Search Results (1,653)

Molecular dynamics (MD) can dynamically reveal the structural evolution and mechanical response of Zirconium (Zr) at the atomic scale under complex service conditions such as high temperature, stress, and irradiation. However, traditional empirical potentials are limited by their fixed function forms and parameters, making it difficult to accurately describe the multi-body interactions of Zr under conditions such as multi-phase structures and strong nonlinear deformation, thereby limiting the accuracy and generalization ability of simulation results. This paper combines high-throughput first-principles calculations (DFT) with the machine learning method to develop the Deep Potential (DP) for Zr. The developed DP of Zr was verified by performing molecular dynamic simulations on lattice constants, surface energies, grain boundary energies, melting point, elastic constants, and tensile responses. The results show that the DP model achieves high consistency with DFT in predicting multiple key physical properties, such as lattice constants and melting point. Also, it can accurately capture atomic migration, local structural evolution, and crystal structural transformations of Zr under thermal excitation. In addition, the DP model can accurately capture plastic deformation and stress softening behavior in Zr under large strains, reproducing the characteristics of yielding and structural rearrangement during tensile loading, as well as the stress-induced phase transition of Zr from HCP to FCC, demonstrating its strong physical fidelity and numerical stability. Full article

This paper presents a probabilistic methodology for generating fragility surfaces for low- to mid-rise steel moment-resisting frames (MRFs) under fire-following-earthquake (FFE). The framework integrates nonlinear dynamic seismic analysis, residual deformation transfer, and temperature-dependent fire simulations within a Monte Carlo environment, while explicitly accounting for uncertainties in structural properties, ground motions, and fire simulation. A fiber-based modeling strategy is employed, combining temperature-sensitive steel materials with fatigue and fracture wrappers to capture cyclic deterioration and abrupt failure. This formulation yields earthquake-only and fire-only fragility curves along the surface boundaries, while interior points quantify the joint fragility response under sequential hazards. The methodology is benchmarked against a machine learning (ML) synthesis framework originally developed for earthquake–tsunami applications and extended here to FFE. Numerical results for a three-story steel MRF show excellent agreement (R² > 0.95, RMSE < 0.02) between simulated and ML-generated surfaces, demonstrating both the efficiency and hazard-neutral adaptability of the ML framework for multi-hazard resilience assessment. Full article

This study presents a new formulation for laminated composite thick shells by incorporating the discrete Kirchhoff–Mindlin triangular (DKMT) element into the vector form intrinsic finite element (VFIFE) method. This integration enables the accurate modeling of transverse shear effects, which are difficult to capture using conventional VFIFEs. In this framework, the shell is discretized into particles whose motions are analyzed over discrete time intervals, referred to as path elements. Euler’s law of motion governs particle dynamics, while triangular elements connect the particles and describe local deformation and internal forces. Quaternions represent rigid body rotations within the convected material frame, and internal forces are obtained from the shape functions of the VFIFE–DKMT element. The formulation is validated through numerical examples involving geometrically nonlinear displacements, dynamic responses, and large deformations in isotropic and composite shells. The results demonstrate the accuracy and robustness of the proposed method in predicting the nonlinear motion of thick shell structures. Full article

Accurate prediction of concrete compressive strength is essential for ensuring structural safety in civil engineering, particularly in road and bridge construction, where inadequate strength can lead to deformation, cracking, or collapse. Traditional non-destructive testing (NDT) methods, such as the Rebound Hammer Test, estimate strength using regression-based formulas fitted with measurement data; however, these formulas, typically optimized via the least squares method, are highly sensitive to initial parameter settings and exhibit low robustness, especially for nonlinear relationships. Meanwhile, AI-based models, such as neural networks, require extensive datasets for training, which poses a significant challenge in real-world engineering scenarios with limited or unevenly distributed data. To address these issues, this study proposes a gradient-boosting artificial bee colony (GB-ABC) algorithm for robust regression curve fitting. The method integrates two novel mechanisms: gradient descent to accelerate convergence and prevent entrapment in local optima, and a point density-weighted strategy using Gaussian Kernel Density Estimation (GKDE) to assign higher weights to sparse data regions, enhancing adaptability to field data irregularities without necessitating large datasets. Following data preprocessing with Local Outlier Factor (LOF) to remove outliers, validation on 600 real-world samples demonstrates that GB-ABC outperforms conventional methods by minimizing mean relative error rate (RER) and achieving precise rebound-strength correlations. These advancements establish GB-ABC as a practical, data-efficient solution for on-site concrete strength estimation. Full article

This study presented a comprehensive finite element (FE) investigation into the flexural behavior of RC T-beams strengthened in the negative moment region using near-surface mounted (NSM) carbon-fiber-reinforced polymers (CFRP) rods. A three-dimensional nonlinear FE model was developed and validated against experimental data, achieving close agreement with normalized mean square error values as low as 0.006 and experimental-to-numerical ratios ranging from 0.95 to 1.04. The validated model was then employed to conduct a systematic parametric analysis considering CFRP rod diameter, concrete compressive strength, longitudinal reinforcement ratio, and FRP material type. The results showed that increasing CFRP diameter from 6 to 10 mm enhanced ultimate load by up to 47.51% and improved stiffness by 1.48 times. Higher concrete compressive strength contributed to stiffness gains exceeding 50.00%, although this improvement was accompanied by reductions in ductility. Beams with reinforcement ratios up to 2.90% achieved peak loads of 309.61 kN, but ductility declined. Comparison among FRP materials indicated that CFRP and AFRP offered superior strength and stiffness, whereas BFRP provided a more balanced combination of strength and deformation capacity. Full article

The effect of third-order shear deformation theory (TSDT) on thick functionally graded material (FGM) spherical shells under sinusoidal thermal vibration is investigated by using the generalized differential quadrature (GDQ) numerical method. The TSDT displacement field and an advanced nonlinear shear correction coefficient are used to derive the equations of motion for FGM spherical shells. The simple stiffness of FGM spherical shells under a temperature difference along the linear vs. z-axis direction is considered in the heat conduction equation. The dynamic GDQ discrete equations of motion subjected to thermal load and inertia terms can be expressed in matrix form. A parametric study of environmental temperature, FGM power-law index, and advanced nonlinear shear correction on thermal stress and displacement is conducted under the vibration frequency of a simply homogeneous equation and applied heat flux frequency. This is a novel method for obtaining the numerical GDQ results, comparing cases with linear and advanced nonlinear shear correction. The novelty of the present work is that an advanced varied-value type of shear correction coefficient can be successfully used in the thick-walled structure of FGM spherical shells subject to thermal vibration while considering the nonlinear term of TSDT displacements. The purpose of the present work is to investigate the numerical thermal vibration data for a two-material thick FGM spherical shell. Full article

Understanding the nonlinear mechanical behaviour of mitral valve chordae tendineae is critical for accurate biomechanical modelling in cardiac simulations. This study integrates high-resolution 3D finite element analysis with experimentally derived Cauchy stress–Green–Lagrange strain data to capture both material and geometric nonlinearities. A one-dimensional formulation incorporating strain-dependent elasticity and large deformation kinematics was developed and validated against 3D simulations in COMSOL Multiphysics. Calibrated using experimental stress–strain data and validated against high-fidelity 3D finite element simulations in COMSOL, it reveals that neglecting transverse deformation overestimates axial force by 7%. Cross-sectional area reduction during stretch remained consistently around 12%, underscoring the importance of Poisson effects. A polynomial fit to the strain-dependent modulus of elasticity enables efficient force prediction with excellent agreement to experimental data. These results advance the mathematical modelling of biological tissues with nonlinear hyperelastic behaviour, providing a foundation for patient-specific simulations and real-time predictive tools in cardiovascular engineering. Full article

During the Fused Deposition Modeling (FDM) molding process, temperature changes are nonlinear and instantaneous, which is a key parameter affecting FDM printing efficiency, molding accuracy, warpage deformation, and other factors. This study presents a finite element simulation framework that integrates ANSYS Parametric Design Language (APDL) with birth–death element technology to investigate the temperature evolution and thermomechanical behavior during the FDM process. The framework enables dynamic simulation of the complete printing and cooling cycle, capturing the layer-by-layer material deposition and subsequent thermal history. Results indicate that temperature distribution follows a gradient pattern along the printing path, with rapid heat dissipation at the periphery and heat accumulation in the central regions. Thermomechanical coupling analysis reveals significant stress concentration at the part bottom (310 MPa) and progressive strain increase from bottom (3.68 × 10⁻⁵ m) to top (2.95 × 10⁻⁴ m). Experimental validation demonstrates strong agreement with numerical predictions, showing maximum temperature deviations below 8% and strain distribution errors within 5%. This integrated approach provides an effective tool for predicting thermal-induced deformations and optimizing FDM process parameters to enhance part quality. Full article

Addressing the core issue of rock mass failure and deformation induced by local water-induced uneven expansion in expansive soft rock tunnels, this study systematically analyzes the stress–displacement response of the rock mass under various working conditions. This analysis integrates physical model testing with FLAC3D 6.0 numerical simulation and covers four typical expansion zone configurations (vault, spandrel, haunch, invert) as well as multiple stages of stress loading. Leveraging the mathematical analogy between heat conduction and fluid seepage and combining it with a thermo-hydraulic coupling approach, the FLAC3D temperature field module precisely simulates the moisture-induced stress field. This overcomes the limitations of traditional tools for direct moisture field simulation and enables quantitative assessment of how localized expansion impacts tunnel lining failure. The study reveals that horizontal expansion zones significantly increase the risk of shear failure in tunnel structures. Expansion zones at the tunnel crown and base (invert) pose critical challenges to overall safety and exhibit a pronounced nonlinear relationship between stress loading and displacement. This research deepens the theoretical understanding of the interaction between localized non-uniform expansion and the surrounding rock mass and provides crucial technical guidance for optimizing tunnel support systems and improving disaster monitoring and prevention measures. Full article

Transmission towers located above mined-out areas may experience collapse or instability due to mining-induced ground subsidence and deformation, which poses significant risks to the safe operation of power transmission lines. To clearly evaluate the deformation resistance and failure threshold of transmission towers under mining-induced ground deformation, this article examines a typical 220 kV self-supporting transmission tower located in a mining area of Northern Shaanxi Province through a detailed three-dimensional finite element model constructed and simulated using ANSYS 2022. The mechanical response and failure process of the tower structure were systematically simulated under five typical deformation conditions: tilt, horizontal compression, horizontal tension, tilt–compression, and tilt–tension. The results indicate that under individual deformation conditions, the critical deformation values of the tower are 35 mm/m for tilt, 10 mm/m for horizontal compression, and 8 mm/m for horizontal tension, demonstrating that the structure is most sensitive to horizontal tensile deformation. Under combined deformation conditions, the critical deformation values for the combined tilt–compression and tilt–tension conditions exhibited a marked reduction, reaching 8 mm/m and 6 mm/m. Compared to individual deformation conditions, transmission towers demonstrate a significantly higher susceptibility to structural failure under combined deformation conditions. The displacement at the tower head and the tower tilt angle exhibit a linear positive correlation with the values of ground surface deformation. Under individual deformation conditions, the tilt of the tower was approximately 0.903 times the tilt deformation value and 0.089 times the values of horizontal compression and tension deformation, indicating that tilt deformation exerts a more pronounced influence on the inclination of the tower. Under combined deformation conditions, the tilt of the tower reached approximately 0.981 times that of the tilt–compression deformation value and 0.829 times that of the tilt–tension deformation value. Compared to the tower tilt induced individually by horizontal compression or tension deformation, the tilt under combined deformation conditions demonstrated a significantly greater value. Under mining-induced ground deformation, a redistribution of support reactions occurs, exhibiting either nonlinear or linear increasing trends depending on the type of deformation. The findings of this article provide a theoretical basis and data support for disaster prevention and control, safety evaluation, and structural design of transmission lines in mining areas. Full article

After reservoir impoundment, water infiltration weakens rock strength and accelerates creep deformation. Existing models seldom capture both strength degradation and creep behavior under prolonged saturation. This study develops a coupled hydro-mechanical creep model that integrates saturation-dependent elastic modulus reduction, cohesion decay with pore pressure, and a nonlinear creep law modified by a Heaviside function. Simulation of rock deformation during water infiltration reveals that water–creep coupling increases steady-state deformation by over 50% compared to strength degradation alone. A case study of a high arch dam reservoir slope demonstrates that models incorporating both water-weakening and creep effects predict significantly larger deformations than those ignoring these mechanisms. The model provides a practical tool for predicting long-term deformation in reservoir slopes under water–rock interaction. Full article

The structural integrity of transmission towers, as the backbone of power grids, is critical to overall grid safety, relying heavily on the reliability of bolted connections. Dynamic loads such as wind-induced vibrations can cause bolt loosening, potentially leading to structural deformation, cascading failures, and large-scale blackouts. Traditional manual inspection methods are inefficient, subjective, and hazardous. Existing automated approaches are often limited by environmental noise sensitivity, high computational complexity, sensor placement dependency, or the need for extensive labeled data. To address these challenges, this paper proposes a portable acoustic detection system based on Variational Mode Decomposition (VMD) and an Adversarial Multilayer Perceptual Network (AT-MLP). The VMD method effectively processes non-stationary and nonlinear acoustic signals to suppress noise and extract robust time–frequency features. The AT-MLP model then performs state identification, incorporating adversarial training to mitigate distribution discrepancies between training and testing data, thereby significantly improving generalization and noise robustness. Comparison results and analysis demonstrate that the proposed VMD and AT-MLP framework effectively mitigates structural variability and environmental interference, providing a reliable solution for bolt loosening detection. The proposed method bridges structural mechanics, acoustic signal processing, and lightweight intelligence, offering a scalable solution for condition assessment and risk-aware maintenance of transmission towers. Full article

To overcome the limitations imposed by the anisotropy and heterogeneity of natural loess, this study establishes a novel quantitative macro–micro correlation framework for investigating the deformation mechanisms of artificial structural loess (ASL). ASL samples were prepared by mixing remolded loess with cement (0–4%) and NaCl (0–16%), followed by static compaction (95% degree) and 28-day curing (20 ± 2 °C, >90% RH) to replicate the structural properties of natural loess under controlled conditions. An integrated experimental methodology was employed, incorporating consolidation/collapsibility tests, particle size analysis, X-ray diffraction (XRD), and mercury intrusion porosimetry (MIP). A three-dimensional nonlinear model was proposed. The findings show that intergranular cementation, particle size distribution, and pore architecture are the main factors influencing loess’s compressibility and collapsibility. A critical transition from medium to low compressibility was observed at cement content ≥1% and moisture content ≤16%. A strong correlation (Pearson |r| > 0.96) was identified between the mesopore volume ratio and the collapsibility coefficient. The innovation of this study lies in the establishment of a three-dimensional nonlinear model that quantitatively correlates key microstructural parameters (fractal dimension value (D), clay mineral ratio (C), and large and medium porosity (n)) with macroscopic deformation indicators (porosity ratio (e) and collapsibility coefficient (δ_s)). The measured data and the model’s output agree quite well, with a determination coefficient (R²) of 0.893 for porosity and 0.746 for collapsibility, verifying the reliability of the model. This study provides a novel quantitative tool for loess deformation prediction, offering significant value for engineering settlement assessment in controlled cementation and moisture conditions, though its application to natural loess requires further validation. Full article

In engineering practice, soils will inevitably experience some rotation of principal stress directions. Recent experimental evidence has highlighted how principal stress axes rotation significantly impacts clay behavior. However, most existing constitutive models accounting for this effect are essentially designed for sand and may not be applicable to clays. This paper introduces an anisotropic bounding surface model to reproduce the response of clay to principal stress axes rotation. The model’s key innovation lies in its incorporation of a secondary mapping procedure in the deviatoric stress ratio plane, which utilizes a relocatable mapping center. This step is a complement to the conventional radial mapping procedure in the meridional plane, which utilizes a fixed mapping center. This constitutive enhancement facilitates the precise modeling of plastic deformation triggered by the rotation of principal stress axes, without introducing additional loading mechanisms or incremental stress–strain nonlinearity. The performance of the model is first evaluated under various conditions and then verified through comparisons between simulation results and experimental data. The results demonstrate the effectiveness of the model and underscore the necessity of incorporating stress rotation effects into the constitutive modeling of clay. Full article

Accurate prediction of dam deformation is crucial for structural safety monitoring. For enhancing the prediction accuracy of concrete dam deformation and addressing the issues of insufficient precision and poor stability in existing methods when modeling complex nonlinear time series, a concrete dam deformation prediction method based on mode decomposition and Self-Attention-Gated Recurrent Unit (SAGRU) was proposed. First, Variational Mode Decomposition (VMD) was employed to decompose the raw deformation data into several Intrinsic Mode Functions (IMFs). These IMFs were then classified by K-means algorithm into regular signals strongly correlated with water level, temperature, and aging factors and weakly correlated random signals. For the random signals, an Improved Wavelet Threshold Denoising (IWTD) method was specifically applied for noise suppression. Based on this, a Deep Learning (DL) model based on SAGRU was constructed to train and predict the decomposed regular signals and the denoised random signals, respectively. And finally, the sum of the calculation results of each signal can be output as the predicted deformation. Experimental results demonstrate that the proposed method outperforms existing models in both prediction accuracy and stability. Compared to LSTM, this method reduces the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by approximately 30.9% and 27.2%, respectively. This provides an effective tool for analyzing concrete dam deformation and offers valuable reference directions for future time series prediction research. Full article