineering. Traditional ground motion predictions are primarily based on actual strong motion records, using least squares regression to derive seismic motion parameter prediction models. However, conventional least squares regression often assumes linear relationships or predefined functional forms between variables, which may fail to fully capture the complex nonlinear relationships inherent in seismic data. In contrast, deep learning models can learn patterns from data and provide higher prediction accuracy for complex data distributions. In this study, deep learning methods were applied, and 9 953 vertical ground motion records from the NGA-West2 database were selected for model training and prediction. The self-DNN vertical seismic response spectrum prediction model was established and its performance was compared with traditional prediction models and a DNN neural network models. The results indicate that the vertical seismic response spectrum prediction model established using deep learning algorithms achieves high accuracy and delivers excellent predictive performance. These findings and analyses provide valuable references for vertical seismic response spectrum prediction and structural seismic design.

As a research hotspot in earthquake engineering, the performance-based seismic design concept has achieved mature applications in the seismic damage assessment of bridges, but its implementation in seismic design still needs further research. This study proposes a multi-objective optimization design method for piers based on seismic reliability by integrating the probabilistic seismic risk analysis framework with response surface theory and the improved Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ). First, the method for establishing seismic reliability of bridges is elaborated by combining seismic fragility and seismic hazard theories. A mathematical optimization model is then proposed with the seismic reliability of bridges and the material cost of piers as objective functions. A systematic design workflow for seismic optimization of piers is established by embedding response surface theory and the NSGA-Ⅱ. Subsequently, a typical highway bridge is taken as a case study. In accordance with the seismic design specifications for bridges in China, the seismic hazard curve and seismic vulnerability curve are developed, and the seismic damage characteristics of the bridge are analyzed. Finally, a response surface model for seismic reliability is developed to perform seismic optimization design for the case study bridge. The results show that the response surface model based on the quadratic polynomial can accurately describe the implicit relationship between the design parameters of piers and the seismic reliability of the bridge. The proposed seismic optimization design method in this paper can improve the seismic reliability of the bridge or reduce the material cost of the piers. Incorporating seismic reliability as an objective function directly consider the influence of piers on the seismic damage risk of the bridge. In addition, the multi-objective optimization seismic design can overcome the limitations of traditional empirical design methods and achieve more refined quantitative design. Designers can flexibly obtain the optimal solution from the Pareto solution set based on different optimization strategies.

Ground motion prediction models are an important foundation for seismic hazard analysis. Currently, the research on vertical ground motion prediction models in China is relatively few, and most of the existing ground motion prediction models used parametric equations, which may have limited prediction accuracy. Therefore, the development of horizontal and vertical ground motion prediction models with better prediction accuracy and reliability is necessary for further research. To address the above problems, this study, uses 1991 sets of Chinese horizontal and vertical ground motion records. The Butterworth non-causal filter method is applied to filter and reduce the noise of Chinese ground motion. The Chinese horizontal and vertical ground motion prediction model (CHV-DNN) is developed based on the deep learning method, and it is comprehensively assessed in terms of model performance, physical characteristics, and intra-and inter-event residual analyses. Finally, a correlation coefficient model for Chinese horizontal and vertical ground motion is provided. The results show that based on the residual analysis results of the CHV-DNN model, the most of the inter-event residuals are mainly distributed in the range of [-1, 1], and most of the residuals within events are mainly distributed in the range of [-1.5, 1.5], and the intra-event and inter-event residuals are both uniformly distributed on both sides of the residuals 0 baseline, which validate the reliability and accuracy of the model; The CHV-DNN model has better prediction accuracy and also has well physical characteristics; the correlation coefficient model calculated based on CHV-DNN has been more reasonable. The Chinese horizontal and vertical ground motion prediction model developed in this study will provide a research foundation for horizontal and vertical seismic hazard analysis in China.

To systematically study the influence of near-fault pulse characteristics on the seismic response of skew bridges, a four-span highway skew continuous girder bridge was taken and finite element models of the skew bridges with different skew angles were established by using OpenSees. The near-fault pulse-like ground motions with high-frequency components were artificially synthesized by using the decomposition-incorporation method. The effects of the moment magnitude and fault distance to the piers, main girders, beam-abutment collisions and exterior shear keys of skew bridges are analyzed. The results show that the sensitivity of the top displacement of the middle pier to the moment magnitude is greater than that of the side piers, but the sensitivity to the fault distance is not much different. The increase of the moment magnitude increases the displacement and rotation ratio of the main girder, as well as the maximum pounding force between the girder and the abutment. The results of increasing the fault distance are opposite to the previous results and the rotation ratio of the girder varies most when the skew angles is 15°. It is suggested to strengthen the shear reinforcement of the exterior shear keys for skew bridges with skew angles greater than 15°. As the moment magnitude is greater than 6.0 or the fault distance is 2~7 km, the exterior shear keys are in a state of failure under the near-fault ground motion.

At 7:58 on April 3, 2024, an M_S7.3 earthquake occurred (23.81°N, 121.74°E) in the waters of Hualien County, Taiwan, China, which was characterized as a thrust rupture. This earthquake is the largest earthquake since the “9·21” Chi-Chi earthquake. To comprehensively understand the characteristics and disaster effects of the earthquake and learn from the experience and lessons of the earthquake disaster, the cause of the earthquake is explained in combination with the mechanism of earthquake generation. Then, 714 strong ground motions recorded by 238 stations of the Earthquake Network Center of the Meteorological Bureau of Taiwan within 32 km of the fault are selected to analyze the engineering characteristics of these ground motions. Based on disaster investigation data, the relation between the earthquake and the structural damage of civil engineering as well as the distribution pattern of earthquake damage are discussed. The results show that ground motions of this earthquake have the characteristics of significant middle-to-high frequency contents with slow attenuation of PGA and Sa over rupture distance, causing the damage to medium-to-short period structures. The distribution of earthquake damage is concentrated in Hualien County, New Taipei City and Taipei City, and along the east side of the crustal butt belt of Taitung longitudinal Valley, the distribution is linear with the development of the fault zone. Hualien County is the most serious earthquake damage due to its proximity to the focal point, while New Taipei City and Taipei City are far away from the focal point, but the earthquake damage is also more serious due to the mountain amplification effect and basin amplification effect of ground motions. The relevant study can provide reference for the research of seismic fortification and seismic regionalization of medium-to-short period structures.

Estimating the epicentral distance from a single station is a critical task in real-time earthquake early warning systems. To address the limitations of the traditional B-Δ method, which relies on limited P-wave information and exhibits significant prediction errors, this study utilizes strong-motion data from the Japan K-NET network. A 3-second time window of three-component acceleration waveforms is used as input to a convolutional neural network (CNN), which directly extracts feature information from the waveforms to establish a CNN-based epicentral distance estimation model (CNN-Dis). The results show that in the test dataset, by normalizing both the input data and labels, the CNN-Dis model achieves an mean absolute error (MAE) of 28.119 6 km and a standard deviation of 34.682 7 km, outperforming the model without normalization. Compared to the traditional B-Δ method, the CNN-Dis model improves the reliability of epicentral distance estimation. Moreover, the CNN-Dis model provides relatively reliable results for offshore earthquakes, in contrast to inland events. The CNN-Dis model enhances the accuracy of epicentral distance estimation to a certain extent and provides strong support for the iteration and performance optimization of earthquake early warning technologies.