Search Results (267)

An integrated numerical method is proposed for analyzing the nonlinear seismic response of near-fault building clusters, comprising three algorithms: (1) a structural investigated lump algorithm for elastoplastic dynamic response of structure; (2) a connecting investigated lump algorithm for bidirectional wave propagation between the site and elastoplastic building clusters; (3) a geomedia investigated lump algorithm for seismic wave propagation with an improved viscoelastic constitutive model, which allows independent definition of P/S-wave quality factors to characterize geomedia attenuation. Validated for its capability in simulating site-city dynamic interaction problems via a shaking table test, the method is applied to study the seismic response of near-fault building clusters in Xichang City under a hypothetical M_w6.8 earthquake. It is shown that irrespective of whether shallow geological structures are considered, clusters (c2–c4) situated in rupture-forward surface area within ~1.5 km of the fault trace entered the elastoplastic stage, while others (c1, c5) remained elastic. Shallow geological structures may reverse locally hanging-wall/footwall effects of both near-fault structural seismic response and ground motion. A notable seismic-response characteristic of near-fault structures undergoing the elastoplastic stage is that the permanent structural motion displacement (PSMD) at the slab of a specific floor incorporates not only the non-zero permanent ground motion displacement (PGMD) but also the non-zero final structural residual displacement (FSRD) relative to the supporting ground. The developed method could provide support for seismic damage assessment, site selection, and structural optimization design of near-fault building clusters. Full article

Surface subsidence induced by underground coal mining is a primary driver of ecological degradation. The traditional Remote Sensing Ecological Index (RSEI), however, struggles to capture surface deformation constraints and vegetation response lags. To address this, we developed a Mining Deformation–Ecology Coupling Index (MDECI). This index integrates Interferometric Synthetic Aperture Radar (InSAR)-monitored surface stability with multi-spectral indicators via Principal Component Analysis (PCA). We applied this method to the Datong Coalfield, China, using 231 Sentinel-1A SAR scenes and 8 Landsat images (2017–2024) to validate the effectiveness of the index. Meanwhile, we systematically analyzed non-linear response mechanisms, the Ecological Turning Point (ETP), and spatial clustering characteristics. The results demonstrate the following: (1) InSAR and MDECI effectively identified patterns of surface subsidence and ecological decline. Subsidence centers expanded to a maximum of −2085 mm, causing the mean MDECI in these areas to drop to 0.185 (<−1800 mm). This represents a 57.4% decrease relative to the regional average (0.434). (2) MDECI outperformed traditional models with a stable Average Correlation Coefficient (ACC) (0.63–0.75) and high cross-correlation coefficients with RSEI (0.906) and the Mine-specific Eco-environment Index (MSEEI) (0.931). During the 2018 drought, MDECI maintained a robust ACC of 0.628 while RSEI dropped to 0.482. (3) Multi-scale analysis revealed a unimodal MDECI response with an ETP at −100 mm. Initial ‘micro-disturbance gain’ (0.371 to 0.471) is followed by a progressive decline to a minimum of 0.185 under severe deformation. (4) Local Indicators of Spatial Association (LISA) spatial clustering characterized the distribution patterns of ecological damage and localised high-maintenance areas. High–Low damaged areas accounted for 5.09%, while High–High high-maintenance areas reached 9.00%. The scale of High–High areas was approximately 1.77 times that of the damaged areas. The MDECI addresses the deficiencies of traditional indices in high-disturbance areas and isolates the impact of mining on the ecology, providing a quantitative basis for risk identification and differentiated restoration. Full article

Composite materials are critical components in advanced equipment such as aerospace, however, delamination defects that readily arise during manufacturing and in service present serious risks to equipment safety. Terahertz non-destructive testing is highly effective for analyzing the internal structure of composite materials, making it an effective approach for precise identification of delamination defects. Current terahertz detection approaches mainly depend on single domain features, making it difficult to capture complementary information from both the time and frequency domains. To address this, a Time-Frequency Feature-fusion Network (TFFN) is proposed. In this network, a three-branch architecture is employed: local transient patterns and pulse-related structural features are extracted by the local time-frequency branch; damage-sensitive frequency bands are focused on by the frequency-domain branch through a channel-space-frequency band attention mechanism; and deep integration of time-frequency features is achieved by the time-frequency fusion branch using Manifold Mixup. Finally, the features extracted from the three branches are adaptively fused via a cross-branch attention mechanism, and defect identification is accomplished by the classifier. Experimental results show that this method achieves accuracies of 98.40% on the glass fiber reinforced polymer (GFRP) dataset and 98.63% on the quartz fiber reinforced polymer (QFRP) dataset, surpassing the best existing method by 2% and 1.25%, respectively. A substantial improvement in both defect identification accuracy and the model’s generalization ability for layered structures is thereby achieved. Full article

This study presents a methodology for determining the optimal placement of seismic sensors along the height of buildings to minimize the uncertainty in reconstructing structural responses at non-instrumented floors. Due to the extensive benefits of instrumentation—from model validation to damage detection and structural health monitoring—the number of instrumented structures is steadily increasing. However, to keep installation and maintenance costs within a reasonable range, structures are often instrumented sparsely. The response at non-instrumented locations is typically estimated using deterministic or probabilistic model-based, data-driven, or hybrid methods. Specifically, the authors recently proposed a method that combines a deterministic beam model with Gaussian Process Regression (GPR) to estimate responses at non-instrumented floors of an instrumented building. The present paper proposes a methodology to determine optimal sensor locations that minimize the uncertainty associated with this response estimation. This work is a sequel to a previous study that was limited to stationary excitation and extends the method to seismic excitations. The methodology is first verified through a numerical example and then applied to two real instrumented buildings. The results demonstrate that an average 40% reduction in uncertainty is achievable when sensors are positioned according to the proposed optimization approach, in comparison with a random distribution of sensors. Between the two real-life cases studied in this paper, the level of reduction in the response uncertainty is around 10% for the 52-story building because the existing sensors are almost uniformly distributed, while it is around 80% for the 73-story building because the existing sensors are distributed to measure the localized behavior of the building. Full article

This study investigates the thermo-mechanical response of geocell-reinforced concrete pavements through scaled model tests and three-dimensional finite element analyses. Static, thermal, traffic, and coupled temperature–loading tests were conducted to clarify the deformation evolution, strain distribution, and damage-related response of the reinforced structure. The results show that, under static loading, pavement settlement evolves through three stages, namely initial compaction, plastic development, and stable strengthening, indicating progressive mobilization of geocell confinement. Under thermal loading, slab strain exhibits pronounced spatial and temporal non-uniformity, and the slab center is identified as the thermally sensitive zone. Under coupled temperature–loading conditions, both strain and settlement show a non-monotonic response near 1.1–1.3 kN, suggesting a potential damage-initiation range. Post-test crack observations further provide direct qualitative evidence that local cracking damage occurred in the slab under representative loading conditions. Under traffic loading, permanent deformation accumulates with load repetitions and is highly sensitive to load amplitude, indicating a load-sensitive transition in cumulative deformation behavior rather than a definitive fatigue threshold. Numerical results further show that geocell reinforcement reduces central settlement by 17.4% relative to plain concrete pavement and by 7.6% relative to doweled pavement, while producing a smoother deflection basin and a more uniform stress distribution. Parametric analyses indicate that the optimum geocell height is approximately one-third of the slab thickness; beyond this range, the marginal reinforcement benefit decreases. Overall, the results demonstrate that geocell reinforcement can effectively improve load transfer, deformation compatibility, and thermo-mechanical stability of concrete pavements under the investigated conditions. Full article

The prediction of dynamic responses of offshore wind turbine foundations under wind-wave-current multi-field coupled loads is the cornerstone of safety in offshore wind power engineering. The currently widely adopted equivalent load application method, while computationally efficient, simplifies loads into concentrated forces applied at the pile top and tower top, neglecting fluid-structure dynamic interaction mechanisms, which leads to deviations in response predictions. To overcome this limitation, this paper proposes a high-precision bidirectional fluid-structure interaction numerical framework. The fluid domain employs computational fluid dynamics (CFD) to construct an air-seawater two-phase flow model, utilizing the standard k-ε turbulence model and nonlinear wave theory to accurately simulate complex marine environments. The solid domain establishes a wind turbine-stratified seabed system via the finite element method (FEM), describing soil-rock mechanical properties based on the Mohr-Coulomb constitutive model. Comparative studies indicate that the equivalent static method significantly underestimates the displacement response of pile foundations, particularly under the extreme shutdown conditions examined in this study. This value should be interpreted as a case-specific observation rather than a universal deviation, and the discrepancy may vary with sea state, wind speed, current velocity, and wind–wave misalignment, thereby leading to non-conservative estimates of stress distribution. In contrast, the fluid-structure interaction method can reveal key physical processes such as local flow acceleration and wake–interference effects around the tower and the parked rotor under shutdown conditions, and the nonlinear interaction and resistance-increasing mechanisms between waves and currents. This model provides a reliable tool for safety assessment and damage evolution analysis of wind turbine foundations under extreme marine conditions, promoting the transformation of offshore wind power structure design from empirical formulas to mechanism-driven approaches. Full article

Rock fracture behavior is strongly influenced by grain size and boundary effects, which complicate the determination of fracture parameters and the interpretation of size-dependent failure. This study investigates the fracture behavior of sandstone and diorite within the framework of the boundary effect model (BEM) using three-point bending tests, acoustic emission (AE), and digital image correlation (DIC). By varying the prefabricated crack length, different values of the structural geometric parameters a_e were obtained, and the fracture toughness K_IC and tensile strength f_t were identified by regression analysis. The results show that K_IC = 0.6841 MPa·m^0.5 and f_t = 4.5625 MPa for sandstone, whereas K_IC = 2.7233 MPa·m^0.5 and f_t = 21.8218 MPa for diorite. Increasing the prefabricated crack length reduces the peak load and prolongs the pre-peak damage evolution stage. Diorite, with a larger average grain size, exhibits higher AE energy release, a higher proportion of high-energy AE events, and a larger fracture process zone (FPZ) than sandstone. Moreover, the AE energy distribution along the crack propagation direction shows a distinct “three-stage” characteristic, consistent with the non-uniform distribution of local fracture energy g_f predicted by boundary effect theory. The results indicate that BEM can reasonably characterize the fracture behavior of rocks with different grain sizes, and the identified material parameters can be used to construct a BEM-based structural failure curve for estimating nominal failure stress over a wider range of structural geometric parameters. Full article

With the rapid development of the aviation industry, engines operate under extreme conditions of high temperature, high pressure, and high vibration, making them prone to surface damage such as ablation. Ablation not only affects the structural integrity of engine components but also threatens flight safety, making efficient and accurate detection of paramount importance. Traditional detection methods rely on manual visual inspection and non-destructive testing, which suffer from high subjectivity and low efficiency. In recent years, deep learning has achieved significant progress in industrial defect detection. However, conventional CNN-and Transformer-based architectures still suffer from substantial computational overhead and inadequate boundary segmentation accuracy in aero-engine ablation detection. This paper proposes a novel dual-pathway network Visual State-Space Residual Neural Network (VSS-ResNet) based on Mamba that combines Visual State Space (VSS) modules with ResNet50. This architecture leverages the global modeling capability of VSS modules and the local feature extraction capability of CNNs, effectively enhancing the accuracy and robustness of ablation boundary detection with the support of multi-scale feature fusion modules. Experimental results demonstrate that the proposed method achieves superior performance in mIoU, mPA, and Acc compared to mainstream segmentation models such as U-Net, Pyramid Scene Parsing Network (PSPNet), and DeepLab V3+ on a self-constructed engine endoscopic ablation dataset, validating its potential in intelligent aero-engine inspection. Full article

Under non-independent and identically distributed (Non-IID) conditions, significant variations exist in local model updates across clients and training phases during the collaborative modeling process of differential privacy federated learning (DP-FL). Fixed clipping thresholds and noise scales struggle to accommodate these diverse update differences, leading to mismatches between local update intensity and noise perturbations. This imbalance results in data privacy leaks and suboptimal model accuracy. To address this, we propose a differential privacy federated learning method based on adaptive clipping thresholds. During each communication round, the server adaptively estimates the global clipping threshold for that round using a quantile strategy based on the statistical distribution of client update norms. Simultaneously, clients adaptively adjust their noise scales according to the clipping threshold magnitude, enabling dynamic matching of clipping intensity and noise perturbation across training phases and clients. The novelty of this work lies in a quantile-driven, round-wise global clipping adaptation that synchronizes sensitivity bounding and noise calibration across heterogeneous clients, enabling improved privacy–utility behavior under a fixed privacy accountant. Using experimental results on the rail damage datasets, our proposed method slightly reduces the attacker’s MIA ROC-AUC by 0.0033 and 0.0080 compared with Fed-DPA and DP-FedAvg, respectively, indicating stronger privacy protection, while improving average accuracy by 1.55% and 3.35% and achieving faster, more stable convergence. We further validate its effectiveness on CIFAR-10 under non-IID partitions. Full article

In many densely populated towns and semi-urban areas, masonry buildings often stand close to busy roads, exposing them to blasts from improvised explosives or other localized sources. Such structures are rarely designed to resist sudden explosive forces, making severe damage or even progressive collapse likely. Even moderate-intensity blasts can weaken walls, endanger occupants, and cause significant property loss. Unlike reinforced concrete, masonry is highly susceptible to explosive impact. Therefore, understanding how these buildings behave under blast loads and developing affordable protection methods is crucial. Low-rise unreinforced masonry (URM) structures, usually up to about 13 m in height (roughly 2–4 stories), common in villages, semi-urban regions, and conflict-prone zones, are particularly at risk. In many areas, these poorly constructed buildings lack proper engineering design and are therefore highly vulnerable to blast damage. Non-load-bearing internal dividers and perimeter enclosures are especially prone to lateral displacement, which can initiate instability and, in severe cases, lead to overall structural failure. This research focuses on reducing catastrophic damage in URM walls when exposed to close-proximity blast forces using concrete-based protective coatings, both with and without embedded steel-welded wire mesh. The study references a previously tested laterally supported clay brick wall built with cement–sand mortar as the baseline model, with its behavior validated against experimental findings from existing literature. Two blast cases were considered corresponding to scaled stand-off distances of 2.19 m/kg^1/3 and 1.83 m/kg^1/3, representing moderate flexural-shear cracking and full structural failure, respectively. To replicate the observed behavior, a comprehensive 3D numerical simulation was developed using the ABAQUS/Explicit 2020 solver. The model’s predictions were benchmarked and verified through comparison with reported test data. While both blast intensities were used to confirm computational accuracy, the effectiveness of UHPC and UHPFRC protective coatings with and without embedded wire mesh was specifically evaluated under the more severe collapse scenario (Z = 1.83 m/kg^1/3). Results indicated that at a scaled distance of 1.83 m/kg^1/3, the uncoated URM wall could not withstand the blast because of poor tensile and bending capacity. In contrast, the UHPC- and UHPFRC-coatings provided improved confinement and better stress distribution. When welded wire mesh was embedded, crack control improved further, the interface bond strengthened, and a larger portion of blast energy was absorbed and dissipated. Full article

Buried oil and gas pipelines, the critical arteries of global energy infrastructure, are increasingly vulnerable to severe geological hazards such as reverse faulting, yet their structural integrity is often pre-compromised by stochastic corrosion damage accumulated during service. However, quantifying the coupled impact of spatial corrosion heterogeneity and large ground deformation remains a formidable challenge due to the complex nonlinearities involved in soil–structure interactions and wall thinning. This study establishes a probabilistic assessment framework integrating random field theory, nonlinear finite element analysis, and a generative conditional diffusion model to characterize realistic 2D non-Gaussian corrosion morphologies. The numerical results reveal a significant geometric stiffening effect induced by internal pressure, where moderate operating levels effectively suppress cross-sectional distortion by counteracting the Brazier effect. Consequently, this mechanism facilitates a fundamental transition in failure modes from localized tensile rupture to ductile buckling, significantly extending the critical fault displacement threshold. Furthermore, probabilistic fragility analysis demonstrates that the spatial dispersion of pitting, rather than just average wall thinning, governs the initiation of premature failure. Mechanistic analysis indicates that high internal pressure, while providing pneumatic support, exacerbates tensile strain localization at corrosion pits, leading to a heightened probability of premature rupture under minor fault deformations, a critical hazard that traditional deterministic models significantly underestimate. These findings provide a quantitative theoretical foundation for the reliability-based design and maintenance of energy lifelines traversing active tectonic zones. Full article

To address the challenge of identifying damage in the hangers and bridge deck systems of long-span suspension bridges, this paper proposes a non-contact monitoring method based on video image recognition. This method extracts structural vibration displacement responses through video acquisition and image analysis, and combined with the strain mode change rate index, it achieves damage localization, type identification, and severity assessment. The principle of extracting displacement time-history data from video images is first elaborated, and MATLAB-based computational code is developed, including pixel tracking and time-history curve generation methods. The eigensystem realization algorithm is used to identify displacement mode shapes, which are then converted into strain mode shapes via the central difference method. The strain mode change rate and its deviation rate are proposed as damage indicators: under undamaged conditions, the curve is smooth; at damage locations, peaks appear; the distribution range of peaks can distinguish between hanger damage and bridge deck cracks; the deviation rate quantifies damage severity. The feasibility of the method is validated through finite element simulations and physical model experiments. The results show that hanger damage causes broad peaks, while bridge deck cracks present narrow peaks; the deviation rate increases monotonically with damage severity. Applied to an in-service suspension bridge, the method successfully identified hanger bending and weld cracking, with assessment results consistent with on-site inspections. This study demonstrates that the strain mode change rate analysis based on video images enables damage identification without prior knowledge of the structural health state, relying solely on the damaged state response. Offering advantages such as non-contact measurement, full-field monitoring, and no need for sensor deployment, it provides a new technical approach for the long-term monitoring of suspension bridge hanger systems. Full article

With the continuous advancement of autonomous driving technology, lane marking-based environment perception has become a critical component of autonomous vehicle systems. However, long-term vehicle loads cause road markings to deteriorate and fade, significantly compromising driving safety. Traditional road marking quality inspection methods are inefficient and struggle to achieve high-performance, convenient detection. To address these challenges, this paper proposes an integrated framework for road marking detection and evaluation using Unmanned Aerial Vehicle (UAV) imagery. The framework comprises three core modules: lightweight data acquisition, efficient marking extraction, and accurate distress assessment. First, optimized UAV flight parameters enable low-cost, highly flexible, and safe data collection. Second, the YOLOv8-MEB model, combined with instance segmentation screening and local image optimization, achieves lane segmentation precision and recall above 90% with FPS exceeding 60. Furthermore, a standard marking template library is constructed, and a RANSAC-based template matching method with affine transformation is employed to restore intact marking shapes. A contour correction strategy is introduced to mitigate errors induced by construction inaccuracies. The proposed framework supports nine common types of road markings and yields approximately 10% error in distress ratio calculation under non-severe damage conditions, providing a practical technical reference for intelligent road maintenance. Full article

As a critical barrier for power network safety, insulating materials are susceptible to internal microcracks, delamination, and other hidden defects that can trigger dielectric strength degradation and space charge accumulation, ultimately leading to insulation breakdown. Ultrasonic shear wave non-destructive testing enables defect identification without damaging the material. Therefore, this paper focuses on the identification and imaging of internal defects in insulating components using ultrasonic shear waves. First, a physical model for ultrasonic shear wave NDT is established. Based on the refraction and reflection characteristics of ultrasonic waves in materials with different acoustic impedances, a defect localization formula is derived. Through simulation verification, for the three defects set at different positions in the defect model, the positioning error is less than 0.5 mm. Subsequently, defects such as circular holes, triangular shapes, cracks, and bottom grooves were simulated. Analysis of the echo data revealed a correlation between the distance from the sensor to the defect and the echo amplitude. For groove defect imaging, the differential SAFT algorithm was employed, achieving a width error of 1 mm for imaging a 2 mm wide by 5 mm high groove, clearly presenting the defect morphology. Finally, an imaging software program for defect structure reconstruction was developed based on the simulation model presented in this article. We collected side and back view data through the constructed ultrasonic transverse wave non-destructive testing experimental platform, and visualized defects in insulation materials with grooves using this ultrasonic imaging program. This study achieved defect localization and imaging through simulation of various defect types combined with synthetic aperture focused imaging algorithms, providing a reference for visualization and industrial application of ultrasonic shear wave non-destructive testing technology. Full article

In the last decades, ozone (O₃)-based medical treatments have become a widely applied complementary therapy for several pathological conditions. O₃ is administered at low dosages since the induction of a mild oxidative stress does not cause damage but stimulates the antioxidant cell response through the nuclear factor erythroid 2-related factor 2 (Nrf2). Mitochondria are sensitive to even mild oxidative stress, thus being a responsive target for O₃. This study aimed to evaluate the mitochondrial response to low O₃ doses used for medical treatments. As the skeletal muscle represents a primary target in local O₃ treatments, a murine non-tumoral muscle cell line was selected as an appropriate in vitro model. Transmission electron microscopy, biochemistry, and flow cytometry provided original information on the O₃ dose-dependent modifications of mitochondrial structural and molecular features. Low O₃ doses promoted an increase in mitochondrial area and in cristae extension, as well as an enhancement of the electron transport chain complexes and of antioxidant catalase and manganese-dependent superoxide dismutase. Nrf2 maintained its association with the outer mitochondrial membrane, thus exerting its protective role. All mitochondrial modifications were observed 24 h after treatment and disappeared after 48 h, demonstrating that cells promptly respond to the O₃-driven oxidative stress, effectively restoring homeostasis. Full article