Search Results (850)

Currently, clustering algorithms are mainly used to classify fiber-reinforced composite cylinder damage. However, the number of clustering categories is heavily influenced by the evaluation criteria, and the real damage type categorization cannot be determined. Therefore, we propose a semi-supervised algorithm that obtains higher damage classification information with a small number of labels. Specifically, we first performed a phased fiber-reinforced composite cylinder pressurization experiment and collected damage signals through acoustic emission (AE) hits. We analyzed the damage types of the collected burst-type acoustic emission hits (each hit corresponds to a single waveform captured when the hit’s amplitude exceeds the preset threshold) and marked a small number of these hits. Then, we constructed a mean-teacher semi-supervised network structure based on transfer learning, achieving a classification accuracy of 85.92%. Compared to traditional supervised learning and clustering algorithms, the accuracy improved by nearly 30%. Full article

The fractal characteristics of coal pore–fracture networks and their evolution under compression are essential for predicting rock mass failure and fluid transport. This study combines micro-CT scanning with fractal theory and seepage mechanics to investigate the structural evolution of coal under uniaxial compression and its impact on fluid transport. CT scans were performed at four characteristic stages (initial, elastic, plastic, and failure) to reconstruct three-dimensional fracture networks. Quantitative analysis reveals that fracture porosity increases sequentially from 0.44% to 5.01%, with the failure stage reaching 11.4 times the initial value. Fracture length and aperture distributions follow power-law scaling, and their fractal dimensions exhibit distinct evolution patterns: length dimension increases from 2.43 to a peak of 2.56 in the plastic stage and then drops to 2.47 at failure, while aperture dimension decreases from 2.29 to a trough of 2.12 before rebounding to 2.26. These patterns reflect a dynamic adjustment of network complexity, transitioning from primary fractures to micro-fracture dominance and finally to main fracture coalescence. Based on the Knudsen number, three diffusion regimes of Fick, transition and Knudsen are identified. A fractal permeability model is developed by idealizing the pore space as tortuous capillaries, showing that permeability scales with the fourth power of the maximum pore diameter and is positively influenced by the fractal dimension and the number of large pores. Furthermore, a coupled seepage–stress model is derived, incorporating pressure transmission, shear transmission, and crack opening coefficients. The damage variable is expressed as a function of stress level and fractal dimension. These findings provide theoretical support for predicting gas transport and failure behavior in coal under coupled hydro-mechanical conditions. Full article

To investigate the dynamic impact performance of carbon fiber reinforced polymer (CFRP)-confined recycled concrete, this study designed four series comprising 80 specimens with parameters including strain rate, recycled coarse aggregate replacement ratio, and number of CFRP confinement layers. Split Hopkinson Pressure Bar (SHPB) impact tests were conducted to analyze the dynamic failure mode, stress–strain responses under dynamic loading, and variation in compressive strength of the CFRP-confined concrete specimens. Additionally, a modified Weibull statistical model and fractal theory were employed to analyze the dispersion characteristics of dynamic compressive strength. The results show that the dynamic compressive strength exhibits clear strain-rate sensitivity. The presence of CFRP confinement does not alter the fundamental shape of the stress–strain curves under different strain rates. The proposed modified Weibull statistical model accurately predicts the distribution of dynamic compressive strength at varying strain rates, with an average prediction error of 3.4% and a maximum error of 5.3%. Fractal dimension can quantitatively characterize the evolution trend and degree of crack-induced damage. Within the strain rate range of 52.85–138.42 s⁻¹, the fractal dimension of unconfined ordinary concrete specimens increases from 1.647 to 2.138; for unconfined recycled concrete, it increases from 1.612 to 2.158. The fractal dimension for CFRP-confined ordinary concrete specimens increases from 1.524 to 1.938, and for CFRP-confined recycled concrete specimens, from 1.503 to 2.019. The fractal dimension increases with the increase of strain rate, reflecting a typical strain rate effect. Full article

Mo–Re alloys serve as critical structural components for high-temperature nuclear reactors, and their irradiation degradation is closely related to the evolution of vacancy-type defects. In this study, heavy-ion and He-ion irradiations were performed under RT to introduce an average displacement damage of 3.5 dpa within the 1 μm-thick surface layer of Mo–Re alloys with Re content up to 47 wt.%. PALS, SPB-DBS and CDB techniques were employed to characterize the size, concentration, depth distribution and local chemical environment of irradiation-induced vacancy-type defects. The results demonstrate that the longer lifetime component of irradiated Mo–Re alloys ranged from 262 to 280 ps, corresponding to medium-sized vacancy clusters. The S parameter of all specimens increased significantly from approximately 0.42 to 0.50, with negligible differences (<0.01) among various Mo–Re alloys. No distinct characteristic peak of Re was observed near 17 × 10⁻³ m₀c at the vacancy sites, which was inconsistent with simulation predictions. Mo–Re alloys exhibit similar vacancy-type defect features to pure Mo, implying weak interactions between Re solute atoms and vacancy-type defects under RT irradiation. Full article

Slamming-induced whipping has been recognized as a key contributor to fatigue damage of large ships operating under severe sea states. However, accurate prediction of whipping responses remains challenging because of complex nonlinear fluid–structure interactions. This study aims to investigate the characteristics of slamming-induced whipping and quantitatively analyze its influence on the fatigue damage of an ultra-large container ship. A three-dimensional fully nonlinear time-domain hydroelastic method, in which the boundary element model is coupled with a Timoshenko beam model, is employed to predict the slamming-induced whipping responses. Segmented model tests in long-crested irregular waves are conducted to provide wave loads of hull girders under severe sea states. The total and wave-frequency vertical bending moments are separated by the fast Fourier transform, and their statistical characteristics are evaluated through probability distributions. Fatigue damage is assessed on the basis of the rainflow counting method and the Palmgren–Miner cumulative damage rule. The contribution of high-frequency whipping responses to fatigue damage is quantitatively evaluated using a fatigue damage factor. It is demonstrated that slamming-induced whipping can significantly amplify fatigue damage by increasing stress amplitudes and cycle counts, particularly under high forward speeds and severe sea conditions. The findings provide a reliable reference for the fatigue design and safety assessment of ultra-large container ships. Full article

Due to climate change, corrosive conditions, and hydrogen-rich environments, steel energy pipelines inevitably develop a variety of defects. These deficiencies compromise pipeline safety and reliability, and neglecting them may result in pipeline leaks, fractures, and even potentially catastrophic explosions. Although a considerable body of literature reviews the effects of metal-loss defects like corrosion and cracks on pipeline safety and reliability, the impact of geometric deformation, like dents, lacks a comprehensive review. This work employs a hybrid systematic literature review (SLR) and bibliometric analysis (BA) to investigate the current research status of pipeline dent assessment. Four questions are answered: (1) What are the publication distribution characteristics, active journals, production organizations, and production authors related to research on pipeline dents? (2) What criteria have been employed for evaluating the pipeline dent? (3) From what perspective has the integrity of dented pipelines been assessed, and what research approaches have been used? (4) What are the future challenges and prospects of pipeline dent studies? The findings demonstrate that depth-, strain-, and damage-based evaluation criteria are widely employed to assess pipeline dents, each with merits and limitations. Despite the simplicity and ease of use of depth- and strain-based criteria, they are prone to underestimation flaws. In contrast, damage-based criteria, which consider multiple factors, are limited by their complexity and high computational resource requirements. The reliability of dented pipelines is predicted with remaining strength, fatigue life, and failure pressure using theoretical modeling, experimental testing, numerical simulation, or a combination of these methods. Future dent studies should involve refining numerical models, full-scale testing under varied loading conditions, and integrating advanced sensing techniques for real-time inspection. Full article

Invasive alien species (IAS) threaten coastal wetland ecosystems, and smooth cordgrass (Spartina alterniflora) is among the most damaging invaders along the coast of China. We compiled occurrence records from the invaded range (China) and native range (United States) and retained 358 and 291 spatially thinned occurrences after quality control and definition of coastal-accessible areas. We assembled climatic, topographic, land use, soil and anthropogenic predictors and fitted species distribution models using the biomod2 ensemble-modeling framework, complemented by an ecospat-based comparison of native and invaded niche spaces. The ensemble model (EM) showed high predictive accuracy (China: AUC = 0.98, TSS = 0.99; USA: AUC = 0.99, TSS = 0.94). Elevation (73.6%) and human influence (6.0%) were the strongest predictors, highlighting the role of intertidal geomorphology and human-mediated propagule pressure. Niche overlap between ranges was low (Schoener’s D = 0.13), and the invaded niche showed substantial unfilling (0.36), indicating additional environmental space at risk of colonization in China. The current suitable habitat forms a continuous coastal belt from the Bohai Rim through the Yellow Sea–East China Sea to the South China Sea. Projections under future climate change suggest predominantly stable suitable areas with localized expansions but potential contractions in some periods. Our results may support the early warning, surveillance prioritization, and adaptive management of S. alterniflora under climate change. Full article

In order to clarify the controlling effects of tectonic activity on hydrocarbon migration efficiency in the Permian–Triassic strata of the Luzhou area, Sichuan Basin, this study takes faults as the research objective. Using 3D seismic data, tectonic evolution records, and single-well test data, we systematically analyze the geometric characteristics, activity phases, classification by grade and type, and reservoir-controlling effects of faults. The results show that a total of 843 reverse faults have been identified in the study area. The major faults are distributed in a NE-SW trend, with eight planar combination styles developed, and the main cross-sectional styles are back-thrust and “Y”-shaped types. The faults experienced four phases of tectonic activity: Caledonian, Hercynian, Indosinian, and Yanshan–Himalayan. Among these, the Indosinian phase is the key formative phase, effectively connecting the source rocks and reservoirs. The faults are classified into three grades and four categories: source-connected faults, reservoir-modifying faults, damaging faults, and source-connected and damaging faults. Migration efficiency is jointly controlled by fault grade, activity phases, and the penetrated formations. Among them, third-order source-connected faults formed during the Indosinian phase exhibit the highest migration efficiency, while first-order damaging faults formed during the Yanshan phase tend to cause hydrocarbon dissipation. This study can provide a reference for hydrocarbon exploration and the prediction of favorable areas in the Luzhou area. Full article

Freeze-induced damage in concrete is governed by complex interactions between pore-scale phase transition and macroscopic mechanical response, while the underlying pore structure is typically difficult to observe directly. This study proposes an integrated framework for porosity inversion in concrete under freeze–heaving conditions, combining mechanical modeling, finite element simulation, and deep learning. A mechanics-based model is first developed to describe frost-heaving behavior in porous concrete, accounting for elastoplastic deformation of the matrix and partial volumetric expansion induced by pore water freezing. Based on this formulation, a parametric finite element model with randomly distributed pores is constructed to generate datasets linking pore characteristics to full-field deformation responses. Building upon these physics-consistent data, a deep learning framework is established to reconstruct pore distribution directly from three-component strain fields. The model employs a Vision Transformer backbone to capture global deformation patterns and incorporates a Kolmogorov–Arnold Network-based nonlinear mapping to enhance representation of the highly nonlinear inverse relationship. The results demonstrate that the proposed approach achieves accurate pore reconstruction and porosity prediction with stable convergence and satisfactory generalization performance across different porosity levels. The study provides a physically interpretable and computationally efficient pathway for linking deformation fields to internal pore structure, offering new potential for non-destructive characterization and durability assessment of concrete in cold-region environments. Full article

Currently, solar photovoltaic (PV) systems are a priority for end-use decarbonization, aimed at reducing reliance on fossil fuels. However, PV systems are typically exposed to outdoor conditions, making them more susceptible to aging and damage. In this paper, a predictive maintenance approach that integrates digital twin technology with the copula-based model is proposed. This integration enables accurate simulation of the PV system’s condition and precise representation of the correlation between the power output of the digital twin and that of the actual system. Given the power output of the digital twin, predictive maintenance is performed based on the conditional cumulative distribution function (CDF) of the actual power output, which is derived from the copula model. A comprehensive case study was conducted to evaluate the performance of the proposed approach named OCAD (Optimal Copula-based Anomaly Detector), which achieved an accuracy of 92.51% and an F1-score of 92.13%. This significantly outperforms conventional models, including SVM, KNN, and ANN, demonstrating the effectiveness of the proposed predictive maintenance strategy. Full article

Progressive collapse represents a catastrophic failure mode for reinforced concrete (RC) structures, yet the use of architected materials to mitigate this risk remains largely unexplored. This study presents a numerical feasibility investigation of RC beam–column sub-assemblages with auxetic metamaterial inserts embedded in critical joint regions. A hierarchical multiscale framework is developed to link the effective behavior of auxetic metamaterials with structure-scale collapse response. The framework couples macroscale structural analysis with mesoscale fracture simulations through a hybrid voxel–Voronoi discretization strategy. Baseline finite element models are validated against published experimental results for conventional RC specimens, while the auxetic-enhanced configurations are evaluated numerically. Under high tensile strain, the auxetic insert expands laterally because of its negative Poisson’s ratio and generates a localized confining stress field within the surrounding concrete. The simulations suggest that this mechanism may promote crack bifurcation, redistribute localized cracking into a more distributed damage pattern, and delay compressive crushing and crack coalescence. Compared with the corresponding conventional RC configurations, the auxetic-enhanced models predict a 25% increase in load redistribution capacity and a 20% enhancement in deformation ductility. These predicted improvements require future experimental validation using physical auxetic-enhanced RC specimens. The findings provide a computational basis for exploring material-by-design strategies aimed at improving the robustness of critical RC joint regions under progressive collapse demands. Full article

Polycrystalline diamond has great potential for power-device heat dissipation and precision manufacturing owing to its exceptional hardness, excellent thermal conductivity, and superior wear resistance. However, the challenges of material removal and controlling thermal damage hinder high-quality machining. In this study, a three-dimensional transient temperature field model is developed for multi-pulse nanosecond laser ablation of polycrystalline diamond. The model incorporates the Gaussian spatial distribution of laser energy, Lambert–Beer depth-dependent absorption, multi-pulse energy superposition, and three-dimensional heat conduction. The heat conduction equation is numerically solved using MATLAB, and lateral and longitudinal temperature gradients are introduced to characterize thermal accumulation and material removal behavior. The model is validated by comparing the predicted ablation depths with experimental measurements, which show consistent variation trends. The results indicate that increasing the number of scans, single-pulse energy, and pulse frequency enhances thermal accumulation, expands the microgroove width, and increases the ablation depth, whereas increasing the scanning speed weakens thermal accumulation and reduces the ablation depth. In addition, a shorter pulse width increases the instantaneous power density and strengthens near-surface thermal concentration. This study provides theoretical guidance for controlling the heat-affected region and optimizing process parameters in precision laser machining of diamond. 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

Acantholyda posticalis (Hymenoptera: Pamphiliidae) is a forestry pest in China. They primarily infest pine trees, causing serious ecological damage. The research aims to identify the key environmental factors influencing the suitable distribution area of Acantholyda posticalis and their optimal conditions, and investigate the impacts of climate change and possible impacts of its main host plants on the distribution of Acantholyda posticalis. By utilizing the MaxEnt model, we predict the potential distribution of Acantholyda posticalis and its main host plant, Pinus tabuliformis, under current and future climatic conditions. The results indicate that under current climatic conditions, the suitable areas for Acantholyda posticalis in China are extensive in the Loess Plateau and North China Plain regions and have extensive overlapping area with the distribution of Pinus tabuliformis. The dominant environmental factors influencing the distribution of suitable areas for Acantholyda posticalis are the Minimum Temperature of the Coldest Month, Precipitation of the Wettest Quarter, Altitude and Temperature Seasonality. Under the SSP126 and SSP585 climate scenarios for the period 2081–2100, the overall suitable area for Acantholyda posticalis is projected to follow a decreasing trend, exhibiting a tendency to extend toward the southern and eastern regions. Meanwhile, the moderately and highly suitable areas are more concentrated and extensive. The research provides a theoretical foundation for the control of Acantholyda posticalis and the protection of the ecological environment. Full article

The reinforcement and retrofit of damaged steel buildings has emerged as a primary focus in civil engineering. It should be noted that completing the reasonable strengthening design for avoiding the sudden collapse of a structure in extreme engineering conditions was an urgent task, while the existing method required a long time which significantly influenced the reinforcing practice. In the present study, an improved explainable machine learning (ML) framework was developed for the rapid assessment of the bending property of repaired laminated channel beams. Firstly, a comprehensive database of 192 samples combining experimental and finite element data was established. The Mahalanobis distance analysis and Pearson correlation analysis were sequentially performed to evaluate the singularity of the samples and the dependencies between the variables. Secondly, the adversarial tests were conducted on the randomly selected 10 pairs of training and testing sets to determine the database with the best distribution consistency. Then, three machine-learning models of artificial neural networks (ANN), random forest (RF), and extreme gradient boosting tree (XGBoost) were respectively trained and validated. Finally, the explainability analysis of the XGBoost model was carried out in the global and local perspectives based on the SHAP method. The prediction accuracy (R²) of all ML models exceeded 90%, demonstrating good accuracy and providing a useful reference within the current database for the reinforcement design of damaged steel beams in emergency situations. In addition, the XGBoost model achieved superior prediction accuracy (R² = 97.98%) and stability (CoV = 0.82%) compared to ANN and RF. The explainability analysis revealed that boundary conditions and load type had the most significant influence on bending capacity. The proposed ML approach enabled efficient and reliable bending capacity estimation, supporting rapid decision-making in emergency reinforcement scenarios for damaged steel structures. Full article