Search Results (309)

This study proposes the first ground motion prediction equation (GMPE) for the parameter I_Np, an intensity measure based on the spectral shape. A Machine Learning Algorithm based on Support Vector Machines (SVMs) was employed due to its robustness towards outliers, which is a key advantage over ordinary linear regression. I_Np also offers a more robust measure of the ground motion intensity than the traditionally used spectral acceleration at the first mode of vibration of the structure Sa(T₁). The SVM algorithm, configured for regression (SVR), was applied to derive the prediction coefficients of I_Np for diverse vibration periods. Furthermore, the complete dataset was analyzed to develop a unified, generalized expression applicable across all the periods considered. To validate the model’s reliability and its ability to generalize, a cross-validation analysis was performed. The results from this rigorous validation confirm the model’s robustness and demonstrate that its predictive accuracy is not dependent on a specific data split. The numerical results show that the newly developed GMPE reveals high predictive accuracy for periods shorter than 3 s and acceptable accuracy for longer periods. The generalized equation exhibits an acceptable coefficient of determination and Mean Squared Error (MSE) for periods from 0.1 to 5 s. This work not only highlights the powerful potential of machine learning in seismic engineering but also introduces a more sophisticated and effective tool for predicting ground motion intensity. Full article

This article presents an extensive experimental investigation of the dynamic characteristics of three-arch historical masonry bridges, using Operational Modal Analysis (OMA). The research thoroughly characterizes the dynamic behavior of four representative masonry bridges from the Apulia Region in Southern Italy through detailed experimental campaigns. These campaigns employed calibrated and optimally implemented accelerometric monitoring systems to acquire high-quality dynamic data under controlled excitation and environmental conditions. The selected bridges include the Santa Teresa Bridge in Bitonto, the Roman Bridge in Bovino, the Roman Bridge in Ascoli Satriano and a moderner road bridge on the Provincial Road SP123 in Troia; they span almost two millennia of construction history. The experimental framework incorporated several non-invasive excitation methods, including controlled vehicle passes, instrumented hammer impacts and ambient vibration tests, strategically chosen for optimal signal quality and heritage preservation. This investigation demonstrates the feasibility of capturing the dynamic behavior of these complex and specific historic structures through customized sensor configurations and various excitation methods. The resulting natural frequencies and mode shapes are accurate, robust, and reliable considering the extended data set used, and have allowed a rigorous seismic assessment. Eventually, this comprehensive data set establishes a fundamental basis for understanding and predicting the seismic response of several three-span masonry bridges to accurately identify their long-term resilience and effective conservation planning of these valuable and vulnerable heritage structures. In conclusion, the data comparison enabled the formulation of a predictive equation for the identification of the first natural frequency of bridges from geometric characteristics. Full article

Scaffolds are temporary structures that workers usually use during building or repair work. These structures can be built in different shapes and types depending on the type of joints to which the beams and columns of the scaffolds are connected. Due to their temporary nature, they are very sensitive to vibration under dynamic or static actions, and this causes many accidents and unstable behaviours in them. This unstable behaviour has different reasons, including bracing conditions and slenderness of the columns, stiffness of joints and anchors, imperfections in the construction, damage and corrosion due to climate change, etc. This article aims to reanalyse the mechanical properties of scaffold joints and anchors and obtain some critical factors in the overall stability of the mentioned structures, including load-bearing capacity, initial stiffness, energy absorption, and ductility. To this aim, some recent research on scaffolds has been summarised and discussed, and then the failure mode and mechanical behaviour of the scaffolds in different types of scaffold joints and anchors have been estimated and considered from previous studies. Moreover, some mechanical properties, including ductility, initial stiffness, and energy absorption, have been estimated and developed based on the force-displacement curves of previous studies. The results highlight the crucial importance of the mechanical properties and behaviour of anchors and joints in estimating the behaviour and stability of scaffolds. The results also revealed that determining the mechanical characteristics of the mentioned elements can have a significant influence on the optimisation and design of scaffolds more accurately and predictably. Moreover, determining the mechanical properties of the anchors and joints can enhance our insights and understanding of how the mentioned parameters can improve the behaviour, stability, and safety of the scaffold structures. Full article

Corrosion-induced cracking of reinforced-concrete (RC) covers is well known, yet key knowledge gaps persist. Most studies isolate uniform corrosion or a single non-uniform corrosion pattern and ignore the effects of boundary restraint and structural configurations, leading to inaccurate predictions of cracking thresholds and crack propagation patterns. This study systematically investigates the influence mechanisms of constraint conditions and structural configurations on corrosion-induced cracking behavior using the phase-field model. The results indicate that the non-uniformity of steel corrosion is a critical factor governing cover cracking. As the corrosion non-uniformity coefficient increases, the critical corrosion level exhibits a monotonic decreasing trend—from 0.95% to 0.15% under strong constraints and from 0.52% to 0.15% under weak constraints. Concurrently, the crack morphology evolves from a single radial crack to a wedge-shaped crack oriented toward the peak corrosion side. The influence of constraint conditions is dualistic, while strong constraints enhance the failure threshold, their mitigating effect diminishes markedly under highly non-uniform corrosion. The critical corrosion threshold for eccentrically arranged corner reinforcement is significantly lower than that for centrally arranged reinforcement; the corrosion angle only induces slight crack deflection and minor threshold fluctuations; and the curved top section, due to its weaker equivalent constraint, exhibits inferior crack resistance compared to the linear top section. Three-dimensional analysis reveals a pronounced longitudinal discreteness effect, which not only substantially elevates the critical corrosion threshold but also leads to diverse spatial failure modes. This work links rust-expansion eigen-displacement to crack propagation within a unified phase-field framework, providing materials-level criteria for evaluating corrosion tolerance and guiding the design of cover materials and reinforcement layouts to enhance durability. Full article

Ultra-High-Performance Concrete (UHPC) is a game-changing, innovative material with the merits of exceptional tensile strength, making it suitable for stirrup-free UHPC beams. In this study, two 4.0 m-long large-scale stirrup-free I-shaped UHPC beams were experimentally explored in bending tests and shear tests. Cracking patterns, failure modes, and ultimate load-bearing capacity were obtained. Experimental findings revealed that the shear capacity of the stirrup-free I-shaped UHPC beams with a web thickness of merely 50.0 mm reached more than 20.0 MPa and demonstrated excellent post-cracking shear behavior. Finite element models were established and verified with experimental results to investigate the shear behaviors of stirrup-free I-shaped UHPC beams, considering the parameters of shear span-depth ratio and longitudinal reinforcement strength. The results demonstrated that as the shear span-depth ratio increases, the shear capacity of UHPC beams exhibits a declining trend, accompanied by increased mid-span deflection and a degradation in stiffness. French code and PCI report were suggested for design purposes, due to rationally conservative prediction and explicit physical indication. Full article

Tomato brown rugose fruit virus (ToBRFV) has been causing severe agricultural damage worldwide since its recent discovery. While related to tobacco mosaic virus, its properties and infection mechanisms are poorly understood. To uncover their structure and nanomechanics, we carried out atomic force microscopy (AFM) measurements on individual ToBRFV particles. The virions are rod-shaped with a height and width of 9 and 30 nm, respectively. Length is widely distributed (5–1000 nm), with a mode at 30 nm. ToBRFV rods displayed a 22.4 nm axial periodicity related to structural units. Force spectroscopy revealed a Young’s modulus of 8.7 MPa, a spring constant of 0.25 N/m, and a rupture force of 1.7 nN. In the force curves a step was seen at a height of 3.3 nm, which is related to virion wall thickness. Wall thickness was also estimated by predicting coat protein structure with AlphaFold, yielding a protein with a length of 7.3 nm. Accordingly, the structural element of ToBRFv is a right circular cylinder with an equal height and diameter of ~22 nm and a wall thickness between 3.3 and 7.3 nm. Thus, at least four to nine serially linked units are required to encapsidate a single, helically organized RNA genome. Fragmentation of ToBRFV into these cylindrical structural units may result in a facilitated release of the genome and thus efficient infection. Full article

This study proposes a system identification (SI) technique based on the constrained recursive least squares (CRLS) method to model the dynamics of the P-SUROII. By simplifying the dynamic model in consideration of the inherent characteristics of underwater vehicles and minimizing the number of parameters to be estimated, the proposed approach aims to improve estimation accuracy. In addition, a simplified thruster input model was applied to quantify the actual thruster output and improve the reliability of the input data. To satisfy the persistent excitation (PE) condition during the estimation process, experiments incorporating various motion modes were designed, and free-running and S-shaped maneuvering tests were additionally conducted to validate the model’s generalization capability and prediction performance. The coefficients estimated using the CRLS method, which is robust to noise and bias, were evaluated using quantitative similarity metrics such as root mean squared error (RMSE) and mean absolute error (MAE), confirming their validity. The proposed method effectively captures the actual dynamics of the underwater vehicle and is expected to serve as a key enabling technology for the future development of high-performance control systems and autonomous operation systems. Full article

The Multi-Mode Model (MMM) is a physics-based anomalous transport model integrated into TRANSP for predicting electron and ion thermal transport, electron and impurity particle transport, and toroidal and poloidal momentum transport. While MMM provides valuable predictive capabilities, its computational cost, although manageable for standard simulations, is too high for real-time control applications. MMMnet, a neural network-based surrogate model, is developed to address this challenge by significantly reducing computation time while maintaining high accuracy. Trained on TRANSP simulations of DIII-D discharges, MMMnet incorporates an updated version of MMM (9.0.10) with enhanced physics, including isotopic effects, plasma shaping via effective magnetic shear, unified correlation lengths for ion-scale modes, and a new physics-based model for the electromagnetic electron temperature gradient mode. A key advancement is MMMnet’s ability to predict all six transport coefficients, providing a comprehensive representation of plasma transport dynamics. MMMnet achieves a two-order-of-magnitude speed improvement while maintaining strong correlation with MMM diffusivities, making it well-suited for real-time tokamak control and scenario optimization. Full article

Lightweight composite structures like fiber metal laminates (FMLs) are widely used to manufacture aircraft structures and substitute metallic parts. While the superior mechanical performance of FMLs, including their high specific strength and excellent impact and fatigue resistance, has gained the interest of many researchers in the aerospace manufacturing industry, there are still some challenges that need to be considered. Conventional approaches like lay-up techniques and autoclave molding can achieve the relatively simple FML parts with large radii and profiles required for aircraft fuselages and flat skins. However, these methods are not suitable for forming complex-shaped structural parts due to the limited failure strain of fiber-reinforced materials and complex failure modes of the laminates. This research puts forward a new methodology that combines the hydroforming and subsequent curing process to investigate the feasibility of manufacturing complex aircraft parts like fairings made by FMLs. In this research, wrinkle formations are analyzed under various parameters during the hydroforming process. The geometrical shape of the initial blanks and the parameters, including blank holder force and cavity pressure, have been optimized to avoid flange edge wrinkles, and the addition of local support materials contributes to improving local wrinkling in the sharp corners. A finite element model (FEM) taking material laws, interlayer contacts, and boundary conditions into account is used to predict the dynamic hydroforming process of the fiber metal laminate, and experimental works are carried out for its verification. It is expected that the proposed method will reduce both costs and time, as well as reducing laminate defects. Thus, this method offers great potential for future applications related to manufacturing complex-shaped aerospace parts. Full article

Accurately quantifying corrosion damage in reinforced concrete (RC) beams is a significant challenge for structural health monitoring. This study introduces a novel damage identification method that integrates the Sparrow Search Algorithm (SSA)-optimized Extreme Learning Machine (ELM) to address this issue. By utilizing dynamic characteristics, including natural frequencies and mode shapes, as input features, the model predicts three critical damage indicators: the mass corrosion ratio (η_s), flexural capacity reduction factor (α), and flexural stiffness reduction factor (β). Validation through ABAQUS finite element simulations demonstrated the superior performance of the SSA-ELM approach compared to conventional ELM, achieving a 60–70% reduction in mean square error (MSE). Specifically, the MSE for η_s decreased from 2.1062 to 0.3174. The experimental validation conducted on seven RC beams with corrosion levels ranging from 0% to 14.1% confirmed the method’s reliability, with prediction errors for α and β ranging from 5 to 10%. This represents a 50% improvement in accuracy compared to conventional ELM, which exhibited errors in the range of 9–20%. SSA-ELM is a novel and more effective solution to the challenges (e.g., early convergence and convergence speed) faced by existing optimized ELM methods (especially GWO-ELM and GA-ELM). Furthermore, the practical implementation of the proposed framework includes a MATLAB R2024a-based graphical user interface (GUI) with Docker containerization, enabling efficient field deployment for structural assessment. Overall, this study establishes SSA-ELM as a promising tool for post-corrosion safety evaluation of RC structures. Full article

The direct relationship between aerosols and clouds strongly influences the effects of clouds on the global climate. Aerosol particles act as cloud condensation nuclei (CCN) and ice nuclei (IN), affecting cloud formation, microphysics, and precipitation, as well as increasing the reflection of solar radiation at the cloud tops. Processes such as gas-to-particle conversion and new particle formation (NPF) control aerosol properties that, together with meteorological conditions, regulate cloud droplet nucleation through Köhler theory and related effects. The indirect aerosol effects described by Twomey and Albrecht demonstrate how changes in aerosols impact droplet number, cloud lifetime, and precipitation efficiency. Cloud microphysical processes, including droplet growth, collision-coalescence, and solid-phase mechanisms such as riming, vapor diffusion, and aggregation, shape precipitation development in warm, cold, and mixed-phase clouds. Ice nucleation remains a significant uncertainty due to the diversity of aerosol types and nucleation modes. This work synthesizes these physical interactions to better understand how the chemical and physical properties of aerosols influence cloud and precipitation processes, supporting improvements in weather and climate prediction models despite numerical challenges arising from the complexity of aerosol–cloud interactions. Full article

As large language models (LLMs) and vision–language models (VLMs) become increasingly used in robotics area, a crucial question arises: to what extent do these models replicate human-like cognitive processes, particularly within socially interactive contexts? Whilst these models demonstrate impressive multimodal reasoning and perception capabilities, their cognitive plausibility remains underexplored. In this study, we address this gap by using human visual attention as a behavioural proxy for cognition in a naturalistic human-robot interaction (HRI) scenario. Eye-tracking data were previously collected from participants engaging in social human-human interactions, providing frame-level gaze fixations as a human attentional ground truth. We then prompted a state-of-the-art VLM (LLaVA) to generate scene descriptions, which were processed by four LLMs (DeepSeek-R1-Distill-Qwen-7B, Qwen1.5-7B-Chat, LLaMA-3.1-8b-instruct, and Gemma-7b-it) to infer saliency points. Critically, we evaluated each model in both stateless and memory-augmented (short-term memory, STM) modes to assess the influence of temporal context on saliency prediction. Our results presented that whilst stateless LLaVA most closely replicates human gaze patterns, STM confers measurable benefits only for DeepSeek, whose lexical anchoring mirrors human rehearsal mechanisms. Other models exhibited degraded performance with memory due to prompt interference or limited contextual integration. This work introduces a novel, empirically grounded framework for assessing cognitive plausibility in generative models and underscores the role of short-term memory in shaping human-like visual attention in robotic systems. Full article

Fatigue damage in steel–concrete composite structures frequently initiates at welded joints due to stress concentrations and inherent defects. This study investigates the stress intensity factors (SIFs) associated with fatigue cracks in the welds of steel longitudinal beams, employing the FRANC3D–ABAQUS interactive technique. A finite element model was developed and validated against experimental data, followed by the insertion of cracks at both the weld root and weld toe. The influences of stud spacing, initial crack size, crack shape, and lack-of-penetration defects on Mode I SIFs were systematically analyzed. Results show that both weld root and weld toe cracks are predominantly Mode I in nature, with the toe cracks exhibiting higher SIF values. Increasing the stud spacing, crack depth, or crack aspect ratio significantly raises the SIFs. Lack of penetration defects further amplifies the SIFs, especially at the weld root. Based on the computed SIFs, fatigue life predictions were conducted using a crack propagation approach. These findings highlight the critical roles of crack geometry and welding quality in fatigue performance, providing a numerical foundation for optimizing welded joint design in composite structures. Full article

The compressive capacity of helical anchors constitutes a pivotal performance parameter in geotechnical design. To precisely predict the compressive bearing behavior of helical anchors in aeolian sand, this study integrates in situ testing with finite element numerical analysis to systematically elucidate the non-linear evolution of its load-bearing mechanisms. The XGBoost algorithm enabled the rigorous quantification of the governing geometric features of compressive capacity, culminating in a computational framework for the bearing capacity factor (N_q) and lateral earth pressure coefficient (K_u). The research findings demonstrate the following: (1) Compressive capacity exhibits significant enhancement with increasing helix diameter yet displays limited sensitivity to helix number. (2) Load–displacement curves progress through three distinct phases—initial quasi-linear, intermediate non-linear, and terminal quasi-linear stages—under escalating pressure. (3) At embedment depths of H < 5D, tensile capacity diminishes by approximately 80% relative to compressive capacity, manifesting as characteristic shallow anchor failure patterns. (4) When H ≥ 5D, stress redistribution transitions from bowl-shaped to elliptical contours, with ≤10% divergence between uplift/compressive capacities, establishing 5D as the critical threshold defining shallow versus deep anchor behavior. (5) The helix spacing ratio (S/D) governs the failure mode transition, where cylindrical shear (CS) dominates at S/D ≤ 4, while individual bearing (IB) prevails at S/D > 4. (6) XGBoost feature importance analysis confirms internal friction angle, helix diameter, and embedment depth as the three parameters exerting the most pronounced influence on capacity. (7) The proposed computational models for N_q and K_u demonstrate exceptional concordance with numerical simulations (mean deviation = 1.03, variance = 0.012). These outcomes provide both theoretical foundations and practical methodologies for helical anchor engineering in aeolian sand environments. Full article

An aircraft that has been carefully optimized for a single flight condition will tend to perform poorly at other flight conditions. For aircraft such as long-haul airliners, this is not necessarily a problem, since the cruise condition so heavily dominates a typical mission. However, other aircraft, such as Unmanned Aerial Vehicles (UAVs), may be expected to perform well at a wide range of flight conditions. Morphing systems may be a solution to this problem, as they allow the aircraft to adapt its shape to produce optimum performance at each flight condition. This study proposes an aerodynamic optimization framework for morphing airfoils by integrating Principal Component Analysis (PCA) for geometric dimensionality reduction and deep learning (DL) for surrogate modeling, alongside an optimization-guided data augmentation strategy. By employing PCA, the geometric dimensionality of airfoil surfaces is reduced from 24 to 18 design variables while preserving 100% shape fidelity, thus establishing a compressed morphing parameterization space. A Multi-Island Genetic Algorithm (MIGA) efficiently explores the reduced design space, while iterative retraining of the surrogate model enhances prediction accuracy, particularly in high-performance regions. Additionally, Shapley Additive Explanation (SHAP) analysis reveals interpretable correlations between principal component modes and aerodynamic performances. Experimental results show that the optimized airfoil achieves a 54.66% increase in low-speed cruise lift-to-drag ratio and 10.90% higher climb lift compared to the baseline. Overall, the proposed framework not only enhances the adaptability of morphing airfoils across various low-speed flight conditions but also facilitates targeted surrogate refinement and efficient data acquisition in high-performance regions. Full article