Qiang Xu

Using numerical simulations, this study investigated the seismic response of concrete dams when subjected to near-fault obliquely incident P waves. For comparison, several near-fault pulse-like movements with different motion parameters were selected and decomposed into non-pulse residual components. A seismic input procedure for P wave oblique incidence was developed and verified based on the viscous-spring artificial boundary theory. A finite element model of a concrete dam system was used for nonlinear time history analyses. The damage and displacement responses were analyzed under pulse-like and non-pulse motions with incident angles varying from −90° to 90°. The response differences induced by the pulse characteristics incident direction were examined. The relationship between the seismic parameters and response indices was also determined to obtain the optimal seismic parameter describing the variation under different incident conditions. Moreover, the coupled effect of the pulse feature and oblique incidence on the dynamic response and seismic behavior was examined. Finally, a nonlinear three-dimensional predictive model was proposed based on the optimal seismic parameter S_a(T₁) and incident angle, exhibiting high correlation and accuracy. The results demonstrated that incident angles between 60° and 75° (with higher spectral acceleration values) intensified the dam damage and vibration when subjected to the oblique near-fault P waves, a crucial discovery for improving the seismic design and safety measure of concrete dams located in regions prone to near-fault seismic hazards.

Accurate and fast prediction of structural response under seismic action is important for structural performance assessment, however, existing deep learning-based prediction methods do not consider the effect of pulse characteristics of near-fault pulse-like ground motions on structural response. To address the above issues, a new method based on wavelet decomposition and attention mechanism-enhanced decomposition learning, i.e. WD–AttDL, is proposed in this study to predict structural response under pulse-like ground motions. This method innovatively combines a WD-based velocity pulse-identification method with decomposition learning, where decomposed pulses and high-frequency features are used as inputs to the neural-network model, thus simplifying the identification of pulse features for the model. The decomposition learning model integrates several types of neural network components such as convolutional neural network feature extraction submodule, long short-term memory neural network temporal learning submodule and self-attention mechanism submodule. In order to verify the accuracy and validity of the proposed methodology, three sets of case studies were carried out, including elasto-plastic time-history analyses of planar reinforced concrete (RC) frame structures, a three-dimensional RC frame structure, and two types of masonry seismic isolation structures. Compared with existing structural seismic response models, WD–AttDL synergistically integrates the advantages of different modules and thus offers a higher prediction accuracy. In particular, it reduces the peak error of the predicted response, which is important for the evaluation of structural performance. In addition, WD–AttDL has a great potential for application in fast vulnerability and reliability analysis of pulse-like earthquakes in nonlinear structures.

This study proposes a novel velocity divergence‐generalized adaptive probability density evolution method (VD‐GAPDEM) for calculating the probability density function of the stochastic response process of stochastic structures under stochastic dynamic loads. First, based on the principle of probability conservation, the velocity divergence‐generalized adaptive probability density evolution equation (VD‐GAPDEE) is derived for a stochastic system that can effectively consider the shape and location changes of the joint transitional probability density of representative points (RPs) in the stochastic response process. Second, a novel VD‐GAPDEM is proposed to solve the VD‐GAPDEE directly using the point selection technique based on the generalized F discrepancy and the second‐order Runge–Kutta method with a smoothing kernel method (Runge–Kutta‐SKFAM). Furthermore, the differences and connections between VD‐GAPDEM and the existing probability density evolution method are analyzed. Additionally, the high computational efficiency and accuracy of the proposed VD‐GAPDEM are demonstrated through three typical examples of stochastic response analysis, involving stochastic systems subjected to stochastic dynamic loads.

Structural seismic response reconstruction is important to assess the safety of structures. This study presents a novel multidomain feature-guided generative adversarial neural network model (MWGAN-TF) for reconstructing the seismic responses of structures, which takes into account the joint non-stationarity of the seismic response in the time-frequency statistical domain. It innovatively incorporates time, frequency, and statistical-domain feature constraints into the multiscale generative adversarial neural network, which guides the model to learn the multidomain feature information of the seismic response at different time scales. A statistical indicator (CNCSI) was proposed to evaluate the performance of the model in capturing nonstationary characteristics. The effectiveness of the MWGAN-TF was verified using response data from numerical models of a three-story moment-resisting frame and reinforced concrete frame structures, as well as the field measurement data of an actual building. Thereafter, the effects of different domain feature-guided models on the reconstruction response accuracy are discussed. The results show that embedding multidomain feature constraints can provide a more reliable response reconstruction by improving the ability of the model to capture nonstationary characteristics. Thus, the deep learning paradigm based on multidomain feature guidance outperforms the classical neural network guided only by time-domain features.

This study aims to propose a seismic performance analysis framework based on the oblique incidence theory, estimate a normalized damage response (RDVR) through seismic parameters and incident angles, and explore the variation in dynamic responses of concrete dams with arbitrary incident angles under near-fault vertically polarized shear (SV) waves. Several near-fault pulse-like records were selected, and their decomposed nonpulse (residual) components were used to guarantee the similarity of the response spectra. Furthermore, a seismic input model of the SV wave oblique incidence was developed based on the viscous-spring artificial boundary method, and its accuracy was verified using a numerical application. The effects of the incident angles of SV waves (−30° to 30° at 5° intervals), seismic intensity, and polarity on the damage and displacement responses of a gravity dam–reservoir–foundation system under pulse-like and nonpulse motions were studied. Additionally, the relationships between the incident angle, seismic characteristics [including the peak ground velocity to peak ground acceleration (PGV/PGA) ratio and spectral acceleration S_a(T₁)], and the proposed RDVR were established. The most unfavorable incidence angles for different cases were derived. The numerical results showed that the negative direction (from downstream to upstream) has a significant effect on the nonlinear responses of a dam. The seismic features of PGV/PGA exhibited a better correlation with RDVR than S_a(T₁). Subsequently, the damage to the gravity dam was estimated using the multivariate fitted method according to the proposed prediction model. The results showed that the proposed prediction model with arbitrary incident angles provides a good estimation of the real results. Thus, it is important to consider the coupled effect of incident angles and seismological characteristics in the seismic assessment of concrete dams located in near-fault regions.