Search Results (386)

Seismic reliability assessment of steel frame structures using nonlinear finite element analysis is often hindered by implicit limit state functions and high computational cost. To address these challenges, this study proposes a machine learning-based framework for global seismic reliability analysis. A nine-story steel frame model is established and validated through modal and pushover analysis. Global sensitivity analysis using the Sobol’ method is performed to identify key parameters governing the maximum inter-story drift ratio. Three machine learning models—PSO-SVR, PSO-XGBoost, and PSO-BPNN—are trained with the selected features and integrated into Monte Carlo simulation (MCS) for reliability calculation. The results show that the PSO-BPNN model achieves the highest accuracy with the maximum error of 1.0259% relative to direct MCS, outperforming the conventional MLE-based approach, which yields errors up to 11.9383% due to the non-standard distribution of the structural response. The impact of training sample size on model performance is also examined, with 1000 samples identified as a practical threshold for acceptable prediction accuracy. Existing code design methods require modifications based on the total probability approach for global reliability analysis. This study offers an efficient and precise methodology for seismic reliability design of steel frame structures, particularly when structural responses deviate from standard parametric distributions. Full article

The seismic fragility of reinforced concrete (RC) bridge piers is critical for urban rail transport systems, as severe pier damage may interrupt post-earthquake operation and threaten network safety. Compared with conventional highway bridge piers, urban rail transport RC solid piers usually have lower axial load ratios, larger cross-sections, and stricter serviceability requirements. However, the combined effects of geometric parameters, reinforcement detailing, and material strength on their cyclic behavior, dynamic response, and seismic fragility remain insufficiently understood. To address this issue, seven 1/4-scale RC solid pier specimens were tested under quasi-static cyclic loading to examine the effects of pier height, transverse reinforcement ratio, and longitudinal reinforcement ratio on damage evolution, hysteretic response, skeleton curves, and energy dissipation. A fiber-based OpenSees model considering bond-slip effects was then established, validated against the tests, and extended to a full-scale prototype pier for parametric analysis. The effects of aspect ratio, axial load ratio, longitudinal reinforcement ratio, stirrup ratio, steel yield strength, and concrete strength were evaluated under cyclic loading and nonlinear dynamic time-history excitations. An incremental dynamic analysis-based probabilistic seismic demand model was further developed using 30 near-fault ground motions, with peak ground acceleration as the intensity measure and displacement ductility as the engineering demand parameter. The results showed that increasing the aspect ratio changed the failure mode from flexure-shear-dominated to flexure-dominated behavior, increasing the ultimate displacement from 122 mm to 155 mm while reducing the peak lateral strength from 263 kN to 248 kN. Increasing the longitudinal reinforcement ratio improved both peak strength and ultimate displacement, from 226 kN to 262 kN and from 120 mm to 160 mm, respectively. The numerical results indicated that aspect ratio, axial load ratio, and longitudinal reinforcement ratio had more pronounced effects on seismic demand and fragility than stirrup ratio. Increasing steel yield strength generally reduced seismic fragility, whereas increasing concrete strength enhanced lateral resistance but did not necessarily improve fragility performance. These findings suggest that the seismic performance of urban rail transport RC solid piers should be evaluated by combining cyclic response, dynamic demand, and fragility-based performance, rather than by maximizing any single design parameter. Full article

With the transition to deep coal mining, the transparent detection of hidden geological hazards in the floor strata is fundamental for production safety. In mine seismic exploration, gliding waves—inhomogeneous plane waves propagating along the coal–rock interface—offer a unique advantage for penetrating high-velocity floors via the skin effect, overcoming the total reflection limitations of conventional in-seam waves. This study investigates the propagation laws and anomaly response characteristics of floor gliding waves using super-critical incidence theory and high-order staggered-grid finite difference simulations. The results demonstrate that the apparent velocities of gliding P and S-waves are bounded by those of the coal and host rock, exhibiting minimal dispersion. Quantitative analysis using a penetration depth model reveals that while penetration depth is frequency-dependent—with lower frequencies providing deeper reach—high-frequency components remain essential for high-resolution imaging. Crucially, the proposed method was validated through a field Case Study at the 11123 working face. By utilizing a specialized deep-hole excitation strategy to ensure super-critical incidence, the inversion successfully identified a hidden fault extending up to 60 m below the floor, which was subsequently confirmed by rock roadway excavation. These findings establish a robust physical basis for designing underground floor-detection systems and provide a significant theoretical reference for addressing detection blind spots in deep mining environments. Full article

High intake tower structures exhibit significant time-varying characteristics in their seismic performance during service life due to environmental erosion and material deterioration. This paper establishes a time-varying seismic fragility analysis framework for intake towers over their full life cycle based on the incremental dynamic analysis (IDA) method. By introducing time parameters, time-varying probabilistic seismic demand models based on displacement and local damage indices are constructed, and five performance levels are defined. The research results indicate that with increasing service life, the probability of the structure reaching critical performance levels exhibits a nonlinear growth. After 40 years of service, material strength and elastic modulus begin to decline significantly, with evident degradation of seismic performance. At a seismic acceleration of 0.8 g, the probability of the 60-year-service structure reaching the slight damage limit state (LS₁) has reached 99%, while the probability of reaching the collapse limit state (LS₄) exceeds 25%. The local damage index results demonstrate that under the same seismic intensity, the exceedance probability of tower-side damage exceeding LS₁ for the 60-year-service structure has increased by approximately 10% compared to that of the new structure (0-year service). Therefore, in the seismic design and retrofitting decision-making for intake towers, the time-varying characteristics over the entire service life must be fully considered. Particularly when the service life exceeds 40 years, seismic fortification standards should be appropriately enhanced or targeted strengthening strategies should be developed based on time-dependent fragility curves, so as to avoid underestimating long-term seismic risks. This study provides a quantifiable scientific basis for whole-life safety assessment and resilience enhancement of high-rise intake towers. Full article

Wireless sensor networks (WSNs) deployed for seismic monitoring must sustain long-term operation under strict energy constraints, where premature node failure degrades spatial coverage and detection reliability. This paper presents a safety-constrained reinforcement learning framework for transmission scheduling in energy-harvesting seismic WSNs. The proposed approach integrates Proximal Policy Optimisation (PPO) with action masking and a runtime guard-layer safety filter that enforces battery-preservation and load-balancing constraints without retraining. The guard layer intercepts policy actions and substitutes safe alternatives when constraint violations are detected, using a scoring function that combines battery headroom with network-wide load equity. Experiments across three network scales (10, 15, and 30 nodes) with solar energy harvesting demonstrate that the guard-enhanced PPO achieves 99.46% transmission success at 30 nodes while maintaining 66.47% node survival—a 58.3% improvement in survival over the highest-reward baseline (Closest) at the cost of only a 6.2% reduction in cumulative reward. Crucially, the guard-enhanced policy outperforms the unconstrained PPO baseline simultaneously on cumulative reward ($+11.4\%$), transmission success ($+0.8$ pp), and node survival ($+15.4\%$), demonstrating that hard safety constraints, when properly aligned with the system’s energy model, provide both performance and safety gains rather than a fundamental trade-off. Sensitivity analysis across event rates ($p_{\mathrm{event}}=0.5$ and $0.9$) confirms that the guard layer’s advantage persists under both moderate and extreme monitoring conditions. Analysis across scales reveals distinct operational regimes: at 10 nodes, heuristic baselines are near-optimal; at 30 nodes, learned policies dominate, and safety filtering becomes critical for sustained operation. Full article

As bridge infrastructure globally transitions from theoretical monitoring toward intelligent digital management, Machine Learning (ML) has emerged as a transformative tool for data-driven lifecycle decision-making. This study presents a systematic and critical review of ML applications across the entire bridge lifecycle, integrating a PRISMA-based scientometric analysis (2020–2025) with a rigorous technical synthesis of 3 major domains. The research reveals a clear hierarchy in deployment readiness; while Design & Optimization and Seismic Fragility Assessment have achieved “High” readiness by leveraging deep learning surrogates to achieve up to a 50-fold computational speedup over traditional simulations, Vibration-Based Damage Identification remains at a “Low–Medium” level due to environmental noise sensitivity and low Signal-to-Noise Ratios (SNR). Technical findings indicate that vision-based models (e.g., ViT, YOLOv8) show strong and promising performance for surface defect detection in controlled or semi-controlled settings, though broader field deployment remains constrained by lighting variability, dataset diversity, and validation at scale. In deterioration modeling and Remaining Useful Life (RUL) prediction, temporal architectures (e.g., LSTM) effectively capture non-linear trends, though operational risks such as “model drift” and “domain shift” in simulation-dependent models necessitate periodic retraining. This review identifies critical bottlenecks, including the “small data” paradox and the “black-box” dilemma. The work concludes by outlining a strategic roadmap centered on Physics-Informed Neural Networks (PINNs), Federated Learning for cross-agency collaboration, and Explainable AI (XAI) to foster professional trust in safety-critical infrastructure management. Full article

This study focuses on RCS frame structures and selects six different types of ground-motion intensity measures (IMs), including peak ground acceleration (PGA), spectral acceleration at the fundamental period Sa(T₁), the modified intensity measure S^* considering period elongation effects, IM₁₂ and IM₁₂₃ accounting for higher-mode effects, and Housner intensity (HI). Based on a set of near-fault pulse-like ground-motion records, a Bayesian seismic fragility analysis characterized by different IMs is conducted. This study reveals the influence of these IMs on the estimation of fragility parameters under three limit states—immediate occupancy (IO), life safety (LS), and collapse prevention (CP)—using both uniform non-informative priors and lognormal weakly informative priors. The results indicate that, in terms of the applicability of IMs across different limit states, all IMs exhibit highly stable fragility parameters in the elastic IO stage, where the results from maximum likelihood estimation (MLE), uniform priors, and lognormal priors are nearly identical, suggesting that sufficient sample information renders the influence of priors negligible. In contrast, in the CP stage, characterized by strong nonlinearity and collapse, the differences among IMs become most pronounced. HI consistently yields stable results across all methods with almost no variation. When the structure enters the CP stage with small samples and strong nonlinearity, the lognormal prior effectively promotes distribution convergence, suppresses over-dispersion, and corrects asymmetry, significantly improving the robustness of parameter estimation. Notably, different IMs exhibit varying sensitivity to Bayesian priors, among which S^* and HI are the least sensitive, demonstrating strong inherent stability and minimal dependence on prior constraints. Full article

This research investigates the effect of seismic loading on FRRs of RC structures using different machine learning (ML) algorithms. First, 20 portal RC frames with varying span numbers and stories are designed for seismic loads. This is then expanded to over 1760 frames by including further specifications such as span length, soil type, and seismic levels. This dataset is derived using decision tree algorithms, ensuring a robust and comprehensive analysis of the relationship between seismic design considerations and FRRs. All the models are subjected to the ISO 834 fire curve. Employing different ML algorithms indicate that the Random Forest Regression provides an accuracy of 81.88%, outperforming alternative algorithms such as Gradient Boosting and Support Vector Regression. Overall, the results suggest that structural elements designed for higher seismic demands exhibit higher FRRs. Additionally, as the number of spans increases, the associated FRRs also increase. An equation is then proposed to correlate the required sprinklers and the FRRs of seismically designed structures, making it possible to adopt a cost-reduction strategy in establishing fire protection systems. The ML-based algorithms here present a functional approach that can assist engineers in reducing structural and fire protection design costs while meeting the fire safety needs. Full article

The housing crisis in rapidly transforming earthquake zones represents the exhaustion of conventional construction paradigms. Unlike single-focused analyses, this study compares conventional reinforced concrete and modular steel systems from a holistic lifecycle perspective, using Turkey as a strategic laboratory for urban transformation. Employing qualitative content analysis, it maps in-depth interviews with 14 sector experts onto a ‘Dialectical Life Cycle Matrix’ via frequency-based consensus indicators. Expert assessments indicate that conventional methods face a structural bottleneck driven by architectural uniformity, labour-related weaknesses, rising costs, and prolonged durations, triggering seismic vulnerability, compromised living quality, and non-circular end-of-life outcomes. Modular systems counter this through factory-controlled rapid production, QA/QC mechanisms, and economies of scale, integrating guaranteed safety and the robust option of steel with R&D-driven human comfort. However, transitioning requires relinquishing deep-rooted advantages—financial flexibility, established order, regulatory comfort, cultural perception, and morphological harmony—introducing local trade-offs: high initial investment, geometric plot and logistical constraints, cultural barriers, and design concerns. Consequently, universal technologies cannot be directly transferred. To overcome Turkey’s local barriers, this study proposes a three-stage transition model: (I) civil and public-led legislative and workforce reforms; (II) financial innovation and gradual hybrid adaptation; and (III) industrial maturation transforming housing into a continuously updated living product. Full article

Seismic loading can significantly affect the safety and serviceability of structures supported by piles, making seismic performance a key consideration in pile foundation design. The coupling between slope effect and dynamic loading can significantly alter pile–soil interaction and consequently influence the response of laterally loaded piles. In the present study, a dynamic extension of the static p-y curve model for piles near clay slopes is developed for analyzing the response of laterally loaded piles under dynamic loading, based on adjustment of the real stiffness component, and the spring and dashpot model. A computational program based on the Beam on Dynamic Winkler Foundation (BDWF) model is developed for analyzing the dynamic response of piles near a slope. Comparison with finite element simulation results shows that the complex stiffness scheme provides accurate response predictions, thereby validating the effectiveness of the proposed model. Finally, parametric analyses are carried out to investigate the effects of loading parameters (excitation frequency and load amplitude), pile parameters (pile diameter, pile length, and adhesion coefficient), boundary conditions (pile-head and pile-tip constraints), and slope parameter (slope angle). The pile–soil system exhibits a characteristic frequency governed by the soil shear-wave velocity and pile diameter, while being essentially independent of slope angle and pile length. Near this frequency, the pile-head stiffness and damping ratio change significantly. The proposed method provides a practical tool for steady-state dynamic analysis of laterally loaded piles near clay slopes. Full article

To investigate the slope stability of the north bank anchorage of the Yellow River Three Gorges Bridge during foundation pit excavation and operational stages, a true three-dimensional geological model was established using Rhino6 and numerical simulations were performed using FLAC3D7.0, supplemented by stereographic projection kinematic analysis and the shear strength reduction (SSR) method. Systematic simulations were conducted for foundation pit excavation, main cable load application, heavy rainfall, and two seismic loading conditions, and the deformation characteristics and plastic zone evolution patterns of the slope under different conditions were analyzed. The stereographic projection kinematic analysis indicates that the dominant discontinuity sets do not constitute kinematically admissible planar sliding, wedge sliding, or toppling failure modes, confirming the validity of adopting a continuum model. The numerical simulation results show that the maximum slope displacement after foundation pit excavation is 13.13 mm, with the plastic zone exhibiting a discontinuous scattered distribution, and the slope is overall stable. After the application of the main cable load, the maximum displacement decreases to 7.86 mm; the counterweight effect of the anchorage self-weight significantly improves the deep stability, while the horizontal cable force generates a wedge-shaped shear plastic zone at the slope toe. Under heavy rainfall conditions, rock mass saturation leads to an increase in the maximum displacement to 11.76 mm with expanded plastic zone volume, where the deterioration of strength parameters and the increase in pore water pressure are the primary causes of reduced stability. Under seismic conditions, the maximum displacements under the natural and artificial seismic waves are 15.83 mm and 17.29 mm, respectively, exhibiting a significant elevation amplification effect with extensive plastic zone development in the shallow surface layer. The shear strength reduction analysis yields factors of safety of 2.4 and 2.27 for the heavy rainfall and seismic conditions, respectively, both significantly exceeding the code requirements, demonstrating that the slope possesses an adequate safety margin under extreme loading conditions. Full article

Inter-module connections are the critical load-transfer components in modular steel buildings (MSBs), whose mechanical behavior directly governs the overall safety and seismic performance of the entire structural system. To address the unresolved issue that the influence of complex loading conditions, especially the coupling effect of biaxial bending, on the load-transfer mechanism and degradation law of bayonet-type self-locking and unlockable connections remains poorly understood, two groups of full-scale quasi-static tests were conducted in this study. Specimen S1 (0°) was designed for the in-plane compression–bending–shear loading condition, while Specimen S2 (45°) was designed for the spatial compression–biaxial bending–shear loading condition. The test results demonstrate that both groups of specimens exhibit typical three-stage mechanical characteristics. The average initial stiffness of Specimen S1 (0°) is 5.47 kN/mm, while that of Specimen S2 (45°) is 6.08 kN/mm. The average ultimate load of S1 (0°) reaches 162.8 kN, and that of S2 (45°) is 164.85 kN. The average ductility coefficient of S1 (0°) and S2 (45°) is 2.79 and 2.14, respectively. Comparative analysis indicates that Specimen S1 (0°) presents superior energy dissipation capacity and ductility, while Specimen S2 (45°) has higher initial stiffness accompanied by faster stiffness degradation in the late loading stage. A high-fidelity refined FE model of the bayonet-type self-locking and unlockable connection was established. The FE analysis results are in good agreement with the test results, with the relative error of the positive flexural bearing capacity controlled within 5%. On this basis, parametric FE analysis was carried out to explore the influence of axial compression ratio on the mechanical performance of the connection. Furthermore, theoretical calculation formulas for the ultimate flexural bearing capacity of the connection under in-plane compression–bending–shear loading and spatial compression–biaxial bending–shear loading were proposed respectively. The calculated results are compared with the test data, with all relative errors within 5%, which verifies that the proposed formulas have favorable prediction accuracy for the ultimate flexural bearing capacity of the connection under both aforementioned complex loading conditions. Full article

Existing structural health monitoring of LNG (liquefied natural gas) liquid storage tanks is strictly constrained by explosion-proof safety and engineering conditions, making it impractical to achieve full-domain coverage through dense sensor deployment. How to achieve effective coverage of structural seismic weak parts under limited measuring point conditions is the core issue for monitoring scheme optimization. This paper takes a practical large full-containment LNG storage tank project as the research object and proposes a targeted sensor deployment method based on finite element seismic response analysis: identifying structural seismic weak parts through refined finite element modeling and seismic response analysis, thereby achieving coverage of critical regions and improved monitoring efficiency under limited sensor constraints. The research approach is as follows: a finite element model of the LNG storage tank is established using ADINA software and verified through modal analysis combined with on-site ambient vibration testing, ensuring the accuracy and engineering applicability of numerical simulation. Typical seismic records including El Centro, Tangshan, and TAFT are selected, and seismic response analysis of the tank is carried out, clarifying the displacement response laws under different seismic waves and identifying the junctions of dome roof and tank wall, buttress columns and tank wall, and the upper and local areas of the tank wall as structural seismic weak parts. Based on the characteristics of these parts and on-site explosion-proof conditions, a four-measuring-point targeted monitoring sensor deployment scheme is formulated and applied in engineering. This research constructs a structural health monitoring method for LNG storage tanks featuring “structural model verification–weak part identification–monitoring scheme customization,” providing a new approach for tank monitoring under explosion-proof safety constraints and partially addressing the limitations of traditional empirical deployment methods. This study establishes a technical path covering the full cycle of routine operation, seismic response, and post-earthquake assessment, providing methodological support for the structural health monitoring of LNG storage tanks, and its core concepts can also serve as a reference for the structural health monitoring of similar large-scale thin-walled storage tanks. Full article

Current seismic design practices primarily emphasize the structural safety of buildings, while research on the safety of non-structural components in interior design remains relatively insufficient. In this study, from the perspective of interior design, we explored the performance of non-structural components during earthquakes and how design strategies can reduce damage and enhance the adaptability of interior spaces, to establish a design framework that integrates safety considerations with behavioral guidance. We conducted a literature review, questionnaire survey, and comprehensive analysis in this study. The questionnaire was structured to investigate (1) the development of interior finishing and seismic design, (2) the seismic performance of non-structural components, and (3) the application trends of seismic disaster prevention and evacuation strategies. The respondents included property owners, design and construction professionals, government agencies, and academic experts. Their responses were analyzed for the differences in perception and needs regarding safety and spatial adaptability among different stakeholder groups. Through analysis, influencing factors were identified, and an integrated design framework of non-structural components—spatial planning and behavioral guidance—was established for the development of an interior design strategy toward earthquake disaster prevention. Among the three dimensions, application trends of seismic disaster prevention and evacuation strategies received the highest evaluation score, with an average score of 4.7 (a standard deviation of 0.4) and a reliability coefficient of α = 0.93. 90% of respondents supported the integration of virtual reality, building information modeling, and simulation-based training to improve evacuation efficiency, demonstrating the high feasibility and promotion potential of disaster-prevention technologies. Full article

The rapid growth of heterogeneous web sources has created significant challenges for the automated extraction and structuring of critical domain-specific information, particularly in real-time seismic monitoring scenarios. Despite the existence of official governmental reporting systems, relevant earthquake-related data are often distributed across diverse online platforms with highly variable and dynamically evolving HTML (HyperText Markup Language) structures, leading to incomplete, delayed, or inconsistent information retrieval. Existing rule-based and semi-automated approaches lack scalability and robustness under such conditions. To address this gap, this study proposes a self-adaptive framework based on large language models (LLMs) for the automated extraction and structuring of earthquake-related web content. The proposed approach integrates transformer-based schema generation, repository-guided schema matching, and an iterative refinement mechanism, enabling the system to dynamically adapt to heterogeneous document structures. A formal utility-based decision mechanism is introduced to optimize schema selection and reuse, while embedding-based similarity modeling facilitates efficient transfer of extraction patterns across structurally related webpages. The experimental evaluation was conducted on a heterogeneous benchmark dataset comprising multiple web domains with diverse structural characteristics. The results demonstrate that the proposed framework achieves a success rate of 85% across all evaluated models, with the best-performing configuration reaching an extraction accuracy of 96.5% and a final composite score of 84.26. Additional analysis reveals significant improvements in extraction completeness, reduction in false positives and false negatives, and effective reuse of a compact set of robust schemas. Error analysis indicates that the primary challenges are associated with noisy HTML structures and incorrect DOM (Document Object Model) element selection, rather than deficiencies in textual content. The findings confirm that combining lightweight transformer models with adaptive memory and schema reuse mechanisms enables the development of scalable, robust, and high-performance web extraction systems. The proposed approach is particularly suitable for real-time information retrieval in safety-critical domains, where timely and accurate data aggregation from heterogeneous sources is essential. Full article