Search Results (1,507)

Rock cohesion ($c$) and angle of internal friction ($\phi$) are core parameters for rock mass stability analysis and engineering design; however, traditional triaxial tests are costly and time-consuming, limiting their availability in preliminary engineering assessments. To address this limitation, this study develops a machine learning framework that predicts these parameters from easily measurable physical properties, enabling rapid and cost-effective estimation without the need for complex laboratory testing. Based on a total of 199 sets of measured data from four rock types (shale, limestone, quartzite, and quartz-mica schist) in the Himalayan region, this study uses P-wave velocity ($V_p$), density ($\rho$), uniaxial compressive strength (UCS), and tensile strength (TS) as input variables. It employs four models: Support Vector Regression (SVR), Random Forest (RF), Multi-Layer Perceptron (MLP), and extreme gradient boosting (XGBoost) to predict $c$ and $\phi$. Hyperparameters were tuned using grid search and Bayesian optimization. We compared unified modeling with rock-type-specific modeling, performed interpretability analysis using SHapley Additive exPlanations (SHAP), and tested robustness by introducing Gaussian noise. The results show that XGBoost produced the best predictions at$c$ (test set $R^2$ = 0.9901, RMSE = 0.512 MPa), while the Bayesian-optimized SVR model yielded the best results at $\phi$ ($R^2$ = 0.9776, RMSE = 0.744°). Rock-type-specific modeling improved the $R^2$ for limestone at $\phi$ by 0.3541; the SHAP contribution for UCS and TS exceeded 70%; Random Forest demonstrated the best noise resistance, with a decrease in $R^2$ of less than 0.04 under 10% noise. In summary, the strategy proposed in this paper allows for the selection of prediction schemes based on data quality and lithological differences, providing a feasible approach for rapidly obtaining rock strength parameters. Full article

This research aims to investigate and improve the accuracy of porosity prediction in steel ingot casting by leveraging Thercast finite element simulations. In particular, the study refines the Niyama criterion through additional physical parameters and solidification modeling, aiming to reduce shrinkage porosity and enhance the overall mechanical reliability of cast components. Simulation results reveal that a lower thermal conductivity and faster cooling rates exacerbate shrinkage porosity, while a refined Niyama indicator using explicit solid-fraction weighting, with viscosity, alloy composition, and shrinkage accounted for through the underlying THERCAST material model, improves spatial localization of porosity-prone regions in the investigated case. For the investigated configuration, reducing the cooling rate to around 1.25 K/s decreased the extent of the simulated region classified as porosity-prone relative to the reference case. Furthermore, the analytical porosity–strength relation indicates a material-dependent reduction in strength when the porosity fraction exceeds 2%, underscoring the structural significance of internal voids. This study extends the practical interpretation of the classical Niyama criterion by combining solid-fraction weighting with material-dependent thermophysical inputs, addressing gaps in existing shrinkage porosity models. The approach integrates simulation findings with actual casting defects identified through ultrasonic scanning and metallographic analysis. By merging experimental insights with advanced finite element simulations, foundries can better regulate casting conditions, particularly cooling rates and thermal gradients, to minimize porosity. The refined porosity prediction framework aids in process optimization, improved material utilization, and superior quality assurance of steel ingots. Full article

This study investigates the compressive performance of fiber-reinforced polymer (FRP) rebar-reinforced Seawater and Sea Sand Concrete (SSC) columns through an integrated approach combining finite element analysis, theoretical derivation, and machine learning. Finite element models were developed to quantify the influence of key parameters on the ultimate bearing capacity and lateral deflection. The results indicate that the compressive capacity decreases significantly with increasing eccentricity and slenderness ratio. Columns reinforced with steel rebars demonstrated superior load-bearing and anti-lateral displacement capabilities compared to their FRP-reinforced counterparts. A theoretical formula for predicting the compressive capacity was derived; however, it systematically overpredicted the experimental measurements by approximately 36%. To develop data-driven predictive models for the ultimate load capacity of FRP–SSC columns, four machine learning models, backpropagation neural network (BPNN), bootstrap aggregating BPNN (Bagging-BP), genetic algorithm-optimized BPNN (GA-BP), and gradient boosting regression trees (GBRT), were employed. Using sectional dimension, concrete strength, reinforcement parameters, eccentricity, and slenderness ratio as inputs, the validation sets of the models achieved R-values of 0.942, 0.918, 0.933, and 0.990, respectively. Feature importance analysis based on SHAP identified eccentricity as the most influential parameter. Results from this work can help to understand the behavior of FRP–SSC columns under compression. Full article

Data regularization and removing noise from data are vital issues in evaluating the compressive strength (CS) of pozzolanic concrete. Because several admixtures are used in pozzolanic concrete in various percentages, past studies used machine learning (ML) techniques to predict the impact of admixture percentages on CS. It is still challenging to predict CS by considering the impact of such admixtures. Various ML techniques may be capable of overcoming these challenges. For this reason, the present study performed a comprehensive comparative analysis of analytical and ML approaches to predict the CS of pozzolanic concrete. A total of 1030 published experimental data points were utilized to develop five regression-based analytical and ML models. Seven statistical metrics were used to evaluate model performance. Eight variable parameters of experimental data were used to predict CS, such as cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and curing age of concrete. Random Forest (RF) shows the highest accuracy in predicting CS compared to other models within the CS range of 20 to 40 MPa. The $R^2$ value of RF is 0.99, which indicates good cross-validation. RF shows 15% higher accuracy compared to the best analytical model, although ML performs better than the analytical model. Concrete age and cement content represent the most effective factors in predicting CS based on SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs), while water exhibits a strong negative impact. Therefore, this study may be used as a guideline to predict CS for the development of sustainable concrete mix design. Full article

Industrial steel property prediction is often constrained by limited labelled data, reducing the effectiveness of conventional machine learning models. This study investigated whether metallurgy-informed feature engineering enhances predictive performance under small-data conditions. A representative set of 300 samples from an industrial low-carbon hot-rolled steel strip dataset (C: 0.02–0.06 wt%; Mn: 0.17–0.38 wt%) was used to derive five physically meaningful descriptors: carbon equivalent (CE), nitrogen-to-aluminum ratio (N/Al), microalloying efficiency index (MEI), thermal processing parameter (TPP), and solid solution strengthening index (SSSI). These descriptors were combined with the original 17 compositional and processing variables to create a 22-feature dataset. Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models were evaluated on an independent 60-sample test set using 5-fold cross-validation. Feature engineering improved the prediction accuracy, with the greatest gain observed for elongation. For XGBoost, the mean percentage error decreased from 3.23% to 3.05%, whereas the test-set R² increased from 0.4935 to 0.5444, representing a 10.3% improvement in the explained variance. For the yield strength, the Random Forest method increased the R² from 0.4744 to 0.4861. Permutation importance and partial dependence analyses identified MEI and TPP as the six most influential predictors across all targets, confirming that the engineered descriptors provide complementary metallurgical information. Learning curve analysis showed slightly higher cross-validation R² values at intermediate training sizes (n = 125–175), indicating modestly improved sample efficiency. These findings establish domain-informed feature engineering as an interpretable and practical strategy for improving machine learning in data-limited steel manufacturing processes. Full article

To solve the problems of explosive spalling and sharp deterioration of mechanical properties of ultra-high performance sprayed concrete (UHPS) under high-temperature conditions during tunnel fires, four groups of specimens with different volume contents (0%, 0.2%, 0.3%, 0.4%) of polypropylene fiber (PPF) were designed based on the optimal mix proportion at room temperature. Multi-gradient high-temperature tests at 20 °C, 200 °C, 400 °C, 600 °C and 800 °C were conducted to explore the effects of PPF on the high-temperature damage evolution and spalling resistance of UHPS. The test results show that no obvious spalling occurs in specimens exposed to temperatures of 400 °C and below. Surface peeling appears in the group without PPF addition at 400 °C, and severe explosive spalling happens in the 0% PPF group at 600 °C to 800 °C, while all PPF-incorporated groups maintain structural integrity. The mass loss rate increases with the rise in temperature and PPF content, reaching 15% in the 0.4% PPF group at 800 °C. In terms of mechanical properties, compressive strength, splitting tensile strength, and flexural strength all rise first and then decline with increasing temperature, and the 0.3% PPF group reaches peak values at 400 °C (compressive strength: 135.95 MPa, splitting tensile strength: 23.24 MPa, flexural strength: 27.42 MPa). Flexural toughness decreases continuously as temperature rises, and the 0.2% PPF group exhibits the best toughness retention. This study clarifies the high-temperature modification effect of PPF on UHPS and its optimal content range, providing important theoretical support and an experimental basis for the fire safety protection design of tunnel lining concrete. Full article

This study introduces a novel class of architected foam structures based on triply periodic minimal surface (TPMS) geometries for passive battery thermal management. Unlike conventional cold plates that rely on active pumping or high-conductivity solid materials, the proposed TPMS-like foams leverage convective fluid transport within a lightweight, electrically insulating polymer matrix. We present the design, fabrication, and experimental characterization of Schwarz Primitive TPMS structures manufactured via injection molding using silicone rubber, which has a comparable quality to additively manufactured polymer cold plates but with significantly lower manufacturing complexity and cost. The TPMS cold plates achieve passive fluid circulation without external pumps, reducing parasitic power consumption while maintaining thermal resistance values of approximately 23.5 K/W. Although its thermal resistance is higher than that of an aluminum plate of the same size (2.7 K/W), the TPMS cold plate is electrically insulating and offers additional safety benefits. Additionally, it can be fabricated from and filled with fire-retardant materials to prevent thermal propagation while maintaining a relatively low temperature gradient. Mechanical compression testing of TPMS foam samples with about 30% solid volume fraction showed a compressive strength of 86.2 kPa at 0.2 strain, equivalent to 30.5% of the compressive modulus of solid silicone (282.6 kPa). Compared to solid silicone plates (thermal resistance is 1420 K/W), the TPMS fluid-filled structures reduce thermal resistance by more than two orders of magnitude. This work establishes design rules, fabrication protocols, and performance benchmarks for TPMS-based passive cooling devices, offering a scalable pathway toward safer, lighter, and more energy-dense battery packs. Full article

Vernacular heritage conservation often separates material fabric, environmental adaptation, and social meaning. This study addresses that separation for one settlement. Socio-environmental heritage is defined here as heritage whose significance resides in the documented interdependence of environmental conditions, material practice, social organisation, and customary governance, rather than in fabric or imagery alone. The single purpose of the article is to develop and demonstrate an evidence-based method for interpreting and conserving Thee Ain Heritage Village in Al-Baha, Saudi Arabia, as such a system. A qualitative architectural case-study design combines a structured literature search, regional comparison, the author’s 2014 field observations and photographs, and published digital-heritage, energy-retrofit, and conservation studies; no human-participant data are analysed. Evidence is organised through three analytical layers—climatic material, socio-spatial, and customary governance—and each evidence–interpretation proposition is classified as directly observed, supported architectural inference, or hypothesis requiring measurement; conservation translation is treated as the output of this sequence rather than as a parallel analytical layer. Coded claim units are documented individually so that every interpretation and implication can be traced to its source, strength, and limitation. The analysis links rocky siting, stone and timber assemblies, thick load-bearing madameek walls, limited openings, vertical domestic hierarchy, controlled thresholds, and the agricultural setting to conservation priorities at landscape, construction, spatial, and adaptation scales. These priorities include compatible repair, retention of wall depth and opening logic, protection of privacy gradients and threshold sequences, and service integration without reducing the village to stone-clad imagery. Unlike previous work centred on digital documentation, energy modelling, or policy-level preservation, the contribution is an evidence-structured method linking architectural observation to bounded interpretation, conservation decisions, and explicit future testing requirements. Full article

Background/Objectives: Forensic age estimation increasingly relies on non-ionizing magnetic resonance imaging (MRI) evaluation of the knee growth plates. The original five-stage classification proposed by Dedouit et al. was developed using spin-echo proton-density-weighted imaging, and its applicability to gradient-echo sequences with different contrast mechanisms has not been fully established. This study aimed to apply the original, unmodified Dedouit classification to a volumetric three-dimensional Multiple Echo Data Image Combination (3D MEDIC) sequence and to generate corresponding age thresholds in a Turkish sample. Methods: Knee MRI examinations of 309 individuals (153 males, 156 females; age range 9.18–25.97 years) were retrospectively evaluated at a 3-Tesla field strength. Two experienced observers independently staged the distal femoral and proximal tibial epiphyses; intra- and inter-observer agreement were assessed using Cohen’s kappa. Spearman’s correlation and the Mann–Whitney U test, with rank-biserial effect sizes and Bonferroni correction, were used to assess the relationship between age and stage and between-sex differences, respectively. To provide forensically applicable thresholds, we additionally modeled the age at which the probability of complete fusion (Stage V) reached 50%, with 95% bootstrap confidence intervals, and calculated the sensitivity, specificity, and area under the curve (AUC) of Stage V for identifying individuals ≥18 years. Results: Agreement was very good for both epiphyses (kappa = 0.808–0.833). Age correlated strongly with stage for both the femur (rho = 0.70–0.76) and tibia (rho = 0.68–0.74). The 50%-probability age for complete fusion ranged from 15.23 years (tibia, females) to 17.43 years (femur, males). Stage V showed high sensitivity (0.96–0.98) but markedly lower and sex-dependent specificity for the 18-year threshold (0.48–0.58 in females versus 0.77–0.85 in males), indicating that Stage V alone is a poor sole criterion for confirming adult status in females. The youngest age at which Stage V was observed was 14.67 years in females and 16.07–16.16 years in males, markedly younger than thresholds reported using spin-echo imaging in the original Dedouit cohort. However, as an extreme-value statistic based on a single individual, this minimum should not be used as a forensic threshold in isolation. Conclusions: The Dedouit classification remains reproducible when applied to a 3D gradient-echo sequence, but the resulting age thresholds may be influenced by acquisition technique, population-specific factors such as socioeconomic status, or both. Consequently, thresholds derived from different MRI sequences or populations should not be assumed to be interchangeable without local validation. Full article

Homogeneous TC4 titanium alloy suffers from the strength–ductility trade-off and exhibits insufficient anti-penetration capacity under high-strain-rate impact. TiC_p-reinforced functionally graded titanium matrix composites (FGTMCs) with a “hard outer, tough inner” gradient architecture are promising lightweight armor materials. The use of Abaqus/Explicit finite element simulation for FGTMCs under dynamic impact can capture transient deformation and damage evolution while enabling rapid evaluation of the impact resistance of different materials; however, research in this area remains scarce. This study conducts a systematic parametric analysis of key simulation parameters, including calibration of the Johnson–Cook constitutive and damage model parameters for TC4 titanium alloy, optimization of mesh partitioning strategies, hourglass control schemes, model dimensions, and boundary conditions to suppress “ghost mesh” numerical artifacts. Material property assignments for TC4 and three typical titanium matrix composites are designed, along with a methodology for constructing functionally graded material models. Two projectile–target matching configurations (small projectile/thin target vs. large projectile/thick target) are compared, and the optimal model of a 700 m/s small-caliber tungsten projectile impacting a 50 mm TC4 target is identified. Parametric analysis demonstrates that a damage parameter D4 = 0.1 significantly improves numerical stability, and a graded mesh strategy with further refinement along the penetration path balances computational accuracy and efficiency. Using the optimized material system, the simulation results reproduce the three-stage damage evolution of titanium alloys under impact—cratering, plastic penetration, and back-face spallation—providing reliable numerical support for the structural optimization of graded armor materials. Full article

Soft-ground finite-element analyses commonly reduce curved Oedometer compression to one constant slope, obscuring where stress-level curvature affects boundary-value predictions. AJOP-T embeds the differentiable Arc Joint via Optimum Parameters map in continuous teardrop bounding-surface plasticity while retaining the inherited yield geometry, non-associated flow, radial mapping and SMP-transformed stress. High-order denotes only the map’s derivative hierarchy: its first two derivatives define tangent hardening and hardening curvature, not gradient, fractional, nonlocal or rate order. This first-phase formulation is deliberately rate-independent and retains constant κ to isolate compression-map hardening; time-dependent and nonlinear cyclic swelling responses are outside its claims. The formulation recovers constant-slope hardening asymptotically, yields a closed-form admissibility boundary, is invariant under SMP, and recovers the parent isotropic normally consolidated settlement equation. Four natural-clay compression maps were fitted; triaxial evidence is fitted for comparison except for one held-out Eastern Osaka extension path. Three implementations agree to at least five significant figures. Paired undrained strip-footing analyses reduce centre settlement by 31.8% in the curved regime but only 0.27% near the high-stress asymptote. A predicted 1.6% low-stress strength-ratio drift is below the reviewed data scatter and is not claimed as experimentally validated. Full article

Frozen soil on alpine slopes is influenced by inclination and aspect-induced differential solar radiation effects, resulting in non-uniform temperature fields and inclined layered ice lenses that enhance anisotropy and degrade mechanical properties. This study investigates the deformation and strength responses of frozen soil under different temperature-gradient magnitudes and orientations and ice-lens conditions. A stress–strain constitutive model incorporating the magnitude and orientation of the temperature gradient is established. In addition, an equal-scale discrete element model based on the parallel-bond contact model is developed and calibrated against the laboratory results. The numerical specimen is divided into 13 layers, and temperature-dependent interparticle bond properties are assigned layer by layer to reproduce the prescribed magnitude and orientation of the temperature gradient. Results show that the orientation of the temperature gradient significantly alters the mechanical response and failure mode. As the inclination angle increases, the failure mode transitions from compressive dilatancy to combined dilatancy–shear failure and ultimately to shear-dominated failure. At −10 °C, increasing the inclination angle from 0° to 30° reduces the compressive strength by 44.48%. The elastic modulus also decreases with increasing inclination, with a maximum inclination-induced difference of 111.98 kPa. Moreover, the presence of an ice lens further reduces specimen stiffness, and the elastic-modulus difference between ice-lens-bearing and ice-lens-free specimens increases from 5.57 kPa at −1 °C to 75.72 kPa at −10 °C. The DEM results show that particles at the top and bottom of the specimen primarily undergo vertical displacement, whereas particles in the middle region exhibit dominant horizontal displacement, forming an X-shaped shear band. The inclined temperature gradient produces a heterogeneous distribution of interparticle bond strength within each horizontal layer. As inclination increases, the shear band evolves from symmetric to asymmetric; particle displacements on the side toward which the temperature gradient points are larger than those on the opposite side, revealing the microscopic origins of macroscopic mechanical behavior. Full article

Tropospheric evaporation ducts can trap radio waves within a refractive layer, which may guide signals above 1 GHz, enabling beyond-line-of-sight transmission. This makes duct-assisted propagation attractive for maritime communications. The marine environment is characterized by complex hydrometeorological variability and frequent extremes, particularly tropical cyclones, which can perturb duct properties and degrade link reliability. This study develops a multivariate cyclone-aware nonlinear regression framework (CNRF) to estimate contemporaneous evaporation duct strength (EDS) by integrating high-resolution dropsonde observations with tropical-cyclone descriptors from the International Best Track Archive for Climate Stewardship (IBTrACS). The framework uses CatBoost, natural-gradient boosting (NGBoost), and a multilayer perceptron (MLP) as base learners, with a random forest (RF) serving as the second-stage nonlinear fusion model. Rather than relying solely on bulk physical parameterization, the framework aims to represent the nonlinear influence of tropical cyclone-related environmental factors on duct strength. Evaluated over 1996–2024, the CNRF attains a test-set R² of 0.791 and a root mean square error (RMSE) of 5.350 M-unit, corresponding to a 23.5% improvement in RMSE over the Naval Postgraduate School (NPS) numerical model. For Hurricane Fiona (2022), the model achieves an RMSE of 6.260 M-unit, and the inclusion of tropical cyclone descriptors improves RMSE by approximately 17.0% relative to a model that excludes tropical cyclone information. The proposed framework facilitates quantitative assessment of extreme-weather-driven duct variability and supports robust design and operation of duct-enabled maritime communication systems. Full article

Resistance spot welding (RSW) is the primary joining technology for automotive advanced high-strength steels (AHSSs), where the inherent severe thermal cycles profoundly alter the heat-affected zone (HAZ) microstructure, leading to localized variations in mechanical properties. Characterizing the spatial gradients in microstructure and the resulting mechanical properties remains a major challenge in weld failure analysis due to the small size of the HAZ and its complex thermal history. In this study, the HAZ of two prominent AHSS grades, a first-generation press hardening steel (PHS1500) and a third-generation quenching and partitioning steel (Q&P1180), was physically simulated using a Gleeble^® 3180 thermomechanical simulator to achieve precise control over the localized thermal cycles. The investigation first evaluated the role of thermal cycle duration, governed by the welding time parameter (300 ms vs. 800 ms), on the microstructural evolution of the PHS1500 steel. Increasing the weld time from 300 ms to 800 ms reduced the cooling rate under the nominal 1400 °C condition from approximately 3000 K/s to 2500 K/s; however, no marked change was observed in the overall microstructural and hardness trends within the investigated range. Subsequently, using the 300 ms thermal profile as a reference baseline, a comparative metallurgical study was conducted between PHS1500 and Q&P1180. Under the same 300 ms thermal history, the maximum hardness reduction relative to the corresponding base material was approximately 42% for PHS1500 and 12% for Q&P1180. The hardness minima were located within FE-estimated temperature ranges close to the Ac₁ region for PHS1500 and around 600 °C for Q&P1180, respectively. This comparison highlighted the distinct microstructural responses of the two generations across the upper-critical (UCHAZ), inter-critical (ICHAZ), and sub-critical (SCHAZ) zones. Moreover, microhardness profiles were correlated with the microstructural findings, establishing a correlation among the simulated thermal history, the observed microstructural evolution, and localized mechanical performance. Full article

This study examines the evolution of microstructure, the metastability of retained austenite (RA), and the corresponding mechanical behavior exhibited by a nanostructured bainitic bearing steel subjected to prolonged austempering within a transformation stasis regime. The results indicate that following the completion of nanostructured bainitic formation at 300 °C for 3 h, a prolonged austempering time does not alter the microstructure, but reduces the dislocation density in BF while increasing the carbon content in RA. For the 4 h and 6 h specimens, a reduction in the overall RA mechanical stability is observed, accompanied by different transformation rates of stress-induced martensite during tensile deformation. This behavior is largely due to the weakened constraint effect of the BF matrix and the evolution of a carbon concentration gradient within the RA. During the transformation stasis, prolonged austempering elevates the yield strength while maintaining an unchanged ultimate tensile strength, albeit with a marginal reduction in microhardness. Relative to the baseline elongation recorded for the 3 h specimen, both the 4 h and 6 h specimens exhibit enhanced ductility, with the 4 h specimen yielding a peak value of 16.8%, which is 1.66 times that of the 3 h specimen. This improvement stems largely from the greater RA volume fraction that transforms into stress-induced martensite in the 4 h specimen, as well as its continuous and stable transformation rate during tensile deformation. Therefore, it can be concluded that an appropriately prolonged austempering time within nanostructured bainitic transformation stasis is essential for optimizing mechanical performance. This study provides a low-cost, energy-saving isothermal heat treatment technical scheme for mass industrial production of high-performance bearing steel. Full article