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The welding quality of beam-column joints of steel structures is one of the most important factors affecting seismic behavior of steel structures. In order to investigate the influence of initial defects on the seismic performance of H-shaped beam-column joints, the extended finite element method was used. A crack was set at the lower flange weld of H-shaped beam-column joints, and the influence of defect position and defect depth on the seismic performance was examined. The loading methods with different amplitudes were adopted. The research findings indicate that the initial defect exerts a significant influence on the seismic performance of the joint. The bearing capacity and energy dissipation capacity of the joints were decreased, and the degree of reduction accelerates with the increase of defect depth. However, the loading amplitude has a relatively minor effect on the seismic performance of the joint, with a maximum difference of 6.97% and an average difference of 0.31% in ultimate bending moment. Similarly, the influence of the initial defect location is limited, with a maximum difference of 11.9% and an average difference of 3.6%.

Near-fault ground motions are often influenced by forward rupture directivity effects, which may generate distinctive long-period velocity pulses and substantially increase seismic demands compared to far-field records. However, conventional design spectra prescribed in seismic codes generally fail to incorporate these near-fault characteristics, potentially resulting in unconservative structural designs. This study examines the spectral amplification associated with near-fault effects and introduces a coefficient-based modification approach derived through regression analysis. A total of 20 ground motion records from the 6 February 2023 Mw 7.7 Pazarcik earthquake were employed - 10 near-fault and 10 far-field - and to account for the bidirectional nature of seismic excitation, both the East-West (EW) and North-South (NS) components were analyzed separately, yielding 40 datasets. Five-percent-damped elastic acceleration response spectra were computed using the Newmark-Beta method across a broad period range, after which average spectra for the near-fault and far-field groups were compared and spectral ratios were calculated to quantify amplification due to fault proximity. The period range was classified into short (T

The ability to accurately estimate structural responses is essential for ensuring safety and enabling early damage detection in complex engineering systems. However, obtaining full-field structural state data is often hindered by the physical limitations of sensor installation in extreme operational environments and the scarcity of failure data required for data-driven approaches. To address these challenges, this paper proposes a physics-based virtual sensing technique that reconstructs the full-field strain distribution using a sparse array of strain sensors. The proposed method utilizes the mode superposition principle, approximating the global structural response as a linear combination of modal weights derived from limited sensor data. A key feature of this approach is the construction of a hybrid basis set that integrates dominant low-order eigenmodes with quasi-static correction vectors, ensuring that both dynamic characteristics and static aeroelastic deformations are accurately captured with high computational efficiency. The method is applied to a blended wing body (BWB) aircraft structure, and its performance is verified through numerical simulations under various cruise conditions with elliptical lift distributions. The analysis results show that the proposed technique effectively estimates the strain field over the entire structure. Relative errors are mostly within 10% compared to the finite element analysis reference value. In addition, the error is less than 4% in the major deformation area, showing high precision. These findings confirm the potential of the proposed virtual sensing framework as a robust and efficient solution for real-time structural health monitoring in aerospace applications.

As the state-of-the-art in seismic resilience evolves from basic life-safety toward damage mitigation and continuous functionality, piloti-type reinforced concrete (RC) buildings remain a critical vulnerability due to their inherent vertical irregularities. While extensive literature addresses general soft-story retrofits, few studies detail the specific plastic hinge evolution and directional isolator–column interactions required to optimize isolation strategies. To bridge this gap, this study evaluates a representative piloti-type RC prototype (Ministry of Land, Infrastructure and Transport, R.O.K.), explicitly selected because its mid-rise height, asymmetric wall layout, and column dimensions accurately represent the broader stock of vulnerable piloti structures. To ensure strict methodological reproducibility, including ASCE-41 plastic-hinge definitions, material nonlinearity parameters, and effective section properties, all modeling choices are comprehensively detailed in SAP2000. Also, distinct from dynamic earthquake simulations, this study employs displacement-controlled quasi-static analyses to systematically map capacity and collapse progression without ground motion variability. Comparative analyses in both principal directions for non-isolated and base-isolated (lead-rubber bearing) configurations reveal that the non-isolated frame develops collapse-level softstory hinges at low displacements. On the other hand, the base-isolated model completes the prescribed displacement history without collapse by dissipating input energy through isolator hysteresis, dramatically reducing superstructure hinge demand and standardizing the inter-story drift profile. Differentiating this work from prior research, the results highlight that directional stiffness disparities and column sizing dictate energy absorption pathways as larger column sections and higher-stiffness axes significantly enhance isolator efficiency. The findings in this study provide novel, reproducible insights into typical piloti-type RC structural interactions, offering practical guidance for performancebased design in high-density urban seismic regions.

A new method for the seismic design of plane steel moment resisting frames is developed. This method determines the design base shear of a plane steel frame through modal synthesis and spectrum analysis utilizing different values of the strength reduction (behavior) factor for the modes considered instead of a single common value of that factor for all these modes as it is the case with current seismic codes. The values of these modal strength reduction factors are derived with the aid of a) design equations that provide equivalent linear modal damping ratios for steel moment resisting frames as functions of period, allowable interstorey drift and damage levels and b) the damping reduction factor that modifies elastic acceleration spectra for high levels of damping. Thus, a new performance-based design method is established. The direct dependence of the modal strength reduction factor on desired interstorey drift and damage levels permits the control of deformations without their determination and secures that deformations will not exceed these levels. By means of certain seismic design examples presented herein, it is demonstrated that the use of different values for the strength reduction factor per mode instead of a single common value for all modes, leads to more accurate results in a more rational way than the codebased ones.

The structural safety of high-speed railway bridges in mountainous, seismic-prone regions is severely challenged by the concurrent hazards of earthquakes and foundation scour. This study presents a systematic investigation into how three key, design-controllable pier material parameters—concrete strength, longitudinal reinforcement strength, and reinforcement ratio—affect the system-level seismic fragility of a typical simply-supported girder bridge under both scoured and non-scoured conditions. A refined numerical model was developed in OpenSees and validated against dynamic response characteristics. Incremental Dynamic Analysis (IDA) was carried out using a suite of spectrally matched ground motions. A newly proposed "Synergistic Effect Index" quantifies the interaction between improvements in different material parameters. The results clearly identify the reinforcement ratio as the most influential parameter: increasing the reinforcement ratio from 0.5053% to 1.5097% reduces the probability of severe damage at PGA=0.4 g by approximately 25% under non-scoured conditions and 22% under scoured conditions. Although scour consistently increases fragility—reducing median seismic capacity by 16-18% across all material configurations—targeted material optimization still offers significant benefit. Notably, under high-intensity shaking (PGA=0.4 g) with scour present, pairwise combinations of material upgrades exhibit an antagonistic effect (Synergistic Effect Index=0.82-0.85), meaning their combined benefit is less than the sum of individual improvements. From these findings, the study proposes a practical, three-tiered design strategy: prioritizing reinforcement ratio optimization (target range 1.0%-1.3%), rationally upgrading material grades, and integrating mandatory scour protection measures. This integrated approach provides a clear and resilient design pathway for bridges facing combined seismic and scour threats.

Input energy is the critical theoretical foundation for performance-based seismic design, as it directly reflects the energy demands imposed on structures during seismic events, thereby offering more physically meaningful metrics for structural performance assessment and design. However, relatively little attention has been devoted to the development of ground motion prediction equations (GMPEs) for near-fault elastoplastic input energy. Based on the NGA-West2 ground motion database, this study selects moment magnitude (Mw), average velocity of shear waves in the uppermost 30 m (VS30), fault type, and rupture distance as key characteristic variables. A machine-learning-based support vector regression (SVR) framework is employed to predict constant-strength relative input energy, with model hyperparameters globally optimized using the particle swarm optimization (PSO) algorithm. These methodological choices aim to enhance the generalization capability and prediction accuracy of PSO-SVR machine-learning-based GMPEs. The rationality of the PSO-SVR machine-learning-based GMPEs fitting was verified through residual analysis and the influence of explanatory variables on the prediction results. The results show that the PSO-SVR machine-learning-based GMPEs for constant-strength relative input energy proposed demonstrates higher accuracy and stability in the prediction of near-fault ground motion data. The findings provide reliable references for performance-based seismic design and contribute valuable insights to the application of machine learning methods in earthquake engineering.

This paper proposed a numerical modeling method for constructing concrete model through the point-volume-element aggregate interference discrimination method based on sub-region retrieval. Firstly, the conventional concrete modeling method was enhanced by optimizing the interference discrimination logic between aggregates and the aggregate retrieval approach. Subsequently, a series of concrete models with varied aggregate geometric shapes, interfacial transition zone (ITZ) thicknesses, aggregate volume fractions, and aggregate gradations were generated, validating the reliability of the proposed method. Finally, the efficiency of the proposed method was validated by comparing the number of iterations and modeling time required with those of the conventional method. The results indicate that the proposed method can control the aggregate geometric shape and ITZ thickness by adjusting the number of vertices and the scaling distance. Compared to the conventional method, the proposed method significantly reduced the number of iterations and modeling time, thereby enhancing modeling efficiency. The advantage of the proposed method became more pronounced as the number of aggregates increased. When the number of generated aggregates reaches 100, the required number of iterations is reduced by 83.6%, and the modeling time is reduced by 66.2%.

In this study, a new model was developed to predict the axial load-carrying capacity of circular concretefilled steel tube (CFST) columns externally confined with fiber-reinforced polymer (FRP). For this purpose, 227 experimental data points collected from the literature were split, with 75% for training and 25% for testing. A new equation was then derived using Gene Expression Programming (GEP). Additionally, prediction models were developed using several machine learning (ML) algorithms, including MLP (Multilayer Perceptron), KNN (KNearest Neighbors), BAG (Bootstrap Aggregating), RF (Random Forest), GBM (Gradient Boosting Machine), LightGBM (Light Gradient Boosting Machine), XGBoost (Extreme Gradient Boosting), and CatBoost (Categorical Boosting). A 10-fold cross-validation approach was employed during the grid search to identify the optimal hyperparameter combination for the ML models. The predictive performances of the proposed models were statistically evaluated and compared with existing equations in the literature. CatBoost demonstrated the best predictive performance on the test data, with a MAPE of 4.075, an RMSE of 180.509, an R2 of 0.988, and a COV of 0.059. SHAP analysis was used to evaluate the contribution of each input parameter to the prediction results.

This paper presents a comprehensive investigation into the stability of a circular tunnel constructed in soil containing a circular void. To achieve this, an adaptive finite element limit analysis (FELA) approach, combined with nonlinear programming (NLP), is employed. To enhance the convergence speed of the NLP algorithm, a novel approach is introduced, incorporating the feasible arc interior point algorithm (FAIPA) to perturb the search direction through a secondary deflection, and an imprecise step search algorithm to improve the efficiency of step length search. Based on the FELA method, both the upper bound (UB) and lower bound (LB) of the non-dimensional stability number are computed. Extensive parametric studies are conducted to evaluate the effects of key parameters on tunnel stability. The computational results are effectively conveyed through the utilization of dimensionless stability tables and charts, specifically designed to facilitate ease of use by engineers. Furthermore, meticulous examination of typical failure mechanisms provides deep insights into the complex behavior of tunnels under varying conditions.