Search Results (1,180)

Flood and flash flood events can generate severe hydraulic actions on civil structures, requiring modeling strategies able to link flow features to structural damage. This paper proposes a two-scale numerical framework based on advanced finite element modeling to assess the vulnerability of structures subjected to inundation and flood-driven impact. At the macroscale, the flood propagation and the interaction with the built environment are simulated through the depth-averaged Shallow Water Equations, adopting a time-explicit interface treatment to capture the evolution of the free surface. The macroscale model provides time-dependent water depth and flow velocity along the external surfaces of the structure, which are then used to derive hydrostatic and hydrodynamic actions, also in comparison with code-based formulations. At the mesoscale, these actions are transferred to a detailed structural model to investigate the nonlinear mechanical response of the building. Structural components are described through a coupled damage–plasticity constitutive law, enabling the prediction of stiffness degradation, cracking-driven damage patterns, and the identification of the most critical structural zones under flood loading. The proposed workflow is finally applied to a real structure located in the municipality of Cosenza (Italy), demonstrating the capability of the approach to combine hydraulic intensity measures with physics-based structural damage assessment, supporting scenario analyses and risk mitigation evaluations. Full article

High-resolution soil depth maps are valuable for environmental modelling, yet reliable data remains scarce in the tropics. This study evaluates the feasibility of mapping depth to bedrock (DTB) in Sri Lanka using a legacy dataset (n = 88) and global environmental covariates (n = 247). A robust machine learning workflow was employed—including feature selection, hyperparameter tuning, and a stacked ensemble of four algorithms (Random Forest, XGBoost, Cubist, SVM)—to test the limits of global data for local mapping. Despite rigorous optimization, the final ensemble model achieved a performance of R² = 0.197 (RMSE = 35.4 cm) under spatial cross-validation. While still modest, this result significantly outperforms existing global products and quantifies the “prediction gap” inherent in using ~1 km resolution global covariates to model micro-scale soil variability. An initial exploration involved log-transforming the target variable; however, following rigorous testing, the untransformed depth was modelled directly to avoid bias in back-transformation. A robustness experiment was further conducted, reducing predictors from 24 to 12, which degraded performance, confirming that the model captures complex, physically meaningful climatic interactions rather than fitting noise. The study concludes that while global covariates can capture regional meso-scale trends (explaining ~20% of variance), they are insufficient for resolving local micro-relief (<50 m). The resulting map and uncertainty products provide a critical “baseline” for national planning, but effectively demonstrate that future improvements will require investment in higher-resolution local covariates (e.g., LiDAR) rather than more complex algorithms. Full article

Mesoscale modeling provides an efficient and cost-effective approach for investigating the damage mechanisms of fiber-reinforced concrete. To address the physical distortion in conventional models that arises from neglecting the volumetric effect of steel fibers and to construct a more realistic random mesoscale model of steel fiber-reinforced concrete (SFRC), this study proposes an efficient modeling method based on geometric entity expansion and point–line topology. First, polygonal aggregates with diverse morphologies are generated using a polar-coordinate perturbation scheme combined with a convex-hull correction algorithm. Next, abandoning the traditional zero-thickness line-segment assumption, steel fibers are expanded into rectangular entities via rigid-body kinematics to explicitly represent their excluded volume. Furthermore, a vector-cross-product-based Point–Line Method is developed to replace conventional circumscribed-circle screening, enabling accurate discrimination of interference interactions between fiber–aggregate and fiber–fiber pairs. An automated framework—consisting of skeleton placement, entity generation, topological discrimination, and mesh mapping—is implemented through a Python 3.13.9 scripting interface, allowing efficient batch generation of high-content mesoscale models with aggregate area fractions up to 70%. The proposed model is then used to simulate the failure process of SFRC specimens under uniaxial compression and benchmarked against experimental results. The results show that the developed mesoscale model accurately reproduces the nonlinear mechanical response and the strengthening–toughening effects of SFRC, achieving a relative error of only 0.31% in peak stress and a root mean square error (RMSE) as low as 1.70 MPa over the full stress–strain curve. The simulations not only confirm the pronounced strength gain due to steel fiber incorporation (~19.7%), but also reveal, at the mesoscale, the mechanism by which fiber bridging suppresses damage localization, thereby demonstrating the reliability and practical effectiveness of the proposed modeling approach. Full article

Accurate short-term lightning forecasting requires reliable representations of both lightning occurrence and intensity, as well as the underlying convective processes. While ensemble prediction systems (EPSs) provide valuable probabilistic information, their ability to resolve mesoscale and convective-scale variability remains limited. In this study, we assess the added value of mesoscale information for probabilistic lightning forecasting over eastern China. A mesoscale ensemble is constructed from deterministic forecasts of the China Meteorological Administration (CMA) Mesoscale Model (MESO) using spatiotemporal neighborhood and time-lagged techniques and is combined with predictors from the CMA Regional Ensemble Prediction System (REPS). Lightning occurrence and counts are modeled within a Bayesian additive model for location, scale, and shape (BAMLSS) framework, using a hurdle-based count regression to account for excess zeros and overdispersion. Influential nonlinear predictors are selected via stability selection combined with gradient boosting. Forecast performance with and without MESO-derived predictors is systematically evaluated. The results indicate that incorporating mesoscale information generally improves forecast skill for both lightning occurrence and intensity across multiple verification metrics. These improvements are associated with MESO-derived predictors related to convective available potential energy and convective precipitation, suggesting the importance of mesoscale processes for probabilistic lightning forecasting. Full article

Sol–gel phase transitions are complex far-from-equilibrium processes characterized by limited reproducibility, whose origin remains poorly understood and rarely quantified. We investigated the thermally induced sol–gel transition of agar using turbidimetry. A phenomenological model was applied to extract key kinetic parameters (maximum absorbance, maximum rate, and characteristic times) from 96 independent replicates. Variability was quantified and compared with that of an enzymatic reaction exhibiting similar sigmoidal kinetics, allowing for separation of experimental, intrinsic, and nonergodic contributions. Agar gelation displays markedly higher variability. The total variability (CV ≈ 16%) exceeds both the experimental error (1–2%) and the nonergodic contribution (≈2%), demonstrating that it predominantly arises from intrinsic process dynamics. Variability increases sharply during early stages of gelation and then evolves more gradually, indicating that stochastic nucleation and network formation pathways drive divergent kinetic trajectories despite identical initial conditions. Variability in gelation is therefore not a measurement artifact but an intrinsic hallmark of the sol–gel transition. This inherent stochasticity limits the predictive power of deterministic models, particularly at meso- and microscopic scales, and should be considered a fundamental feature of gel-forming systems. Our approach provides a quantitative framework for characterizing variability in phase transitions and may be extended to more complex biological and soft matter systems. Full article

This review surveys how nanoparticles and biomolecular nanosized structures interact with model membrane systems, and how these interfacial processes govern their performance in drug and gene delivery, antimicrobial strategies, biosensing, and nanotoxicology. The nanostructures covered include polymeric nanoparticles, lipid-based carriers, peptide nanostructures, dendrimers, and multifunctional hybrids. Model membranes span Langmuir monolayers, supported lipid bilayers, vesicles/liposomes across sizes, and emerging hybrid or asymmetric constructs that better approximate native complexity. Mechanistically, interactions follow recurrent routes—surface adsorption, bilayer insertion, pore formation, and lipid extraction/reorganization—regulated by particle size, morphology, charge, ligand architecture, and lipophilicity, in conjunction with membrane composition, phase state, curvature, and asymmetry. A multiscale toolkit links structure, mechanics, and dynamics: Langmuir troughs and Brewster Angle Microscopy map thermodynamics and mesoscale morphology; atomic force microscopy and quartz crystal microbalance with dissipation resolve nanoscale topography and viscoelasticity; fluorescence microscopy/spectroscopy reports on localization and packing; neutron and X-ray reflectometry quantify vertical structure; molecular dynamics provides atomistic pathways and design hypotheses. Historically, the field advanced from early monolayers and bilayers, through the fluid mosaic model, to raft microdomains and modern biomimetic systems, enabling increasingly realistic experiments. Key advances include cross-method integration linking experimental observations with image-based computational models; persistent debates concern the translation from simplified models to living membranes, the role of dynamic coronas, and scale/force-field limits in simulations. Future efforts should prioritize hybrid models incorporating proteins and asymmetric lipidomes, standardized reporting and reference systems, rigorous coupling of experiments with calibrated simulations and machine learning, and alignment with safety-by-design and regulatory expectations, thereby shifting interfacial measurements from descriptive observation to predictive design rules. Full article

This study presents a distribution-optimized mesostructure estimation method for statistically modeling near-surface aggregate size distributions in concrete by optimizing the spatial arrangement of polydisperse spherical aggregates with respect to formwork boundaries. The approach is based on minimizing the deviation between a generated cumulative aggregate volume function and an idealized linear target function corresponding to a constant area fraction along the specimen depth. To enable efficient computation for systems containing a large number of aggregates, grain size groups derived from the grading curve are represented using symmetric Beta distributions, allowing each group to be described by a single shape parameter. The resulting optimization problem is solved using a derivative-free Powell algorithm. The method inherently captures wall effects, leading to a migration of smaller aggregates toward the specimen boundaries to compensate for the geometric constraints of bigger aggregates. Experimental validation was performed for a single concrete mixture and specimen geometry by determining the depth-dependent mean bulk density of a concrete cube using incremental surface grinding combined with high-resolution 3D laser scanning. The optimized mesostructure shows strong agreement with measured density profiles for the investigated specimen. While the validation is limited to a single mixture and geometry, the results indicate that the proposed method is a computationally efficient approach for incorporating wall effects into mesoscale concrete models. Furthermore, increasing aggregate volume fractions intensify the near-surface accumulation of fine particles. Full article

Existing mesoscopic numerical models still exhibit shortcomings in terms of the aggregate geometric fidelity, interface transition zone (ITZ) characterization, and modeling efficiency. To solve these problems, this paper establishes a two-dimensional mesoscopic model and analysis method for concrete, considering randomly distributed convex polygons of aggregate grains and a three-phase structure comprising aggregate, mortar, and ITZ. An efficient random placement algorithm based on background meshing is proposed to enable rapid and accurate model construction. The effects of aggregate geometry, spatial distribution, and ITZ on mechanical properties and damage evolution have been systematically studied. A quantitative relationship has been established between damage energy and the decay of strength and stiffness, and damage quantification indices have been proposed. The damage rates of mortar and ITZ, along with the variation characteristics of the damage variable d_c at each stage, have been quantified. Neglecting the ITZ leads to overestimation of the peak strength and stiffness of concrete while exacerbating its post-peak brittle behavior. The most significant increases occur in both stiffness decay and damage growth at 90% of peak stress. A sudden change occurs at approximately 0.17% axial strain (corresponding to 80% of peak stress). This study offers a meso-scale foundation for understanding concrete failure and designing high-performance concrete. Full article

Italy and the surrounding seas are recognised as one of the European hotspots for tornadoes and waterspouts. In recent years, the town of Rosignano Solvay (on the Northern Tyrrhenian coast) experienced repeated waterspouts affecting the same areas, raising local concern about the possible influence of heated wastewater discharged into the sea by a nearby industrial site. We reconstruct the mesoscale meteorological conditions of four intense waterspouts near Rosignano Solvay using a limited-area weather model at a high-to-very-high resolution (inner domain grid spacing of 500 m; sensitivity tests at 100 m). At the reported event times, the intensity of key mesoscale precursors (low-level wind shear, 1 km storm-relative helicity, maximum updraft intensity, and lifting condensation level) is consistent with the values typically associated with EF1 (or stronger) tornadoes and waterspouts. The model systematically predicts the peak of instability indices 2–3 h earlier than the reported event times. For one case study, we conduct two sea surface temperature sensitivity experiments to assess the potential atmospheric impact of heated wastewater discharge (temperature increases of +1.5 K and +5 K over a 10 km² area). The resulting changes in instability indices are marginal, with differences of at most 3% relative to the control run. A simple mass-balance estimate for the modified sea patch suggests that, given the reported discharge rates, a plausible impact of the warm water released from the industrial site could lead to an increase in the local sea surface temperature of approximately +0.7 °C over two months. We conclude that synoptic and mesoscale conditions primarily govern waterspout initiation in this region, while the direct effect of the small warm coastal plume from the industrial discharge appears to be minor. Full article

The development of green building materials and high-performance concrete has promoted the use of manufactured sand (MS) in self-compacting concrete (SCC). To investigate the effect of MS lithology on concrete performance, this study prepared C40-SCC using basalt, limestone, and granite manufactured sand, as well as river sand. Workability and mechanical properties were measured via macro-scale tests. A meso-scale random aggregate model, including mortar, aggregate, and interfacial transition zone (ITZ), was established to simulate uniaxial compression. The macro-test results indicate that workability decreases in the order of river sand, granite, limestone, and basalt, while mechanical strength decreases in the order of granite, limestone, basalt, and river sand. The meso-scale simulation reveals that damage initiates at the ITZ and extends into mortar. The simulated stress–strain curves match the experimental data in the ascending branch, with peak stress errors between 1.1% and 6.9%. The failure modes also align with experimental observations. The consistency between the simulation and experimental results verifies the reliability of the meso-scale model. By combining macro-experiments and meso-simulation, this study compares concrete performance and explains the differences from the perspective of damage evolution. The results indicate that MS lithology affects interfacial properties and damage development, thereby determining macro-mechanical behavior. This research provides a theoretical basis for the appropriate selection of MS in SCC. Full article

Coastal seas around Aotearoa, New Zealand, are among the least observed parts of the global ocean, limiting our ability to monitor and forecast marine conditions. The Moana Project addresses this gap with a new observing system that includes temperature sensors mounted on commercial fishing gear—the Mangōpare fishing vessel network. This study presents the first evaluation of New Zealand’s operational ocean 4D-Var data assimilation system that incorporates these fishing vessel (FV) observations into a regional ROMS model. Using just over one year of operational forecasts, we show that FV temperature profiles significantly improve subsurface temperature representation, especially in coastal regions where satellite products have warm biases or miss key features such as upwelling and mesoscale variability. Assimilation of FV data reduces background temperature biases throughout the upper ocean and enhances forecast skill in areas influenced by major currents and dynamic coastal processes. We also identify sensitivity to periods of missing satellite sea surface temperature, which can lead to overfitting of the available observations. Overall, the results demonstrate that FV observations provide essential subsurface information and can substantially strengthen operational coastal ocean forecasting systems. Full article

This review examines fatigue damage in cement-based materials across the micro-, meso-, and macroscales, with emphasis on how damage initiates, transfers, and becomes structurally observable under cyclic loading. At the microscale, capillary pores, unhydrated cement particles, and the calcium–silicate–hydrate (C-S-H) phase govern local stress concentration, bond rupture, limited healing, and microcrack development. At the mesoscale, the interfacial transition zone (ITZ), cement paste, aggregates, and fiber reinforcement effects control crack initiation, deflection, bridging, and coalescence. At the macroscale, specimen size, boundary conditions, loading regime, and environmental exposure shape stiffness degradation, residual strain accumulation, crack growth, and fatigue life. Beyond summarizing existing studies, this review synthesizes a causal damage transfer interpretation that links microscale deterioration, mesoscale crack interaction, and macroscale response. Current gaps include the limited quantitative link between microstructure-informed models and three-dimensional experimental observations, the still-incomplete validation of multiscale predictive frameworks, and the insufficient treatment of coupled fatigue–environment effects. Addressing these gaps is essential for more reliable fatigue life prediction and for developing durable, resource-efficient concrete infrastructure. Full article

Taking the Daliang Tunnel of the Lanzhou–Xinjiang High-speed Railway crossing a reverse strike-slip fault as the engineering background, seismic damage investigations of the Daliang Tunnel and other cross-fault tunnels under earthquake action were conducted. Using 1:50 meso-scale model tests, experimental analyses were carried out on the lining strain response, internal crack development and failure, and surrounding rock pressure variation during fault dislocation. The failure modes and mechanisms of tunnels crossing reverse strike-slip faults were thoroughly explored. Meanwhile, a three-dimensional numerical model of the Daliang Tunnel was established to investigate the influence of dislocation modes with structural zonation within the fault zone on the surrounding rock response. The results indicate that the damage and strain response of the tunnel lining are mainly distributed within the fracture zone, predominantly characterized by combined oblique shear and compression failure. Due to the displacement of the lining induced by strong surrounding rock movement, surrounding rock pressure exhibits considerable variation at the boundaries of the fracture zone, accompanied by certain void detachment phenomena. The overall deformation of the tunnel crossing the reverse strike-slip fault presents an “S”-shaped pattern, which is consistent with the numerical simulations. The compression and dislocation morphology of the sidewalls within the rupture surface is in good agreement with the point cloud plan view. The compressive deformation and strain of the surrounding rock are most significant within the rupture surface. Meanwhile, the soft-to-hard transition segments between the new fracture zone and the rupture surface, as well as between the rupture surface and the influence zone, exhibit a trend of first decreasing and then increasing. Full article

Addressing the thermal management challenges of prismatic aluminum shell lithium battery modules in electric vehicles under high-rate charge–discharge conditions, this study proposes a multi-objective optimization design method for integrated biomimetic liquid cooling plates. By integrating various highly efficient heat transfer structures from nature, including fractal-tree-like networks, leaf vein branching systems, and spider web radial distribution, a novel biomimetic liquid cooling plate topology was constructed. A multi-physics coupled numerical model considering electrochemical heat generation, thermal conduction, convective heat transfer, and thermal stress deformation was established. The NSGA-II algorithm was employed to globally optimize 12 design variables including channel geometric parameters, operating conditions, and structural dimensions, achieving collaborative optimization objectives of maximum temperature minimization, temperature uniformity maximization, pressure drop minimization, and structural lightweighting. The weight coefficients for the four optimization objectives were determined through the Analytic Hierarchy Process (AHP) with verified consistency (CR = 0.02 < 0.10), ensuring rational priority allocation aligned with automotive safety standards. The optimization results demonstrated that compared to the initial design, the optimal solution reduced the maximum temperature under 3C discharge conditions by 9.9% to 34.7 °C, decreased the temperature difference by 31.3% to 3.3 °C, lowered the pressure drop by 24.6% to 2150 Pa, reduced structural mass by 4.0%, and decreased maximum stress by 16.7%. Quantitative comparison with single biomimetic structures under identical boundary conditions showed that the integrated design achieved a 3.3% lower maximum temperature and 25.7% better flow uniformity than the best-performing single structure, demonstrating the synergistic advantages of multi-biomimetic integration. These synergistic performance improvements can be attributed to the hierarchical multi-scale architecture where fractal networks provide macro-scale flow distribution, leaf vein branches ensure meso-scale coverage, and spider web radials achieve micro-scale thermal matching. Long-term cycling tests conducted at 1C/1C rate with 25 ± 1 °C ambient temperature showed that the optimized design maintained a capacity retention rate of 92.3% after 1000 charge–discharge cycles, demonstrating excellent durability. The complex biomimetic channel structure can be fabricated using selective laser melting technology with minimum feature sizes below 0.3 mm, indicating promising manufacturing feasibility. The research findings provide theoretical guidance and technical support for the engineering design of high-performance battery thermal management systems. Full article

Due to the complex mesostructure of calcareous sand, accurately predicting the mechanical response of Asphalt-Bonded Calcareous Sand (ABCS) is extremely challenging. This study pioneers the development of a mesoscale model for ABCS that explicitly incorporates the Interfacial Transition Zone (ITZ) via a random particle algorithm. To overcome the efficiency bottlenecks of traditional time-domain integration, this study establishes a mesoscale framework coupling a random polygonal aggregate algorithm with direct Steady-State Dynamics (SSD) analysis. A major advantage of this framework is its capacity for large-scale parametric sensitivity analysis; herein, 920 independent mesoscale models were generated and rapidly solved across the broadband frequency domain. The framework was rigorously validated, demonstrating high predictive accuracy for both the baseline calibration and an independent 12% asphalt content mixture (baseline R² = 0.99, MAPE = 6.94%; independent validation R² = 0.96, MAPE = 9.73%). Notably, the SSD approach completes calculations (10⁻³ to 10³ Hz) for 10 massive 300 mm RVEs in just 6.5 min. Leveraging this high-throughput capability, the extensive parametric analysis reveals that variations in maximum aggregate size negligibly impact the dynamic modulus under a constant volume fraction. Conversely, an optimal Interfacial Transition Zone (ITZ) thickness of ~75 µm was identified, representing a physical equilibrium between interfacial reinforcement and bulk binder cohesion. Furthermore, an analytical RVE size criterion of 1.7–5.3 times the maximum aggregate size is proposed to satisfy a 5% engineering error tolerance, providing a highly efficient numerical tool for the virtual mix design of reef pavements. Full article