Search Results (280)

The mechanical behavior of lead/rubber bearings (LRBs) is strongly influenced by both ambient temperature and hysteretic heating under seismic loading; however, their coupled effects and underlying mechanisms remain insufficiently understood. This study presents a systematic investigation of the thermo-mechanical response of LRBs through combined experimental and numerical approaches. Dynamic cyclic tests were conducted on full-scale LRBs (700 mm in diameter) over a wide range of ambient temperatures, revealing that ambient temperature and hysteretic heating jointly govern the evolution of key mechanical properties, including stiffness, characteristic strength, and energy dissipation capacity. Specifically, decreasing temperature leads to stiffness and strength enhancement, whereas hysteretic heating induced by cyclic plastic deformation of the lead core results in progressive softening and degradation of restoring force. Based on the experimental observations, a modified uniaxial Bouc–Wen constitutive model is developed, incorporating the coupled effects of ambient temperature, hysteretic heating, and large-strain hardening. The proposed model is implemented in a single-degree-of-freedom (SDOF) base-isolated system to evaluate the seismic response under different temperature conditions. The results reveal a competing mechanism between ambient temperature and hysteretic heating: low temperatures tend to increase base shear and reduce displacement, while hysteretic heating produces the opposite effect, with their relative dominance depending on temperature level and ground motion intensity. Neglecting such thermo-mechanical coupling may lead to significant misestimation of structural response, particularly under long-duration strong ground motions. This study provides new insights into the coupled temperature-dependent behavior of LRBs and establishes a robust modeling framework for the seismic analysis and design of isolation systems under complex service conditions. Full article

Earthquake Early Warning Systems (EEWS) represent one of the most effective technological solutions for mitigating the impacts of strong ground motion in seismically active regions. This study presents the design, implementation, and comprehensive evaluation of a real-time earthquake early warning system for Izmir-a region in Western Anatolia characterized by complex tectonic structures and high seismic hazard-using multi-station seismic acceleration data. The proposed framework integrates multi-threaded data acquisition, signal preprocessing, Min-Max normalization, and Euclidean distance-based similarity analysis to enable rapid detection of anomalous seismic patterns during the early P-wave phase. The system architecture consists of distributed sensor inputs, centralized real-time processing, similarity-based anomaly detection, and user-oriented visualization and alerting modules. The performance of the system was evaluated using both real and synthetic seismic datasets. Instrumental earthquake catalog from the 12 June 2017 Karaburun (Mw 6.2) and 30 October 2020 Samos (Mw 6.6) earthquakes demonstrate that the system can generate early warnings 18 s and 13 s prior to strong ground shaking, respectively. In addition, synthetic seismic scenarios were employed to assess system robustness under varying noise levels, station configurations, and signal conditions. The results indicate that the proposed framework maintains stable detection performance and low false-positive rates across diverse operational scenarios. The methodology emphasizes computational efficiency and inter-station waveform coherence analysis, providing a lightweight alternative to conventional magnitude-based approaches. By avoiding computationally intensive source inversion, the system achieves low-latency performance while preserving detection reliability. The proposed EEWS demonstrates strong generalization capability, scalability, and practical applicability for real-time deployment in earthquake-prone urban environments. Full article

Regional coseismic landslide hazard assessment is important for disaster risk reduction and sustainable development in seismically active mountainous regions. Existing Newmark displacement prediction models exhibit systematic bias when applied to Southwest China due to the region’s distinctive seismotectonic and topographic characteristics. This study addresses this limitation by systematically evaluating and recalibrating seven established models using 591 horizontal strong-motion records from nine significant regional earthquakes (2007–2022). Among the recalibrated versions, the Yiğit2020 framework performed best but showed potential for further improvement. Analysis revealed a stable log-linear correlation between peak ground velocity (PGV) and Newmark displacement, with an average of 0.78 under different critical acceleration levels. By incorporating a log PGV term, a new model was developed, achieving improved performance with an R² of 0.92 and a standard deviation (σ) of 0.30. Validation results further showed that the new model reduced the mean relative error from 74.22% to 66.43% and the median relative error from 53.83% to 38.90%, compared with the recalibrated Yiğit2020 model. In a case study of the 2022 Luding Ms 6.8 earthquake, the proposed model yielded the highest landslide discrimination capability (AUC = 0.687), outperforming other models (AUC = 0.600–0.636). These results support more reliable regional hazard zoning and rapid post-earthquake risk identification, thereby contributing to sustainable land-use planning, infrastructure resilience, and disaster risk reduction in seismically active mountainous regions. Full article

Vertical land motion in urban areas is a critical manifestation of groundwater, directly affecting infrastructure stability and groundwater sustainability. While land subsidence caused by groundwater extraction has been widely investigated, the opposite process—surface uplift induced by groundwater recovery—remains poorly documented or understood, particularly regarding its hydrological mechanisms and potential hazards. Here, we integrate InSAR time-series analysis of Sentinel-1 imagery (2017–2025) with groundwater well records to quantify the spatial–temporal characteristics of uplift in Xi’an, China, and to evaluate its hydrogeological drivers. Results reveal a persistent surface uplift zone south of the ancient city in Xi’an, with rates up to 20 mm/yr. The uplift correlates closely with rising groundwater levels in the shallow confined aquifer, indicating a strong coupling between aquifer recharge and surface uplift. Calculated storage coefficients and hydraulic diffusivity values highlight marked spatial variations, constrained by some ground fissures that act as both mechanical discontinuities and hydrological barriers controlling pressure diffusion. Time-series analysis further identifies the eastward propagation of subsidence-to-uplift reversal in Yuhuazhai, an urban village with groundwater injection, which is used to quantify the diffusivity coefficients. Field investigations show that rapid groundwater rebound can lead to uplift-related hazards, such as basement seepage, underscoring that surface uplift must be considered alongside subsidence in urban water management. Full article

This paper investigates the trajectory planning problem for multiple unmanned aerial vehicles (UAVs) tasked with monitoring ground targets in complex, uneven terrains. The key challenge lies in maintaining continuous Line-of-Sight (LoS) while satisfying non-holonomic motion constraints and handling terrain-induced occlusions. To address this problem, we propose a Beam-search Model Predictive Control (BMPC) framework. The method integrates a first-order kinematic predictor for target motion estimation and a proactive safety altitude margin to guide UAVs toward favorable viewpoints before occlusions occur. The proposed approach is validated through extensive simulations based on high-resolution Digital Elevation Models (DEMs). Monte Carlo results demonstrate a significant reduction in LoS occlusion, decreasing the average occlusion rate from $38.75\pm 26.12\%$ to near zero in the noise-free case, compared with conventional reactive MPC methods. Under perception noise with a standard deviation of $1.5$ m, the LoS retention rate remains above $99\%$, indicating strong robustness to sensing uncertainty. In addition, the algorithm maintains stable computational performance, with an average execution time of approximately $1.68$ s per step in a non-optimized simulation environment. The proposed framework provides an effective solution for autonomous aerial surveillance in environments with substantial elevation variations, such as mountainous regions and urban canyons, by achieving a balance between tracking continuity and computational tractability. Full article

The dynamic response of ancient multi-drum columns, commonly found in historical monuments, is characterized by complex nonlinear mechanisms including rocking, sliding, and wobbling. Unlike modern monolithic columns, these structures consist of large, unbonded stone drums that rotate and interact dynamically during ground motion, resulting in highly nonlinear behavior due to intermittent impacts and evolving contact surfaces. The objective of this study is to evaluate the influence of the friction coefficient at the interfaces on the dynamic response of multi-drum columns. Two structural configurations are considered: (i) simple free-standing multi-drum columns, and (ii) multi-drum columns connected with iron dowels, replicating ancient Greek construction techniques. The columns analyzed are representative of the colonnade system of the Gymnasium of Ancient Messene, Greece. Sinusoidal base excitations with varying characteristics are applied, and parametric study is conducted by varying the interfacial friction coefficient. The results indicate that in the first configuration, low friction promotes interfacial sliding, leading to enhanced energy dissipation, a softened rocking response, and a reduced overturning frequency range. In the second configuration, variations in friction have a limited effect on the collapse frequency range, because at lower friction levels strong excitations lead to dowel reinsertion failure over a wide frequency range. Full article

Video-based action recognition for neural rehabilitation—spanning stroke recovery, Parkinsonian gait assessment, and cerebral palsy monitoring—faces critical challenges, including temporal ambiguity, non-causal motion correlations, and the absence of causally grounded dynamics modeling. While transformer-based architectures achieve strong performance, they often exploit spurious temporal and environmental cues, limiting reliability in safety-critical clinical settings. We propose NeuroPrisma, a neuro-prismatic video framework that integrates frequency-domain spectral decomposition with causal intervention under Structural Causal Models (SCMs) via the backdoor criterion. NeuroPrisma introduces (i) a Prismatic Spectral Attention (PSA) module, which applies discrete Fourier transforms to decompose temporal features into multi-scale frequency bands, disentangling slow postural dynamics from rapid corrective movements, and (ii) a Causal Intervention Layer (CIL), which performs do-calculus-based backdoor adjustment to remove confounding influences and produce causally invariant representations. PSA preconditions representations prior to intervention, improving confounder estimation and causal robustness. Extensive evaluation against seven state-of-the-art models (I3D, SlowFast, TimeSformer, ViViT, Video Swin Transformer, UniFormerV2, and VideoMAE) demonstrates that NeuroPrisma achieves 98.7% Top-1 accuracy on UCF101, 82.4% on HMDB51, 71.2% on Something-Something V2, and 91.5%/95.8% on NTU RGB+D (Cross-Subject/Cross-View), consistently outperforming prior methods. It further reduces the Causal Confusion Score (CCS) by 42.3%, indicating substantially lower reliance on spurious correlations, while maintaining real-time performance with 23.4 ms latency per 16-frame clip on an NVIDIA A100 GPU. All improvements are statistically significant (p < 0.001, Cohen’s d = 0.72–1.24). Evaluation was conducted exclusively on benchmark datasets (UCF101, HMDB51, Something-Something V2, and NTU RGB+D) under controlled conditions, without direct clinical validation on neurological patient cohorts. Overfitting was mitigated using three random seeds (42, 123, 456), RandAugment, Mixup (α = 0.8), weight decay (0.05), and early stopping. Cross-dataset generalization from UCF101 to HMDB51 without fine-tuning achieved 76.2% Top-1 accuracy. Future work will focus on prospective clinical validation across stroke, Parkinson’s disease, and cerebral palsy populations, including correlation with standardized clinical assessment scales such as Fugl–Meyer, UPDRS, and GMFCS. These results establish NeuroPrisma as a causally grounded and computationally efficient framework for reliable, real-time movement assessment in clinical rehabilitation systems. Full article

Earthquake-induced landslides are a major hazard in mountainous regions, where complex topography and near-surface conditions jointly control ground-motion amplification and slope instability. In this context, ground-motion models used as triggering inputs for landslide analyses must accurately represent site effects in complex terrain. This study develops a geomorphometry-informed ground-motion model based on predictors derived from global remote sensing Digital Elevation Models (DEMs), conceived as a triggering component for earthquake-induced landslide applications. The model is based on the eXtreme Gradient Boosting (XGBoost) regression algorithm and predicts peak ground acceleration, peak ground velocity, and spectral accelerations by integrating seismic source parameters, finite-fault source-to-site metrics, and geomorphometric site proxies derived from global DEMs. The model is trained on an extended Italian strong-motion dataset comprising about 8300 recordings from 90 earthquakes with finite-fault rupture models and is evaluated using a strict leave-one-event-out validation scheme. Results show that finite-fault parameterization reduces prediction errors by about 11% compared to point-source formulations, while DEM-derived site proxies improve predictive performance by approximately 5% relative to V_S30 and 12% relative to the fundamental frequency f₀. Residual analysis yields inter-event variability of 0.19–0.22 and intra-event variability of 0.23–0.26. The proposed framework demonstrates how global remote sensing products provide value-added predictors for ground-motion triggering in complex terrain, suitable for integration with earthquake-induced landslide susceptibility models. Full article

Rapid and robust trajectory planning for the Terminal Area Energy Management (TAEM) phase of horizontal-landing Reusable Launch Vehicles (RLVs) is critical but challenging due to large initial deviations, stringent terminal constraints, and strong model nonlinearities. To address the limitations of existing methods in convergence reliability and computational speed, this paper proposes a novel online trajectory optimization framework based on analytical lateral planning and equivalent dynamic decoupling. First, a cubic Bézier curve is employed to parameterize the lateral ground track, enabling the rapid generation of analytical expressions for the lateral states that strictly satisfy boundary constraints. Leveraging these analytical solutions, the original six-degree-of-freedom dynamics are exactly decoupled and reduced to a lower-dimensional model governing only the longitudinal motion. To further mitigate nonlinearity, the third derivative of height with respect to range is introduced as a virtual control variable, transforming the problem into a smoother form. The resulting equivalent longitudinal optimization problem is then efficiently solved using the Gauss Pseudospectral Method. Numerical simulations demonstrate that the proposed method significantly outperforms traditional approaches in computational efficiency: it generates feasible trajectories satisfying all constraints within 0.26 s (3$\sigma$ value). Furthermore, the method exhibits remarkable insensitivity to initial guesses, achieving stable convergence even with simple linear initialization. This approach provides a robust and real-time capable solution for complex TAEM trajectory optimization problems characterized by high nonlinearity and multiple constraints. Full article

This study applies InSAR time series analysis derived from Sentinel-1 satellite data (ascending and descending orbits) processed with ISCE2 and StaMPS (v.4.1) software to evaluate deformation dynamics in three landfill types near Gijón, Spain. Initially, the data were validated against the European Ground Motion Service (EGMS) dataset using a set of Persistent Scatterers (PS) in an urban control area through two analytical approaches (EGMS protocol and PSDefoPAT(2023)). The results showed high consistency, with 82–85% of points classified as highly reliable. Subsequently, this control group was compared with PS from each landfill type (active sanitary, operational inert, and closed inert). Significant deformation differences were found in each landfill type: the active sanitary landfill exhibited distinct anomalies depending on orbit, with strong residual variance instability detected (p < 0.003) in both. Operational inert landfills showed significant anomalies (p < 0.001) in both orbits with variable stability, while closed inert landfills displayed strong stability (p > 0.7) and variable anomalies. These results confirm the efficacy of InSAR approaches for detecting active landfill zones to aid in locating illegal or unauthorized dumping sites and to direct in situ inspection planning. Full article

Prior studies have shown that socio-economic and structural risks can be correlated with earthquake effects. The quantification of these effects was used to formulate robust disaster risk reduction (DRR) strategies and building codes. This is more pronounced in countries with complex tectonic settings, such as the Philippines, where strong-to-major earthquakes can occur. Here, we report the evaluation of deterministic ground shaking (GS) intensity measurements for Camarines Norte, the Philippines, with the objective of assessing and mapping the susceptibility of communities to intense ground motion. GS intensities and peak ground acceleration (PGA) were computed using the Rapid Earthquake Damage Assessment System (REDAS) software developed by the Philippine Institute of Volcanology and Seismology (PHIVOLCS). The PGA was computed as a fraction of acceleration due to gravity, while GS used the PHIVOLCS Earthquake Intensity Scale (PEIS). Simulations were based on recorded earthquakes and mapped active faults near the province. Geographic information systems were used to stack and refine each simulation. Results showed that 13 earthquakes and 13 seismic source zones classified most of the province as PEIS VIII or higher, with the PGA maximum at 0.66 g. The results implied that the province is susceptible to very destructive to completely devastating ground shaking, and it is recommended to incorporate these results into DRR policymaking. Full article

The Tuzla Submarine Landslide represents one of the most significant mass-wasting features associated with the active North Anatolian Fault Zone (NAFZ). The failure surface geometry and sediment stratigraphy indicate the presence of a mechanically weak, saturated layer that may become unstable under strong seismic loading. This study presents a comprehensive geotechnical evaluation of the Tuzla Submarine Landslide. Based on regional sediment properties, the landslide was characterized and modeled with an estimated volume of 0.015 km³ and an average slope angle of 14°. The submarine landslide potential was investigated through re-analysis of seismic, geotechnical, and bathymetric datasets. Finite Element Method (FEM) simulations were conducted to model the seismic slope failure. Based on these analyses, the seismic slope displacements, stress distributions, and equivalent plastic strains were identified. The estimated landslide displacements under varying seismic acceleration scenarios corresponding to three major earthquakes ranged between 2.38 m and 4.12 m, depending on the triggering ground motion and slope stability conditions. These findings highlight that reactivation of the Tuzla submarine landslide, potentially triggered by a future large earthquake along the NAFZ, could pose a moderate landslide hazard to the coastal settlements bordering the Marmara Sea. Full article

Land subsidence and coseismic deformation can interact in groundwater-stressed sedimentary basins, yet basin-scale identification of event-timed vertical offsets in InSAR products requires explicit control of referencing and processing effects. This study evaluates whether the 27 September 2021 Arkalochori earthquake (Mw 6.0; central Crete) produced detectable coseismic vertical offsets within the Messara Basin by applying a reproducible screening workflow to Copernicus European Ground Motion Service (EGMS) Level-3 Vertical time series, from two processing generations (EGMS 2015–2021 and EGMS 2018–2022). An event-centered step metric (stepEQ), defined as the difference between post-event and pre-event mean displacements over a fixed acquisition window, is evaluated across three fixed spatial masks (MESSARA, R15060, R8750) together with a dispersion-based precision proxy (σstep) and a cross-generation sensitivity diagnostic (ΔstepEQ). A supplementary 2 + 2 subset sensitivity analysis indicates that the adopted fixed 3 + 3 estimator is stable at the basin scale, with sensitivity concentrated mainly in threshold-adjacent cases. Results indicate that Arkalochori-related offsets are not expressed as a basin-wide step across Messara; instead, non-background responses form a spatially limited and coherent subset concentrated where the basin intersects the near-source footprint. In EGMS 2018–2022, the higher vertical offset class (C2; |stepEQ| > 40 mm) is exclusively subsidence-direction and is enriched toward the screening center (up to ~19% within the radii mask R8750 m) but remains sparse at the basin scale mask (MESSARA mask) (~1%). Step-dominated points co-locate with strongly subsiding mean vertical velocity regimes and are hosted almost entirely by post-Alpine basin deposits, indicating strong material and background-deformation conditioning of step detectability. Cross-generation comparison shows basin-scale stability of background behavior but localized near-source sensitivity, supporting use of ΔstepEQ as a Quality Control (QC) lens for threshold-adjacent interpretations. The workflow provides a transparent, transferable approach for prioritizing candidate coseismic-step locations in EGMS time series. Results are interpreted as screening-level evidence in the derived vertical signal using event timing, spatial coherence, and QC diagnostics. Full article

Balancing structural safety and economic efficiency in super-tall building design remains a formidable challenge. To address this issue, this study proposes a genetic-algorithm-based multi-variable, multi-objective optimization method. The design variables include the member sizes and vertical layout positions of outrigger and belt trusses, as well as the cross-sectional dimensions of mega-columns. Total structural weight and maximum inter-story drift ratio are adopted as objective functions, while code-specified constraints, such as shear-weight ratio, stiffness-weight ratio, and axial compression ratio, are incorporated to formulate the fitness evaluation for optimization. Taking a 300 m baseline structure designed for 6-degree seismic intensity and equipped with two outrigger trusses and three belt trusses as an example, single-variable sensitivity analyses are first performed. The results show that optimizing any single parameter can yield certain local improvements, yet it cannot overcome the weight–deformation trade-off induced by strong variable coupling. By selecting representative feasible solutions from the multi-variable solution set that match the “optimal” values identified by single-variable optimization as benchmarks, the multi-variable optimum reduces the total structural weight by approximately 6.5–18.4% relative to these representative designs. Moreover, optimal layout strategies of outrigger and belt trusses are investigated for two typical building heights (200 m and 300 m) and two seismic intensity levels associated with design ground motions having a 10% exceedance probability in 50 years, namely 6-degree (0.05 g) and 8-degree (0.20 g). Finally, the proposed method is validated through a case study of a super-tall financial center in Chongqing, where the total structural weight is reduced by 12.3% after optimization while the inter-story drift ratio still satisfies relevant code requirements. The results demonstrate that the proposed framework can generate competitive feasible solutions and provide a systematic means to achieve a balanced trade-off between structural safety and economic efficiency for outrigger–belt-truss super-tall buildings. Full article

Water-surface perception is critical for autonomous surface vehicle navigation, where reliable tracking of task-relevant objects is essential for safe and robust operation. Referring multi-object tracking (RMOT) provides a flexible tracking paradigm by allowing users to specify objects of interest through natural language. However, existing RMOT benchmarks are mainly designed for ground or satellite scenes and fail to capture the distinctive visual and semantic characteristics of water-surface environments, including strong reflections, severe illumination variations, weak motion constraints, and a high proportion of small objects. To address this gap, we introduce Refer-ASV, the first RMOT dataset tailored for ASV navigation in complex water-surface scenes. Refer-ASV is constructed from real-world ASV videos and features diverse navigation scenes and fine-grained vessel categories. To facilitate systematic evaluation on Refer-ASV, we further propose RAMOT, an end-to-end baseline framework that enhances visual–language alignment throughout the tracking pipeline by improving visual–language alignment and robustness in challenging maritime environments. Experimental results show that RAMOT achieves a HOTA score of 39.97 on Refer-ASV, outperforming existing methods. Additional experiments on Refer-KITTI demonstrate its generalization ability across different scenes. Full article