Search Results (555)

The generation of synthetic seismic accelerograms is a critical problem in earthquake engineering, where the scarcity of strong-motion records, particularly for high-magnitude and near-fault scenarios, limits the reliability of structural analyses and probabilistic seismic hazard assessments. This paper presents a proof-of-concept wavelet-decomposed conditional Generative Adversarial Network (WD-cGAN) for the synthesis of seismic accelerograms that reproduce the physical and statistical properties of real ground-motion records. Unlike prior GAN-based approaches that rely on Fourier-domain decomposition, the proposed architecture decomposes each training signal into N wavelet sub-bands (experimentally $N=7$, six detail sub-bands D1–D6 and one approximation sub-band A6) using the Daubechies-4 (db4) discrete wavelet transform (DWT), assigning each sub-band to a dedicated discriminator. A novel energy-based weighting scheme ${\alpha }_i$ modulates the relative contribution of each discriminator to the total generator loss, ensuring that physically dominant, low-frequency bands, which carry the bulk of seismic energy, receive proportionally higher training emphasis. Seismic moment magnitude $M_w$ serves as the primary conditioning variable, enabling targeted synthesis for specific hazard scenarios. The model is implemented in Python v3.9 using PyTorch v.2.10 and trained on accelerograms drawn from the Italian INGV/ITACA v4.0 archive. Preliminary evaluation on 500 synthetic accelerograms across five magnitude classes provides evidence that the proposed wavelet-domain multi-discriminator scheme reproduces the essential spectral shape and non-stationary temporal structure of real ground-motion records within the considered magnitude range; full quantitative validation on a larger and more diverse corpus, rigorous comparison with competing methods, and extended multi-parameter conditioning are identified as the principal avenues for future work. 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

Highway slope instability under coupled traffic and environmental loading poses critical threats to transportation safety in mountainous regions, where dynamic vehicular forces interact with complex geological conditions in ways that single-modality monitoring cannot fully resolve. This study proposes MMDF-DEWS, a multi-modal data fusion and deep learning-based early-warning system that, for the first time, treats quantified traffic-loading parameters as a first-class input modality alongside Interferometric Synthetic Aperture Radar (InSAR) displacement, Global Navigation Satellite System (GNSS) measurements, and embedded geotechnical sensor outputs. A hybrid Transformer–bidirectional LSTM backbone with hierarchical attention-guided fusion enables the model to capture both long-range temporal deformation trends and short-term dynamic responses triggered by heavy-vehicle passage. To guard against over-fitting on a limited number of instability events, we adopt chronological training/validation/test partitioning, five-fold cross-validation for hyper-parameter selection, stratified focal-loss training, and cross-dataset evaluation on two independent public benchmarks: the Three Gorges Reservoir Area Landslide Monitoring Dataset (TGRA-LMD) and the European Ground Motion Service Sentinel-1 (EGMS-S1) dataset. The framework outperforms six state-of-the-art baselines by 4.7–11.2% in F1-score, and ablation studies confirm that the explicit inclusion of traffic-loading features alone improves Warning-class recall by 6.3 percentage points, demonstrating a direct and physically grounded link between cyclic vehicular loading and slope-state prediction. The system satisfies operationally relevant engineering targets for warning lead time and false-alarm rate, and provides interpretable attention maps suitable for transportation-authority decision support. Full article

This paper presents the mechatronic design, mathematical modeling, parameter identification, and nonlinear position control of an open-architecture biaxial shake table capable of generating base acceleration along two orthogonal horizontal directions. The shake table is tailored for engineering research and education. Addressing the limitations of proprietary “black-box” systems, the platform is constructed using standard industrial components (HLTNC-CNC modules and NEMA 23 BLDC motors) to ensure reproducibility. A core contribution is the characterization of the system’s nonlinear dynamics to enhance tracking fidelity. The mathematical model, derived via the Euler–Lagrange formulation, incorporates viscous and Coulomb friction phenomena, which are critical for accurately reproducing zero-velocity crossings in seismic signals. System parameters are identified using the Recursive Least Squares (RLS) algorithm combined with State Variable Filters (SVFs) to process the regression vector. To enable precise closed-loop performance, a nonlinear state observer incorporating the identified friction dynamics is designed for velocity estimation. Furthermore, a Computed Torque Control (CTC) strategy is synthesized and compared against a conventional Proportional-Velocity (PV) controller. Experimental validations using historical ground motions, including the 1986 Colima earthquake, confirm that the CTC strategy reduces the maximum absolute tracking error by more than 75% compared to the PV approach, bounding the peak error to $0.36\mathrm{mm}$ across both axes. Furthermore, in high-amplitude scenarios, the proposed model-based approach achieved an RMS tracking error reduction of more than 83%. These results validate the proposed platform as a reliable and accessible tool for structural dynamics testing. Full article

Hysteretic energy, a critical component of seismic input energy, is predominantly dissipated through the hysteretic behavior of structural members in most conventional structures. The motivation is to establish the conditional probabilistic model of normalized hysteretic energy of the structure after determining its displacement, thereby facilitating the estimation of the Park–Ang damage index. This study develops a probabilistic model for normalized hysteretic energy conditional on the ductility ratio. Three macroscopic hysteretic models, representative of the hysteretic behavior of distinct structural types, are employed to quantify the effects of ground motion characteristics (e.g., magnitude, distance, pulse, duration, and site conditions) and structural properties (e.g., post-yield stiffness and damping ratio). The findings reveal that a lognormal distribution effectively characterizes the normalized hysteretic energy. Among the investigated parameters, ground motion duration leads to a significant influence on the distribution of normalized hysteretic energy (maximum difference up to 30%). To facilitate practical applications, a set of predictive expressions is proposed to estimate the mean and standard deviation of normalized hysteretic energy. The resulting conditional distribution reproduces the empirical distribution derived from the original data, with an average error of approximately 5%. Using established expressions, the required ductility capacity under specified performance objectives can be probabilistically determined in seismic design. Moreover, the established distribution can be used to determine the potential hysteretic energy of the structure for assessing its damage state after an earthquake, as demonstrated through a full-scale shaking table test. 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 study presents a seismic fragility analysis of reinforced concrete column–steel beam (RCS) frame structures using an enhanced version of the Park–Ang damage model. The applicability of various seismic intensity measures (IMs) in fragility assessment was evaluated. Furthermore, a two-parameter IM was refined through simulated annealing optimization. Initially, the damage evolution of the structure under both near-field and far-field ground motions was investigated using a modified Park–Ang model tailored for RCS systems from the literature. Subsequently, seismic fragility was assessed through multiple stripe analysis, developing fragility curves for distinct damage limit states under the two ground motion types. The effectiveness of 22 different IMs was then examined across these limit states. A two-parameter IM that accounts for the softening period was identified as particularly effective in capturing ground motion uncertainty. This measure was further optimized by applying simulated annealing to minimize the record-to-record variability (β_RTR), targeting its period coefficient (n) and weighting factor (α). Finally, the enhanced IM’s sufficiency and scaling robustness were validated. Results indicate that near-field ground motions induce considerably more severe damage in RCS frames compared to far-field motions, with damage concentrating in lower stories. The optimized IM achieved reductions in β_RTR ranging from 8.7% to 38.1% across different damage states. Full article

This paper investigates time-varying output formation (TVOF) tracking for heterogeneous small fixed-wing unmanned aerial vehicles (UAVs) and nonholonomic unmanned ground vehicles (UGVs). The small fixed-wing UAVs operate in three-dimensional space, and the UGVs move on a two-dimensional plane, leading to heterogeneous dynamics with nonholonomic constraints, asymmetric velocity constraints, and input saturation. To address these challenges, distributed adaptive control protocols are developed under switching directed communication topologies. Unlike existing TVOF tracking methods that require global information, the proposed protocols do not rely on the upper bound of the leader’s unknown input or the eigenvalues of the Laplacian matrix. A constructive parameter-selection algorithm is provided, and the closed-loop stability is established using Lyapunov theory. Numerical simulations involving heterogeneous UAV-UGV formations verify that the proposed method achieves TVOF tracking under random disturbance while satisfying the prescribed motion constraints. Full article

Investigating the fundamental link between microscopic vehicular motion parameters and macroscopic traffic flow states is pivotal for advancing refined traffic state estimation research and propelling the progression of Intelligent Transportation Systems. In this paper, a basic Transformer model has been optimized and extended by incorporating embedding and pooling layers, and the model’s hyperparameters have been finely tuned through random search cross-validation. The creation of the Generalized Optimized Transformer (GOT) model ensued, where the multi-head attention mechanism adeptly encapsulates all spatiotemporal dynamics inherent in traffic data. Various benchmark models such as LSTM, RNN, and Transformer were put to test, each demonstrating unique performances in managing different traffic flow states. Among them, the GOT model exhibited superior performance, adeptly handling lane-level traffic state estimation tasks derived from microscopic vehicle trajectory data. In conclusion, this research elucidates the intricate and mutable mapping relationship between microscopic vehicular motion parameters and traffic flow states, proficiently executing lane-level traffic state estimation grounded on microscopic trajectory data. This paper is anticipated to provide fresh insights into the understanding of the complex relationship between microscopic vehicular motion parameters and traffic flow states. Full article

Vertical ground reaction force is an important parameter for describing the developmental characteristics of young children’s vertical jumping. However, its application in large-scale physical fitness monitoring and routine teaching practice is greatly limited. Previous studies have used OpenCap to estimate vertical ground reaction force during adult jumping tasks and have provided preliminary validation, but its effectiveness in young children remains unclear. To examine the correlation and agreement of vertical ground reaction force (GRF) estimated by the OpenCap markerless motion capture system during young children’s vertical jumping and to explore the characteristics of vertical GRF estimated by OpenCap during the vertical jump. Kinematic and kinetic data during vertical jumping were synchronously collected from 16 young children using the OpenCap markerless motion capture system and a three-dimensional force platform, with each child completing three trials. Kinematic data were acquired using the OpenCap markerless motion capture system, and the vertical acceleration of the whole-body center of mass was calculated to estimate vertical GRF based on Newton’s second law. Pearson linear correlation analysis and Bland–Altman analysis were used to examine the differences in characteristics between the estimated vertical GRF and the measured vertical GRF. The vertical GRF characteristics estimated by OpenCap showed moderate-to-high correlations with the measured values. Specifically, the time and mean impulse during the push-off phase, flight phase, and landing stabilization phase were highly correlated (r > 0.85), while the peak force and mean force during the push-off phase showed moderate-to-high correlations (r > 0.7). Bland–Altman analysis showed that the bias in time and impulse during the vertical jump was less than 15%, indicating relatively high agreement; however, the bias in peak force during the landing phase exceeded 40%, indicating weak agreement. These results suggest that the OpenCap markerless motion capture system can effectively estimate vertical GRF characteristics during young children’s vertical jumping, with the best performance observed for vertical GRF variables in the push-off phase. The method used in this study may be applied to obtain vertical GRF during young children’s vertical jumping in non-laboratory settings and to assist in evaluating the developmental level of young children’s vertical jump performance. Nevertheless, OpenCap-derived rapid impact variables, particularly landing peak force, should be interpreted with caution. Full article

Recognizing human actions from static images in complex industrial environments remains challenging due to insufficient feature representation and high computational complexity. This issue is particularly critical in power-grid safety monitoring, where improper worker postures (e.g., bending, climbing, falling) can lead to severe accidents and personal injuries, necessitating automated monitoring systems that operate reliably on resource-constrained edge devices. This study proposes a bio-inspired lightweight recognition framework that integrates an improved YOLO-Pose model with a gated recurrent unit (GRU) network. The scientific motivation is grounded in the observation that the human musculoskeletal system achieves highly efficient motion perception through three key mechanisms: hierarchical muscle coordination providing intrinsic rotation invariance, proprioceptive feedback enabling real-time error correction, and selective neural gating reducing redundant information transmission. These biological principles directly inspire our technical contributions: polar-coordinate encoding provides rotation invariance, three-stage filtering mimics proprioceptive feedback, and GRU gating mirrors selective information propagation. Unlike prior approaches that treat pose-based action recognition as a generic computer vision problem, this work explicitly incorporates anatomical structural constraints into the computational pipeline. The framework addresses three research gaps: (1) existing methods lack biomechanically derived invariance properties; (2) GCN-based approaches use fixed topologies that fail to adapt to occlusion patterns; (3) the trade-off between model complexity and accuracy remains unsatisfactory for edge deployment. Experiments on the self-constructed SKPose dataset demonstrate that the proposed method achieves 95.04% accuracy, outperforming ST-GCN by 3.67 percentage points and 2s-AGCN by 1.94 percentage points, with an inference speed of 48 FPS on 8.7 M parameters in underground power-grid environments and provides practical support for biomimetic perception systems and industrial safety monitoring. Full article

This study presents a probabilistic seismic fragility assessment of a jointed rock slope by integrating field characterization, incremental dynamic analysis (IDA), and numerical modeling. Dominant joint sets are identified through field mapping, and key discontinuity parameters are estimated for the Barton–Bandis non-linear shear strength criterion. Dynamic simulations are performed using the distinct element method with the continuously yielding (C-Y) joint model to capture progressive shear degradation. Twenty real earthquake ground-motion records are scaled incrementally to perform IDA, with critical block displacement and cumulative joint slip adopted as engineering demand parameters (EDPs). A probabilistic seismic demand model (PSDM) is developed to correlate peak ground acceleration (PGA) with EDPs. Kinematic analysis indicates that planar failure along joint set 1 is the most likely failure mechanism (90% probability), followed by wedge failure along the intersection of joint sets 1 and 2 (52%). Fragility curves are derived for three displacement-based damage states: minor (1 cm), moderate (5 cm), and severe (15 cm). The results demonstrate that seismic deformation is strongly controlled by discontinuity geometry and progressive joint slip, with the slope exceeding the severe damage state at PGA levels as low as 0.4 g, indicating high seismic vulnerability. This highlights the importance of integrating field characterization with dynamic numerical modeling for reliable seismic stability assessment of such discontinuous rock mass. Future work should incorporate larger datasets, in situ testing, and 3D modeling to enhance assessment reliability. Full article

Hexapod robots are attractive for operation in cluttered and uneven environments, but their walking stability is strongly affected by the coupled effects of leg morphology and foot-end trajectory planning. In many existing designs, leg-segment proportions, reachable workspace, and swing-phase trajectory smoothness are considered separately, which makes it difficult to clarify how structural parameters and motion planning jointly influence locomotion stability. To address this issue, this study presents a spider-leg-inspired hexapod robot with a simplified three-degree-of-freedom leg configuration. Selected functional characteristics of spider legs, including segmented limb structure and compliant distal contact, were abstracted into an engineering-feasible hexapod platform rather than directly reproducing spider anatomy. A parametric workspace analysis was conducted under a fixed total leg length to compare six candidate femur-to-tibia ratios. Based on forward reach, vertical foot-lifting capability, stride potential, and structural compactness, a 4:6 femur-to-tibia ratio was selected. In addition, an eleventh-order Bézier curve was developed for swing-phase foot trajectory planning and compared with a conventional composite cycloid trajectory under identical tripod-gait conditions. Simulation and straight-line walking experiments showed that the Bézier-based trajectory reduced body-attitude fluctuation and produced smoother angular-velocity variation than the composite cycloid trajectory. The results indicate that the proposed structural design and Bézier-based trajectory can improve flat-ground walking stability of the hexapod robot. This work provides a practical reference for biomimetic structural design and gait-trajectory optimization of multi-legged robots, while further validation on more complex terrain remains necessary. Full article

High-precision plant phenotyping requires efficient 3D reconstruction methods with high geometric quality. 3D Gaussian Splatting (3DGS) has recently emerged as a promising approach for real-time 3D reconstruction, achieving impressive visual quality. However, in crop environments dominated by monochromatic and low-texture regions, existing 3DGS methods often produce ambiguous geometries and fail to recover geometry-consistent 3D surfaces. To address these limitations, we propose LV-3DGS (Leafy Vegetables-3DGS), an optimized 3DGS-based framework tailored for the reconstruction of leafy vegetable scenes. First, a blurred reconstruction module is introduced to mitigate reconstruction artifacts caused by camera motion blur during multi-view image acquisition. Second, we propose a planar optimization strategy and design both local and global geometric consistency regularizations to optimize the model, thereby improving the surface reconstruction quality and geometric accuracy. Third, based on an analysis of individual Gaussian contributions, a contribution-based pruning strategy is developed to selectively remove inaccurate geometric components, achieving accurate scene geometry while reducing memory consumption and improving rendering efficiency. In addition, a quantitative geometric evaluation method is proposed for assessing reconstruction quality. Experimental results demonstrate that the proposed method achieves the highest accuracy among the tested baselines, with SSIM, PSNR, and LPIPS reaching 0.94, 34.53 dB, and 0.11, respectively. Moreover, the geometric consistency (GC) metric attains 0.317 cm. Finally, phenotypic parameters are measured from the reconstructed leafy vegetable point clouds. Compared with ground truth measurements, the proposed approach yields coefficients of determination ($R^2$) of 0.9959, 0.9651, and 0.9895 for plant height, leaf number, and leaf area, respectively. These results are significantly outperform to some existing phenotyping methods, providing a new methodology and technical solution for high-precision, low-cost, and high-throughput crop phenotyping. Full article

Transmission tower-line systems spanning active faults are simultaneously subjected to the “dual characteristic seismic actions” of permanent ground displacement (PGD) and spatially varying near-fault ground motions, rendering their failure mechanisms far more complex than those under conventional site-specific seismic actions. This paper investigates a 500 kV double-circuit “two-tower, three-line” coupled system by establishing a high-fidelity finite element model. An analytical framework is proposed, centered on indexing seismic action and structural response by key parameters: “Permanent Ground Displacement–Peak Differential Displacement–Velocity Pulse Period” (“PGD–Δmax–Tp”). By employing synthesized ground motions, the displacement time history is decomposed into three components—a velocity pulse, high-frequency background noise, and permanent displacement—thereby achieving a strict decoupling of these three control variables. Based on this methodology, three sets of controlled-variable scenarios were constructed to systematically reveal the independent influence of ground motion spectral characteristics, permanent displacement, and peak differential displacement on the system’s response. The research indicates that: spectral characteristics modulate the failure mode (the whiplash effect is triggered when the period ratio μ is approximately 1–2, whereas tower leg buckling occurs when μ ≫ 1); a threshold PGD value exists that triggers a shift in the structural force-resisting mechanism; and the peak differential displacement (Δmax) causes the system’s response to transition from being dominated by conductor slackening and unloading to being governed by inertia and P-Δ effects. The insights gained into the asymmetric response characteristics of towers on opposite sides of the fault provide a quantitative reference for the revision of seismic design codes for cross-fault power transmission projects. Full article