Search Results (279)

The mechanical properties and in situ stress conditions of coal reservoirs critically control the effectiveness of hydraulic fracturing, yet the continuous acquisition of relevant parameters at the well scale is often limited by logging data availability and quality. To address this, an integrated workflow combining machine learning-based parameter inversion with a fracturing suitability evaluation framework was proposed for coalbed methane (CBM) reservoirs. A supervised neural network model was developed to establish nonlinear relationships between conventional logs and key parameters, including Young’s modulus, Poisson’s ratio, and horizontal principal stresses. Based on these inverted parameters, a dimensionless Fracturing Index (FI) was constructed to comprehensively characterize coal fracturability by integrating brittleness, fracture toughness, and stress conditions, with a density-based constraint introduced to ensure mechanical consistency. Point-scale FI values within coal seams were upscaled to the well scale for inter-well comparison and regional evaluation. Results showed that FI varied relatively little within individual wells but markedly between wells, reflecting systematic inter-well variations in mechanical and stress conditions, consistent with spatial patterns revealed by cross-well profiles. Correlation analysis from over ten wells with both FI and treatment data demonstrated positive relationships between FI and breakdown pressure, injected fluid volume, and proppant volume, confirming its engineering relevance. Consequently, a four-level FI-based classification scheme was established to identify favorable zones across the study area. This FI framework provides a practical, interpretable tool for early-stage CBM development, offering quantitative guidance for well prioritization, stimulation design, and regional planning in unfractured areas. Full article

Cervical Intervertebral Disc Degeneration (CIVDD) involves significant microenvironmental physical stiffening, forcing nucleus pulposus cells (NPCs) into a rigid phenotype via F-actin over-assembly. It remains unclear if cyclic tensile strain (CTS) can reverse this physical stiffening, particularly in severe degeneration. This study stratified 18 patients into Mild, Moderate, and Severe cohorts based on MRI. Primary NPCs were subjected to physiological 5% CTS (1 Hz, 24 h). Atomic Force Microscopy (AFM) and immunofluorescence were utilized to evaluate Young’s modulus and cytoskeletal remodeling. Results demonstrated that baseline cellular stiffness increased significantly with degeneration severity. Following CTS, all groups exhibited universal de-stiffening and F-actin depolymerization. Crucially, a “Degeneration Paradox” emerged: the Severe group displayed the highest relative elastic modulus recovery rate, significantly surpassing the Mild group. This microscopic recovery correlated inversely with preoperative disc height loss and range of motion. We conclude that severely degenerated cells are not metabolically quiescent but “physically locked” by a rigid cytoskeleton. Physiological CTS restores compliance via mechanical unloading, confirming that severe cells retain superior relative mechanoplasticity and may benefit from mechanotherapy-based “unlocking” strategies. Full article

Real-time mechanical analysis within digital twin (DT) systems requires high-fidelity models that synchronize with the “as-built” state of physical structures. This paper proposes a technical framework for constructing a “mechanical-core” DT by integrating computer vision (CV) sensing with automated finite element model (FEM) updating. Utilizing the Midas API, we developed a platform that automates data acquisition, modeling, and parameter inversion. A momentum-based optimization algorithm is implemented to invert the instantaneous elastic modulus of bridge segments during cantilever construction. The system was validated through a case study of a continuous box-girder bridge. Quantitative results indicate that the initial theoretical model, based on design-phase assumptions, exhibited a mean relative error of approximately 21.9% in vertical displacement. Following the real-time parameter inversion, this error was significantly reduced to less than 0.2% across all monitored construction stages. The rapid convergence (typically within three iterations) and the substantial increase in predictive accuracy demonstrate that the proposed framework effectively bridges the gap between raw sensing data and structural analysis, providing a reliable basis for proactive engineering decision-making. Full article

The mechanical behavior of deep rock masses under high-stress conditions exhibits significant nonlinear characteristics. However, current in situ stress field inversion methods typically rely on linear elastic constitutive models and multiple linear regression analysis. By analyzing the results of triaxial stress–strain tests and confining pressure calibration experiments on rocks, and drawing on the nonlinear concepts from the Duncan-Zhang model, a nonlinear characterization function was developed, represented by mean stress p, bulk modulus K, and shear modulus G. The nonlinear elastic constitutive model was integrated into a numerical simulation framework, and a new in situ stress field inversion fitting method based on nonlinear elastic constitutive modeling was proposed. This method uses initial linear iterations followed by multiple nonlinear iterations until convergence is achieved. Applied to the inversion of the deep in situ stress field at the Xishan Iron Mine, the results demonstrate that compared to traditional linear regression-based methods, the errors in mean stress, deviatoric stress, and the Lode parameter were reduced by 58%, 50%, and 22%, respectively, confirming the effectiveness of this method in in situ stress field inversion in rock mechanics. Full article

Underwater acoustic coatings are widely used to suppress low-frequency noise radiation and sonar reflection in underwater vehicles. In this study, an underwater acoustic coating model consisting of viscoelastic rubber layers and micro-perforated panel (MPP) structures is investigated, with particular emphasis on the low-frequency sound absorption mechanism and predictive modeling. Based on an improved transfer function method, a novel Micro-Perforated Panel Acoustic Coating Layer (MPPACL) model is developed to describe the coupled acoustic behavior of multilayer coatings under underwater conditions. The low-frequency sound absorption performance is primarily governed by the viscoelastic characteristics of the rubber layer, including material damping and complex modulus, while the incorporation of the MPP further enhances absorption through resonance effects. To efficiently explore the relationship between structural parameters and acoustic response, an ensemble learning-based deep neural network (ELDNN) is constructed using analytically generated data, enabling both forward prediction of sound absorption performance and inverse prediction of structural design parameters. The results show that the frequency prediction accuracy of the IDNN model is 3.7 times that of the DNN model. Furthermore, the proposed MPPACL model has achieved a significantly enhanced sound absorption effect within the frequency range of 50 to 2000 hertz. This effect has also been further verified through underwater experiments. The proposed framework provides an efficient and reliable approach for the design and optimization of underwater acoustic coatings. Full article

This study investigates the performance and microstructure evolution of high-ferrite Portland cement (HFC) concrete under the coupled action of abrasion and freeze–thaw cycles (CAA-FTC). The 3D surface morphology of deteriorated concrete was studied; abrasion depth and volume loss evolution data were collected, while analyzing the abrasion depth fractal dimension. The characteristics of hydration products were determined using mercury intrusion porosimetry and ²⁹Si nuclear magnetic resonance method. The ITZ’s micromechanical properties and thickness were investigated via nanoindentation and SEM-EDS. The results show that under the CAA-FTC conditions, concrete deterioration is significantly exacerbated, leading to increased abrasion depth and volume loss compared to single-factor abrasion. A significant inverse relationship between the abrasion depth fractal dimension and abrasion resistance was revealed. Under CAA-FTC conditions, CG1 and CD1 exhibit increased total porosity with enlarged large pore proportions and reduced medium pores, whereas HFC1 outperforms HFC2-based concrete, showing 8.2–26.4% higher abrasion resistance and 6.5–12.0% greater nanoindentation elastic modulus in the ITZ. Regarding the deterioration factors’ influence weight, abrasion time exhibits a deterioration weight 4.8 times to 10.0 times greater than freeze–thaw cycling, making the former a dominant factor and the latter a secondary contributor. Mechanistically, freeze–thaw cycles reduce the average molecular chain length of C-S-H gel, increase harmful pores and total porosity, and degrade the ITZ’s microstructure, while abrasion causes surface-to-core physical damage and freeze–thaw cycling induces core-to-surface expansive damage. This interaction results in surface scaling, mortar spalling, and structural loosening, significantly reducing physical and mechanical properties of the concrete under study. Full article

Laser powder bed fusion (LPBF) enables controlled gyroid lattices, but mapping both process and design to performance remains challenging when datasets are small and interactions are non-linear. In this study, data-driven models that link energy density and lattice geometry to Young’s modulus and yield strength were established for sheet and network gyroid architectures. To stabilise small-data learning, stacked-autoencoder pre-training was benchmarked against greedy layer-wise pre-training. Compression characterisation data at under-represented energy-density conditions were added to fill data gaps and validate predictions. The models support property-driven design in which given modulus and yield strength targets inform a method that returns feasible combinations of laser powder bed fusion settings and gyroid density and size. Pre-trained models reduced error and captured the relationship between stiffness and density and between strength and density, with yield strength prediction errors of 3.51% for sheet architectures and 8.76% for network architectures. Young’s modulus showed a higher variability that is consistent with sensitivities in LPBF such as surface roughness and thin walls. This work contributes an artificial intelligence method for manufacturing datasets using stacked autoencoder pre-training with fine-tuning, and an inverse-design workflow that maps energy density and gyroid geometry to Young’s modulus and yield strength in titanium lattices. Full article

Background: This study aimed to investigate the biomechanical behavior of conservative inlay restorations fabricated from different CAD/CAM materials by combining experimental flexural strength testing with finite element analysis. Methods: Five CAD/CAM materials were evaluated: feldspathic ceramic (Cerec Blocs), leucite-reinforced ceramic (IPS Empress CAD), resin nano-ceramic (Lava Ultimate), polymer-infiltrated ceramic network (VITA Enamic), and lithium disilicate ceramic (IPS e.max CAD). Young’s modulus and Poisson’s ratio were experimentally determined using three-point bending and nanoindentation tests and used as inputs for 3D FEA. Von Mises (VM) stress distributions within the inlays were analyzed under simulated occlusal loading. Results: Maximum VM stresses showed an inverse relationship with material elasticity. IPS e.max CAD exhibited the highest maximum VM stress (45.571 MPa), whereas the resin nano-ceramic showed the lowest (25.419 MPa). Despite higher stress concentrations in high-modulus ceramics, VM values for all materials remained well below their FS limits. Conclusions: All materials demonstrated adequate mechanical stability under physiological loading. Lithium disilicate showed a comparatively larger margin between stress levels and flexural strength, while lower-modulus materials tended to promote greater stress transfer to supporting structures. Full article

The advancement of large-scale marine infrastructure demands increasingly accurate prediction of settlement in deep-water foundations. The caisson is an important type of deep-water foundation whose additional settlement induced by superstructure construction directly impacts the overall safety of the project. This study focuses on the main tower foundation of the Changtai Yangtze River Bridge, recognized as the world’s largest deep-water caisson foundation. A three-dimensional finite element model was developed using the hardening soil model with small-strain stiffness (HSS) constitutive model to simulate the settlement response of the caisson foundation throughout the entire superstructure construction process. The model’s reliability was verified through systematic comparison with field monitoring data. Furthermore, an inversion analysis was conducted on the initial shear modulus ($G_0^{\mathrm{ref}}$), the most sensitive parameter of the HSS model, based on the measured data. The results reveal that its optimal value exhibits significant load dependency, varying according to the construction stage. Accordingly, practical strategies for parameter determination are proposed: a fixed-value method ($G_0^{\mathrm{ref}}$ = 2$E_{\mathrm{ur}}^{\mathrm{ref}}$) suitable for conventional design and a more precise stage-specific value method. Both approaches markedly enhance the settlement prediction accuracy, particularly under high-load conditions. The findings offer valuable insights for the refined design and safety assessment of similar deep-water mega-foundation projects. Full article

This study investigates the influence of cooling rates on the structural evolution and mechanical properties of ultra-high-molecular-weight polyethylene/high-density polyethylene fibers by systematically varying cooling media from ambient air (f1) to room-temperature water (f5). A significant structural inversion was observed between the as-spun and drawn fiber stages: while slow cooling (f1) promotes thermodynamic crystallization to form large, stable grains and maximum initial crystallinity (54%), rapid quenching (f5) effectively “freezes” the molecular chains in a low-crystallinity, highly orientable precursor state by suppressing thermal relaxation. During subsequent hot-drawing, the quenched samples (f5) exhibited a superior response to tensile stress, achieving the highest maximum draw ratio due to reduced crystalline obstacles and enhanced chain mobility. This enables efficient stress-induced crystallization, leading to near-perfect crystal orientation (f_c > 0.95) and a dense microfibrillar framework. Consequently, the fiber performance trends reversed, with f5 achieving peak tensile strength (1.33 GPa) and modulus, whereas f1 remained limited by its rigid thermal history. These findings highlight that rapid quenching is essential for developing high-performance fibers by preserving a precursor structure that maximizes the potential of stress-induced crystallization. Full article

Excavation of underground spaces often causes significant initial damage to surrounding rock, which can notably alter its mechanical properties. However, most studies on loading rate effects neglect the role of initial damage. This study investigates how initial damage and loading rate together affect granite’s mechanical behavior and fracturing characteristics. Granite specimens with different initial damage levels were subjected to uniaxial compression at varying loading rates to assess their mechanical parameters, stress thresholds, failure modes, energy evolution, and associated acoustic emission (AE) activity. Results indicate that granite’s mechanical behavior exhibits greater sensitivity to loading rate than to initial damage. As the loading rate increases, both strength and elastic modulus initially decrease and then rise, while the dissipated-to-input energy ratio reaches a maximum when the strength is at its lowest. This phenomenon occurs because, when cracks are allowed to fully develop, a relatively higher loading rate increases the likelihood of crack initiation and propagation, thereby reducing strength. The AE responses of initial damage granite samples (IDGSs), including counts, RA/AF value, b-value, and entropy, exhibit stage-dependent variations and contain precursory information before failure. Moreover, AE signals display multifractal characteristics across different loading rates. These findings reveal the mechanisms underlying granite’s mechanical response when both initial damage and loading rate act together: initial damage primarily affects the complexity and number of local microcracks, while loading rate determines the dominant crack initiation and propagation modes. Moreover, how the failure time of IDGSs varies with loading rate can be described by an inverse exponential function. These findings enhance insight into the coupling mechanism of initial damage and loading rate, with significant implications for failure warning and the cost-effectiveness of underground excavation. Full article

Shale oil reservoirs exhibit ultralow permeability and complex pore structures, which result in non-Darcy low-velocity flow and cause permeability to be stress-sensitive. Moreover, two-phase flow of oil and gas frequently occurs during the depletion of shale oil reservoirs. Consequently, investigating the rate-transient behavior of shale oil wells necessitates comprehensive consideration of multiphase flow, threshold pressure gradients, and stress sensitivity. Although numerous analytical models exist for rate-transient analysis of multistage fractured horizontal wells, none of them simultaneously incorporate these critical factors. In this study, we extend the classical five-region model to incorporate multiphase flow, threshold pressure gradients, and stress sensitivity. The proposed model is solved using Pedrosa’s transformation, perturbation theory, the Laplace transform, and the Stehfest numerical inversion method. A systematic analysis of the influence of various parameters on the oil production rate and cumulative oil production is conducted, and a field case study is presented to validate the applicability and effectiveness of the model. It is found that the permeability modulus of the main fracture, the half-length of the main fracture, and the threshold pressure gradient of the unstimulated reservoir have a significant influence on cumulative oil production spanning 20 years. With a 100% relative input error, these parameters result in prediction errors of 23.77%, 16.65%, and 17.78%, respectively. In contrast, the threshold pressure gradient of the main fracture and the threshold pressure gradient of the stimulated reservoir have a negligible impact; under the same level of input error (100%), they cause only 0.36% and 0.48% prediction errors in the 20-year cumulative oil production period, respectively. This research provides an efficient and reliable framework for analyzing production data and forecasting shale oil well performance. Full article

Although PINNs have demonstrated strong predictive capabilities in forward problems, their performance in inverse problems remains inadequate, largely due to unquantifiable noise encountered during the multi-parameter identification of prestressed concrete beams. Experimental measurements are often noisy, sparse, or asymmetric, while numerical or analytical models, although physically consistent, are typically approximate and lack regularization from well-defined multi-fidelity data. To address this limitation, this paper proposed a multi-fidelity data and prior-enhanced physics-informed neural network (MF-rPINN), which integrates multi-fidelity data with physics prior relational constraints to guide parameter identification using only sparse experimental observations. The MF-rPINN architecture is designed to enforce consistency between each training iteration and a prescribed set of experimental measurements, while embedding the second-order displacement function into the loss function. Experimental results demonstrate that the proposed MF-rPINN achieves accurate parameter identification even under noisy and incomplete observations, owing to the combined regularization effects of governing physical laws and the second-order displacement prior. The minimum relative errors of the elastic modulus are −6.49% and −9.32% under different and identical loading conditions, respectively, while the minimum relative errors of the prestress force are 0.65% and 4.51%. Compared with classical analytical approaches, MF-rPINN exhibits superior robustness and is capable of predicting continuous displacement fields of prestressed concrete beams while simultaneously identifying prestress force and elastic modulus. These advantages highlight the potential of MF-rPINN as a reliable surrogate modeling tool for practical engineering applications. Full article

To enhance the actuation performance of artificial muscles, a thermo-piezoelectric coupled model was developed based on the inverse piezoelectric effect of piezoelectric bimorphs. By altering the effective piezoelectric coefficient, elastic modulus, and effective thermal expansion coefficient of the thermo-piezoelectric bimorph model, the bending motion of artificial muscles was simulated. The effects of multi-walled carbon nanotube (MWCNT) and Genipin crosslinking on the bending force and output displacement of the artificial muscles were analyzed, illustrating how crosslinking affects the equivalent actuation response. The results showed that MWCNT and Genipin crosslinking significantly improved the actuation performance of the artificial muscles. Through numerical simulation, the optimal crosslinking ratio was determined to be 43.34% MWCNT and 0.1% Genipin, at which the best actuation performance was achieved. Compared to non-crosslinked techniques, the artificial muscles with crosslinked structures exhibited markedly enhanced actuation behavior. Full article

The long-term flexural performance of steel-concrete composite beams after creep is influenced by multiple factors such as the degree of shear connection, cross-sectional form, and boundary conditions. The engineering community has an ambiguous understanding of the coupling effects of these factors. To address this issue, this paper conducts systematic experimental research: six simply supported beams (three box-shaped, three I-shaped) and four continuous beams (two box-shaped, two I-shaped) were designed with three degrees of shear connection (0.57, 1.08, 1.53). These beams first underwent simulated creep tests (24 °C, 80% relative humidity, 10 kN load, 180 days), followed by monotonic bending tests. The results indicate: (1) A high degree of shear connection (1.53) reduces creep deflection by 15–20% compared to partial connection (0.57) and delays the initiation of interface slip to 30% of the ultimate load; (2) Box sections exhibit 10–15% lower creep deflection than I-sections, though both experience 40–60% stiffness reduction after creep; (3) Continuous beams show a 25% improvement in crack resistance in the negative moment region and a 50% increase in flexural capacity at mid-span compared to simply supported beams; (4) After creep, the elastic modulus of concrete decreases by 40–60% (inversely related to the degree of shear connection), with fully connected specimens retaining 55–61% of their strength, while partially connected specimens retain only 43–49%. This study quantifies the degradation patterns of concrete performance, clarifies the influence mechanisms of key structural factors, and provides theoretical and experimental support for the long-term performance design of composite beams. Shear connection design is crucial for mitigating creep effects. Full article