Search Results (118)

Crack initiation, propagation, and slippage serve as the key mesoscopic mechanisms contributing to the deterioration of deep tunnel surrounding rocks. In this study, a secondary anisotropy of deep tunnels surrounding rocks was proposed: The axial-displacement constraint of deep tunnels forces cracks in the surrounding rock to initiate, propagate, and slip in planes parallel to the tunnel axial direction. These cracks have no significant effect on the axial strength of the surrounding rock but significantly reduce the tangential strength, resulting in the secondary anisotropy. First, the secondary anisotropy was verified by a hybrid stress–strain controlled true triaxial test of sandstone specimens, a CT 3D (computed tomography three-dimensional) reconstruction of a fractured sandstone specimen, a numerical simulation of heterogeneous rock specimens, and field borehole TV (television) images. Subsequently, a novel SSA (strain-softening and secondary anisotropy) constitutive model was developed to characterise the secondary anisotropy of the surrounding rock and developed using C++ into a numerical form that can be called by FLAC^3D (Fast Lagrangian Analysis of Continua in 3 Dimensions). Finally, effects of secondary anisotropy on a deep tunnel surrounding rock were analysed by comparing the results calculated by the SSA model and a uniform strain-softening model. The results show that considering the secondary anisotropy, the extent of strain-softening of the surrounding rock was mitigated, particularly the axial strain-softening. Moreover, it reduced the surface displacement, plastic zone, and dissipated plastic strain energy of the surrounding rock. The proposed SSA model can precisely characterise the objectively existent secondary anisotropy, enhancing the accuracy of numerical simulations for tunnels, particularly for deep tunnels. Full article

The structural units with different characteristic scales in gradient nanostructured (GS) 316L stainless steel act synergistically to achieve the matching of strength and plasticity, and the intrinsic plasticity of nanoscale and ultrafine grains is fully demonstrated. The macroscopic stress–strain responses of each material unit in the GS surface layer can be measured directly by tension or compression tests on microspecimens. However, the experimental results based on microspecimens do not reflect either the extraordinary strengthening effect caused by non-uniform deformation or the intrinsic plasticity of nanoscale and ultrafine grains. In this paper, a method for constructing depth-dependent constitutive relationships of GS materials was proposed, which combines strain hardening parameter (hardness) with physics-informed neural networks (PINNs). First, the microhardness distribution on the specimen cross-sections was measured after stretching to different strains, and the hardness–strain–force test data were used to construct the depth-dependent PINNs model for the true strain–hardness relationship (PINNs_εH). Hardness–strain–force test data from specimens with uniform coarse grains were used to pre-train the PINNs model for hardness and true stress (PINNs_Hσ), on the basis of which the depth-dependent PINNs_Hσ model for GS materials was constructed by transfer learning. The PINNs_εσ model, which characterizes the depth-dependent constitutive relationships of GS materials, was then constructed using hardness as an intermediate variable. Finally, the accuracy and validation of the PINNs_εσ model were verified by a three-point flexure test and finite element simulation. The modeling method proposed in this study can be used to determine the position-dependent constitutive relationships of heterogeneous materials. Full article

Many dynamic systems experience unwanted actuation caused by an unknown exogenous input. Typically, when these exogenous inputs are stochastically bounded and a basis set cannot be identified, a Kalman-like estimator may suffice for state estimation, provided there is minimal uncertainty regarding the true system dynamics. However, such exogenous inputs can encompass environmental factors that constrain and influence system dynamics and overall performance. These environmental factors can modify the system’s internal interactions and constitutive constants. The proposed control scheme examines the case where the true system’s plant changes due to environmental or health factors while being actuated by stochastic variances. This approach updates the reference model by utilizing the input and output of the true system. Lyapunov stability analysis guarantees that both internal and external error states will converge to a neighborhood around zero asymptotically, provided the assumptions and constraints of the proof are satisfied. Full article

The relationship between income and subjective well-being (SWB) has been widely studied. While previous research has shown that the correlation between income and SWB is not always strong, there is limited research examining how the choice of SWB index influences this relationship. Drawing on survey data collected from 32 countries between 2015 and 2017, this study explores how the income–SWB relationship varies across different SWB indices. The dataset encompasses both developed and developing nations. We analyzed six types of SWB indices documented in the literature—covering a broader range than is typically included—and conducted comparative analyses. To account for the possibility of a nonlinear relationship between income and these SWB measures, we used a semiparametric approach by applying generalized additive models. Our findings show that these six indices can be categorized into three groups: (1) mental health and affect balance, (2) subjective happiness and eudaimonia, and (3) life satisfaction and the Cantril Ladder. These results underscore the significant impact that the selected SWB index can have on the income–SWB relationship. While economic development is often assumed to enhance SWB, our analysis reveals that this relationship does not hold consistently across all SWB indicators. In particular, certain indicators show little or no improvement in well-being despite increasing income levels, suggesting the presence of excessive or inefficient consumption that fails to contribute to genuine human flourishing. These findings challenge the conventional growth-centric paradigm and call for a deeper societal and academic inquiry into what constitutes “true prosperity.” From a sustainability perspective, aligning economic progress with authentic improvements in well-being is essential. This requires not only more careful selection and interpretation of SWB metrics, but also a broader re-evaluation of consumption patterns and policy goals to ensure that future development contributes meaningfully to human and ecological well-being. Full article

The high water head of some pumped storage power stations will induce the cracking of the concrete lining of their diversion tunnel and the leakage of high-pressure water, which will affect the safety of the tunnel and the surrounding rock. At present, there is no solution to the problem of impermeability of concrete materials after cracking. This paper proposes a composite lining to solve this problem. The composite lining with modified polydimethylsiloxane coating can effectively prevent high-pressure water, but its crack resistance needs to be further studied. Therefore, the tensile mechanical properties, constitutive relationship of modified polydimethylsiloxane impermeable coating, and the crack resistance mechanical properties of modified polydimethylsiloxane impermeable composite lining were studied by laboratory tests and numerical simulations. The results show that the true fracture elongation of the modified polydimethylsiloxane impermeable coating is as high as 118.98%, and its mechanical behavior can be described by a simplified polynomial hyperelastic constitutive model. The in situ stress will affect the crack width of the concrete lining. When the lateral pressure coefficient is less than 1, the crack width decreases with the increase in the lateral pressure coefficient. When the lateral pressure coefficient is greater than 1, the crack width increases with the increase in the lateral pressure coefficient. To prevent the cracking of modified polydimethylsiloxane coating, its spraying thickness needs to increase with the increase in crack width. The ratio of the coating’s thickness to crack width is recommended from 0.162 to 1.930 for internal water pressure from 1 MPa to 10 MPa, respectively. The suggestion provides a reference for designing the impermeable composite lining structure subjected to high internal water pressure. Full article

The degree of tree curvature exerts a significant influence on the utilization of forestry resources. This study proposes an enhanced quantitative structural modeling (QSM) method, founded upon terrestrial laser scanning (TLS) point cloud data, for the precise extraction of 3D curvature characteristics of tree trunks. The conventional approach operates under the assumption that the tree trunk constitutes an upright rotating body, thereby disregarding the tree trunk’s true curvature morphology. The proposed method is founded on the classical QSM algorithm and introduces two zoom factors that can dynamically adjust the fitting parameters. This improvement leads to enhanced accuracy in the representation of tree trunk curvature and reduced computational complexity. The study utilized 146 sample trees from 13 plots in Jixi, Anhui Province, which were collected and pre-processed by TLS. The study combines point cloud segmentation, manual labeling of actual curvature and dual-factor experiments, and uses quadratic polynomials and simulated annealing algorithms to determine the optimal model factors. The validation results demonstrate that the enhanced method exhibits a greater degree of concordance between the predicted and actual curvature values within the validation set. In the regression equation, the coefficient of the two-factor method for fitting a straight line is 0.95, which is substantially higher than the 0.75 of the one-factor method. Furthermore, the two-factor model has an R² of 0.21, indicating that the two-factor optimization method generates a significantly smaller error compared to the one-factor model (with an R² of 0.12). In addition, this study discusses the possible reasons for the error in the results, as well as the shortcomings and outlook. The experimental results demonstrate the augmented method’s capacity to accurately reconstruct the 3D curvature of tree trunks in most cases. This study provides an efficient and accurate method for conducting fine-grained forest resource measurements and tree bending studies. Full article

This study presents an innovative data-driven methodology to model the hardening behavior of sheet metals across a broad strain range, crucial for understanding sheet metal mechanics. Conventionally, true stress–strain data from such tests are used to analyze plastic flow within the pre-necking regime, often requiring additional experiments to inverse finite element methods, which demand extensive field data for improved accuracy. Although digital image correlation offers precise data, its implementation is costly. To address this, we integrate experimental data from standard tensile tests with a machine-learning approach to estimate the flow curve. Subsequently, we conduct finite element simulations on uniaxial tensile tests, using materials characterized by the Swift constitutive equation to build a comprehensive database. Loading force-gripper displacement curves from these simulations are then transformed into input features for model training. We propose and compare three models—Models A, B, and C—each employing different input feature selections to estimate the flow curve. Experimental validation including uniaxial tensile, plane strain, and simple shear tests on the DP590 and DP780 sheets are then carefully considered. Results demonstrate the effectiveness of our proposed method, with Model C showing the highest efficacy. Full article

Few-shot relation extraction constitutes a critical task in natural language processing. Its aim is to train a model using a limited number of labeled samples when labeled data are scarce, thereby enabling the model to rapidly learn and accurately identify relationships between entities within textual data. Prototypical networks are extensively utilized for simplicity and efficiency in few-shot relation extraction scenarios. Nevertheless, the prototypical networks derive their prototypes by averaging the feature instances within a given category. In cases where the instance size is limited, the prototype may not represent the true category centroid adequately, consequently diminishing the accuracy of classification. In this paper, we propose an innovative approach for few-shot relation extraction, leveraging instances from the query set to enhance the construction of prototypical networks based on the support set. Then, the weights are dynamically assigned by quantifying the semantic similarity between sentences. It can strengthen the emphasis on critical samples while preventing potential bias in class prototypes, which are computed using the mean value within prototype networks under small-size scenarios. Furthermore, an adaptive fusion module is introduced to integrate prototype and relational information more deeply, resulting in more accurate prototype representations. Extensive experiments have been performed on the widely used FewRel benchmark dataset. The experimental findings demonstrate that our AIRE model surpasses the existing baseline models, especially the accuracy, which can reach 91.53% and 86.36% on the 5-way 1-shot and 10-way 1-shot tasks, respectively. Full article

Cemented tailings backfill (CTB) plays an important role in mine filling operations. In order to study the long-term stability of CTB under the dynamic disturbance of deep wells, ultrafine cemented tailings backfill was taken as the research object, and the true triaxial hydraulic fracturing antireflection-wetting dynamic experimental system of coal and rock was used to carry out a static true triaxial compression test, a true triaxial compression test under unidirectional disturbance, and a true triaxial compression test under bidirectional disturbance. At the same time, the acoustic emission monitoring and positioning tests of the CTB were carried out during the compression test. The evolution law of the mechanical parameters and deformation and failure characteristics of CTB under different confining pressures is analyzed, and the damage constitutive model of the filling body is established using stochastic statistical theory. The results show that the compressive strength of CTB increases with an increase in intermediate principal stress. According to the change process of the acoustic emission ringing count over time, the triaxial compression test can be divided into four stages: the initial active stage, initial calm stage, pre-peak active stage, and post-peak calm stage. When the intermediate principal stress is small, the specimen is dominated by shear failure. With an increase in the intermediate principal stress, the specimen changes from brittle failure to plastic failure. The deformation and failure strength of CTB are closely related to its loading and unloading methods. Under a certain stress intensity, compared with unidirectional unloading, bidirectional unloading produces a greater deformation of the rock mass, and the failure strength of the rock mass is higher. This study only considers the confining pressure within the compressive limit of the specimen. Future research can be directed at a wider range of stresses to improve the applicability and reliability of the research results. Full article

The beam structure constitutes a vital element in construction and bridge engineering. Static damage detection technology provides a method for identifying potential damage by measuring static displacements, with the advantage of being easy to implement. In this work, a two-stage damage detection method is proposed to determine the location and severity of damage in beam structures. The first stage identifies the damage location based on the displacement difference curves of the beam structure under static loading before and after the damage occurs. The second stage employs an artificial neural network to determine the severity of the damage. The proposed two-stage damage detection method has been validated in both a numerical model and an experimental model of beam structures. The following conclusions can be drawn from both numerical simulations and experimental studies. Regardless of the loading position, the turning points in the displacement difference curves always occur in the damaged regions, indicating that the damage locations in the beam structure can be determined by the turning points of the displacement difference curves. A single inflection point in the displacement difference curve indicates the presence of a single damage, while multiple inflection points indicate the existence of multiple damaged elements, with each inflection point corresponding to a damaged location. Furthermore, the severity of the damage can be accurately calculated using an artificial neural network. For experimental example 1, the damage locations identified by the proposed method all fall within the actual damage area, and the average error between the obtained damage severity and the true value is approximately 3.8%. For experimental example 2, the distance error between the damage location identified by the method and the actual damage location is approximately 1.4%, and the error between the obtained damage severity and the true value is approximately 2.8%. This two-stage damage detection method is more convenient to implement than traditional detection methods because it can precisely identify damage in beam structures using only a small amount of displacement data, providing a simple and highly practical solution for detecting defects in beam structures. Full article

Sensor networks, which are increasingly being used in a broad range of applications, constitute a measurement paradigm involving ensembles of sensors measuring possibly different quantities at a discrete sample of spatial locations and temporal points outside the laboratory. If sensor networks are to be considered as true metrology systems and the measurement results derived from them used for decision-making, such as in a regulatory context, it is important that the results are accompanied by reliable statements of measurement uncertainty. This paper gives a preview of some of the work undertaken within the European-funded ‘Fundamental principles of sensor network metrology (FunSNM)’ project to address the challenges of measurement uncertainty evaluation in some real-world sensor network applications. The applications demonstrate that sensor networks possess features related to the nature of the measured quantities, to the nature of the measurement model, and to the nature of the measured data. These features make conventional methods of measurement uncertainty evaluation, and established guidelines for measurement uncertainty evaluation difficult to apply. An overview of some of the modelling tools used to address the challenges of measurement uncertainty evaluation in those applications is given. Full article

In its duration of service, Bailey truss is commonly exposed to corrosion threats due to the failure of anti-corrosion coatings, resulting in corrosion damage to its steel members and degradation of its structural performance. There is a lack of research on the degradation of the material properties of Bailey truss due to natural corrosion. This paper investigated the degradation of a Bailey truss that had been in service for eight years, in northeast China. Tensile tests were carried out on corroded specimens from three truss members (the chords, diagonals, and verticals), to establish regression equations for the minimum residual cross-sectional rate and several material properties of four parts (chord flange, chord web, diagonal web, and vertical web). The equations were compared with the degradation of steel properties with different yield strengths, cross-sectional shapes, and corrosion types. Fitting formulas for the true constitutive models of the four parts of non-corroded and corroded specimens were developed. It was found that the diagonal truss member was the most severely affected by corrosion, while the chord web was the least impacted. The degradation trend of 80% in regard to natural corrosion specimens is lower than that of the Bailey truss diagonal web and the degradation trend in terms of the yield strength and ultimate strength in the same part is less than 5%. According to the Ramberg–Osgood model, the multi-curve constitutive model fitting formulas are suitable for four parts of naturally corroded Bailey truss. Full article

Sound planning for urban mobility is a key facet of securing a sustainable future for our urban systems, and requires the careful and comprehensive assessment of its components, such as the status of the cities’ public transportation network, and how urban planners should invest in developing it. We use agent-based modelling, a tried and true method for such endeavours, for studying the history, planned future works and possible evolution of the tram line network in the Greater Geneva region. We couple these models with knowledge graphs, in a way that both are able to mutually enrich each other. Results show that the information organisation powers of knowledge graphs are highly relevant for effortlessly recounting past events and designing scenarios to be directly incorporated inside the agent-based model. The model features all 5 tram lines from the current real-world network, servicing a total of 15 communes. In turn, the model is capable of replaying past events, predicting future developments and exploring user-defined scenarios. It also harnesses its self-organisation properties to autonomously reconstruct an artificial public transportation network for the region based on two different initial networks, servicing up to 29 communes depending on the scenario. The data gathered from the simulation is effortlessly imported back into the initial knowledge graphs. The artificial networks closely resemble their real-world counterparts and demonstrate the predictive and prescriptive powers of our agent-based model. They constitute valuable assets towards a comprehensive assessment of urban mobility systems, compelling progress for the agent-based modelling field, and a convincing demonstration of its technical capabilities. Full article

Wildfires pose a growing threat to Mediterranean ecosystems. This study employs advanced classification techniques for shrub fractional cover mapping from satellite imagery in a fire-prone landscape in Quinta da França (QF), Portugal. The study area is characterized by fine-grained heterogeneous land cover and a Mediterranean climate. In this type of landscape, shrub encroachment after land abandonment and wildfires constitutes a threat to ecosystem resilience—in particular, by increasing the susceptibility to more frequent and large fires. High-resolution mapping of shrub cover is, therefore, an important contribution to landscape management for fire prevention. Here, a 20 cm resolution land cover map was used to label 10 m Sentinel-2 pixels according to their shrub cover percentage (three categories: 0%, >0%–50%, and >50%) for training and testing. Three distinct algorithms, namely Support Vector Machine (SVM), Artificial Neural Networks (ANNs), and Random Forest (RF), were tested for this purpose. RF excelled, achieving the highest precision (82%–88%), recall (77%–92%), and F1 score (83%–88%) across all categories (test and validation sets) compared to SVM and ANN, demonstrating its superior ability to accurately predict shrub fractional cover. Analysis of confusion matrices revealed RF’s superior ability to accurately predict shrub fractional cover (higher true positives) with fewer misclassifications (lower false positives and false negatives). McNemar’s test indicated statistically significant differences (p value < 0.05) between all models, consolidating RF’s dominance. The development of shrub fractional cover maps and derived map products is anticipated to leverage key information to support landscape management, such as for the assessment of fire hazard and the more effective planning of preventive actions. Full article

Dipole localization, a fundamental challenge in electromagnetic source imaging, inherently constitutes an optimization problem aimed at solving the inverse problem of electric current source estimation within the human brain. The accuracy of dipole localization algorithms is contingent upon the complexity of the forward model, often referred to as the head model, and the signal-to-noise ratio (SNR) of measurements. In scenarios characterized by low SNR, often corresponding to deep-seated sources, existing optimization techniques struggle to converge to global minima, thereby leading to the localization of dipoles at erroneous positions, far from their true locations. This study presents a novel hybrid algorithm that combines simulated annealing with the traditional quasi-Newton optimization method, tailored to address the inherent limitations of dipole localization under low-SNR conditions. Using a realistic head model for both electroencephalography (EEG) and magnetoencephalography (MEG), it is demonstrated that this novel hybrid algorithm enables significant improvements of up to 45% in dipole localization accuracy compared to the often-used dipole scanning and gradient descent techniques. Localization improvements are not only found for single dipoles but also in two-dipole-source scenarios, where sources are proximal to each other. The novel methodology presented in this work could be useful in various applications of clinical neuroimaging, particularly in cases where recordings are noisy or sources are located deep within the brain. Full article