This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge compon...This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge components are prone to cracks and other defects,severely compromising structural safety and service life.Traditional inspection methods relying on manual visual assessment or vehicle-mounted sensors suffer from low efficiency,strong subjectivity,and high costs,while conventional image processing techniques and early deep learning models(e.g.,UNet,Faster R-CNN)still performinadequately in complex environments(e.g.,varying illumination,noise,false cracks)due to poor perception of fine cracks andmulti-scale features,limiting practical application.To address these challenges,this paper proposes CACNN-Net(CBAM-Augmented CNN),a novel dual-encoder architecture that innovatively couples a CNN for local detail extraction with a CBAM-Transformer for global context modeling.A key contribution is the dedicated Feature FusionModule(FFM),which strategically integratesmulti-scale features and focuses attention on crack regions while suppressing irrelevant noise.Experiments on bridge crack datasets demonstrate that CACNNNet achieves a precision of 77.6%,a recall of 79.4%,and an mIoU of 62.7%.These results significantly outperform several typical models(e.g.,UNet-ResNet34,Deeplabv3),confirming their superior accuracy and robust generalization,providing a high-precision automated solution for bridge crack detection and a novel network design paradigm for structural surface defect identification in complex scenarios,while future research may integrate physical features like depth information to advance intelligent infrastructure maintenance and digital twin management.展开更多
Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce different...Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.展开更多
With the rapid growth of technologies requiring high-power energy storage,achieving long-term cyclic stability under ultra-high current density is a key challenge.Aqueous zinc-ion batteries(AZIBs)are promising candida...With the rapid growth of technologies requiring high-power energy storage,achieving long-term cyclic stability under ultra-high current density is a key challenge.Aqueous zinc-ion batteries(AZIBs)are promising candidates due to their intrinsic safety and low cost,but they suffer from severe interfacial instability at rates exceeding 10 mA cm^(-2),which drastically shortens their cycle life.Inspired by theoretical calculations,triglyme(TGDE)additive with strong electron-donating groups into Zn(OTf)_(2) electrolytes effectively disrupts the hydrogen-bond network among free water molecules,while the weak coordination of TGDE with Zn^(2+)promotes the entry of OTf-into the primary Zn^(2+)solvated sheath,thus decreasing the coordination number of water with Zn^(2+).As such,the hydrogen-bond network and the bulk solvated structure are reconstructed with better stability.Moreover,the strong adsorption of TGDE lying on the Zn(002)surface would induce Zn depositions along(002)together with the reduced exposed surface,further effectively inhibiting side reactions.Likewise,TGDE electrolyte induces the formation of such ZnF_(2)-ZnS dual-layer solid electrolyte interface(SEI)with superior chemical stability and ionic conductivity,thereby regulating Zn^(2+)flux with dendrite-free depositions.Based on this electrolyte,Zn‖Zn cells can be stably cycled for 1300 h at a limit of 10 mA cm^(-2) and 10 mAh cm^(-2).The assembled Zn‖V_(2)O_(5) full cells still maintain 99.9%capacity retention after 1000 cycles at 10 A g^(-1).This work provides a feasible approach for designing aqueous electrolytes to reconstruct the hydrogen-bond network and solvated structure,which can be extended to the applications of high-rate and high-temperature scenarios.展开更多
Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to ...Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to the unique functional attributes and interaction patterns inherent to different layers.This paper addresses the critical question of whether structural information from a known layer can be used to reconstruct the unknown intralayer structure of a target layer within general weighted output-coupling multilayer networks.Building upon the generalized synchronization principle,we propose an innovative reconstruction method that incorporates two essential components in the design of structure observers,the cross-layer coupling modulator and the structural divergence term.A key advantage of the proposed reconstruction method lies in its flexibility to freely designate both the unknown target layer and the known reference layer from the general weighted output-coupling multilayer network.The reduced dependency on full-state observability enables more deployment in engineering applications with partial measurements.Numerical simulations are conducted to validate the effectiveness of the proposed structure reconstruction method.展开更多
An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximatio...An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.展开更多
Software systems play increasing important roles in modern society,and the ability against attacks is of great practical importance to crucial software systems,resulting in that the structure and robustness of softwar...Software systems play increasing important roles in modern society,and the ability against attacks is of great practical importance to crucial software systems,resulting in that the structure and robustness of software systems have attracted a tremendous amount of interest in recent years.In this paper,based on the source code of Tar and MySQL,we propose an approach to generate coupled software networks and construct three kinds of directed software networks:The function call network,the weakly coupled network and the strongly coupled network.The structural properties of these complex networks are extensively investigated.It is found that the average influence and the average dependence for all functions are the same.Moreover,eight attacking strategies and two robustness indicators(the weakly connected indicator and the strongly connected indicator)are introduced to analyze the robustness of software networks.This shows that the strongly coupled network is just a weakly connected network rather than a strongly connected one.For MySQL,high in-degree strategy outperforms other attacking strategies when the weakly connected indicator is used.On the other hand,high out-degree strategy is a good choice when the strongly connected indicator is adopted.This work will highlight a better understanding of the structure and robustness of software networks.展开更多
Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration response...Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration responses with complex features,resulting in a lower reconstruction accuracy.This paper addresses this challenge by leveraging the advanced feature extraction and learning capabilities of fully convolutional networks(FCN)to achieve precise reconstruction of acceleration responses.In the designed network architecture,the incorporation of skip connections preserves low-level details of the network,greatly facilitating the flow of information and improving training efficiency and accuracy.Dropout techniques are employed to reduce computational load and enhance feature extraction.The proposed FCN model automatically extracts high-level features from the input data and establishes a nonlinearmapping relationship between the input and output responses.Finally,the accuracy of the FCN for structural response reconstructionwas evaluated using acceleration data from an experimental arch rib and comparedwith several traditional methods.Additionally,this approach was applied to reconstruct actual acceleration responses measured by an SHM system on a long-span bridge.Through parameter analysis,the feasibility and accuracy of aspects such as available response positions,the number of available channels,and multi-channel response reconstruction were explored.The results indicate that this method exhibits high-precision response reconstruction capability in both time and frequency domains.,with performance surpassing that of other networks,confirming its effectiveness in reconstructing responses under various sensor data loss scenarios.展开更多
Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approac...Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.展开更多
BACKGROUND Depression,non-suicidal self-injury(NSSI),and suicide attempts(SA)often co-occur during adolescence and are associated with long-term adverse health outcomes.Unfortunately,neural mechanisms underlying self-...BACKGROUND Depression,non-suicidal self-injury(NSSI),and suicide attempts(SA)often co-occur during adolescence and are associated with long-term adverse health outcomes.Unfortunately,neural mechanisms underlying self-injury and SA are poorly understood in depressed adolescents but likely relate to the structural abnormalities in brain regions.AIM To investigate structural network communication within large-scale brain networks in adolescents with depression.METHODS We constructed five distinct network communication models to evaluate structural network efficiency at the whole-brain level in adolescents with depression.Diffusion magnetic resonance imaging data were acquired from 32 healthy controls and 85 depressed adolescents,including 17 depressed adolescents without SA or NSSI(major depressive disorder group),27 depressed adolescents with NSSI but no SA(NSSI group),and 41 depressed adolescents with SA and NSSI(NSSI+SA group).RESULTS Significant differences in structural network communication were observed across the four groups,involving spatially widespread brain regions,particularly encompassing cortico-cortical connections(e.g.,dorsal posterior cingulate gyrus and the right ventral posterior cingulate gyrus;connections based on precentral gyrus)and cortico-subcortical circuits(e.g.,the nucleus accumbens-frontal circuit).In addition,we examined whether compromised communication efficiency was linked to clinical symptoms in the depressed adolescents.We observed significant correlations between network communication efficiencies and clinical scale scores derived from depressed adolescents with NSSI and SA.CONCLUSION This study provides evidence of structural network communication differences in depressed adolescents with NSSI and SA,highlighting impaired neuroanatomical communication efficiency as a potential contributor to their symptoms.These findings offer new insights into the pathophysiological mechanisms underlying the comorbidity of NSSI and SA in adolescent depression.展开更多
This paper presents the variational physics-informed neural network(VPINN)as an effective tool for static structural analyses.One key innovation includes the construction of the neural network solution as an admissibl...This paper presents the variational physics-informed neural network(VPINN)as an effective tool for static structural analyses.One key innovation includes the construction of the neural network solution as an admissible function of the boundary-value problem(BVP),which satisfies all geometrical boundary conditions.We then prove that the admissible neural network solution also satisfies natural boundary conditions,and therefore all boundary conditions,when the stationarity condition of the variational principle is met.Numerical examples are presented to show the advantages and effectiveness of the VPINN in comparison with the physics-informed neural network(PINN).Another contribution of the work is the introduction of Gaussian approximation of the Dirac delta function,which significantly enhances the ability of neural networks to handle singularities,as demonstrated by the examples with concentrated support conditions and loadings.It is hoped that these structural examples are so convincing that engineers would adopt the VPINN method in their structural design practice.展开更多
Solid polymer electrolytes(SPEs)have attracted much attention for their safety,ease of packaging,costeffectiveness,excellent flexibility and stability.Poly-dioxolane(PDOL)is one of the most promising matrix materials ...Solid polymer electrolytes(SPEs)have attracted much attention for their safety,ease of packaging,costeffectiveness,excellent flexibility and stability.Poly-dioxolane(PDOL)is one of the most promising matrix materials of SPEs due to its remarkable compatibility with lithium metal anodes(LMAs)and suitability for in-situ polymerization.However,poor thermal stability,insufficient ionic conductivity and narrow electrochemical stability window(ESW)hinder its further application in lithium metal batteries(LMBs).To ameliorate these problems,we have successfully synthesized a polymerized-ionic-liquid(PIL)monomer named DIMTFSI by modifying DOL with imidazolium cation coupled with TFSI^(-)anion,which simultaneously inherits the lipophilicity of DOL,high ionic conductivity of imidazole,and excellent stability of PILs.Then the tridentate crosslinker trimethylolpropane tris[3-(2-methyl-1-aziridine)propionate](TTMAP)was introduced to regulate the excessive Li^(+)-O coordination and prepare a flame-retardant SPE(DT-SPE)with prominent thermal stability,wide ESW,high ionic conductivity and abundant Lit transference numbers(t_(Li+)).As a result,the LiFePO_(4)|DT-SPE|Li cell exhibits a high initial discharge specific capacity of 149.60 mAh g^(-1)at 0.2C and 30℃with a capacity retention rate of 98.68%after 500 cycles.This work provides new insights into the structural design of PIL-based electrolytes for long-cycling LMBs with high safety and stability.展开更多
Neural-Network Response Surfaces （NNRS） is applied to replace the actual expensive finite element analysis during the composite structural optimization process. The Orthotropic Experiment Method （OEM） is used to s...Neural-Network Response Surfaces （NNRS） is applied to replace the actual expensive finite element analysis during the composite structural optimization process. The Orthotropic Experiment Method （OEM） is used to select the most appropriate design samples for network training. The trained response surfaces can either be objective function or constraint conditions. Together with other conven- tional constraints, an optimization model is then set up and can be solved by Genetic Algorithm （GA）. This allows the separation between design analysis modeling and optimization searching. Through an example of a hat-stiffened composite plate design, the weight response surface is constructed to be objective function, and strength and buckling response surfaces as constraints; and all of them are trained through NASTRAN finite element analysis. The results of optimization study illustrate that the cycles of structural analysis ean be remarkably reduced or even eliminated during the optimization, thus greatly raising the efficiency of optimization process. It also observed that NNRS approximation can achieve equal or even better accuracy than conventional functional response surfaces.展开更多
The active Lamb wave and piezoelectric transducer(PZT)-based structural health monitoring(SHM)technology is a kind of efficient approach to estimate the health state of aircraft structure.In practical applications,PZT...The active Lamb wave and piezoelectric transducer(PZT)-based structural health monitoring(SHM)technology is a kind of efficient approach to estimate the health state of aircraft structure.In practical applications,PZT networks are needed to monitor large scale structures.Scanning many of the different PZT actuator-sensor channels within these PZT networks to achieve on-line SHM task is important.Based on a peripheral component interconnect extensions for instrumentation(PXI)platform,an active Lamb wave and PZT network-based integrated multi-channel scanning system(PXI-ISS)is developed for the purpose of practical applications of SHM,which is compact and portable,and can scan large numbers of actuator-sensor channels and perform damage assessing automatically.A PXI-based 4 channels gain-programmable charge amplifier,an external scanning module with 276 actuator-sensor channels and integrated SHM software are proposed and discussed in detail.The experimental research on a carbon fiber composite wing box of an unmanned aerial vehicle(UAV)for verifying the functions of the PXI-ISS is mainly discussed,including the design of PZTs layer,the method of excitation frequency selection,functional test of damage imaging,stability test of the PXI-ISS,and the loading effect on signals.The experimental results have verified the stability and damage functions of this system.展开更多
A method of system structural analysis based on decision making trial and evaluation laboratory together with interpretative structural modeling(DEMATEL-ISM) and entropy is proposed to clarify system structure of comm...A method of system structural analysis based on decision making trial and evaluation laboratory together with interpretative structural modeling(DEMATEL-ISM) and entropy is proposed to clarify system structure of communication networks and analyze mutual influencing degree between different networks.Mutual influencing degree and importance degree of elements are both considered to determine weights of elements,and the entropy of expert judgment results is used to omit unimportant influence relation and simplify system structure.Structural analysis on communication networks system shows that the proposed method can quantificationally present weights and mutual influencing degree of elements,and reasonably simplify system structure.The results indicate the rationality and feasibility of the method.展开更多
The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and co...The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.展开更多
The purpose of this paper is to identify the critical road sections and intersections in a road network which have great influence on the normal transport functions of the road network and to optimize the road network...The purpose of this paper is to identify the critical road sections and intersections in a road network which have great influence on the normal transport functions of the road network and to optimize the road network structure by reducing its vulnerability. In this paper, the framework of road network structural vulnerability measurement and improvement model is proposed. The network efficiency model is used to define road network structural vulnerability. Shanghai freeway network is analyzed based on this model. We find that using this model the critical components of the road network can be identified. Two methods which are increasing connections and rewiring are proposed to optimize the road network structural vulnerability and the results can be used to reduce the network vulnerability. The measurement method that we put forward for structure vulnerability is useful and important to optimize road network structure.展开更多
Cholesteric liquid crystals(CLCs)exhibit unique helical superstructures that selectively reflect circularly polarized light,enabling them to dynamically respond to environmental changes with tunable structural colors....Cholesteric liquid crystals(CLCs)exhibit unique helical superstructures that selectively reflect circularly polarized light,enabling them to dynamically respond to environmental changes with tunable structural colors.This dynamic color-changing capability is crucial for applications that require adaptable optical properties,positioning CLCs as key materials in advanced photonic technologies.This review focuses on the mechanisms of dynamic color tuning in CLCs across various forms,including small molecules,cholesteric liquid crystal elastomers(CLCEs),and cholesteric liquid crystal networks(CLCNs),and emphasizes the distinct responsive coloration each structure provides.Key developments in photochromic mechanisms based on azobenzene,dithienylethene,and molecular motor switches,are discussed for their roles in enhancing the stability and tuning range of CLCs.We examine the color-changing behaviors of CLCEs under mechanical stimuli and CLCNs under swelling,highlighting the advantages of each form.Following this,applications of dynamic color-tuning CLCs in information encryption,adaptive camouflage,and smart sensing technologies are explored.The review concludes with an outlook on current challenges and future directions in CLC research,particularly in biomimetic systems and dynamic photonic devices,aiming to broaden their functional applications and impact.展开更多
We propose molten polymer's entanglement network deformation to be nonaffine and use transient network structural theory with the revised Liu's kinetics rate equation and the revised upper convected Maxwell co...We propose molten polymer's entanglement network deformation to be nonaffine and use transient network structural theory with the revised Liu's kinetics rate equation and the revised upper convected Maxwell constitutive equation to establish a nonaffine network structural constitutive model for studying the rheological behavior of molten Low Density Polyethylene (LDPE) and High Density Polyethylene (HDPE) in oscillatory shear. As a result, when the strain amplitude or frequency increases, the shear stress amplitude increases. At the same time, the accuracy of the nonaffine network model is higher than that of affine network model. It is clear that there is a small amount of nonaffine network deformation for LDPE melts which have long chain branches, and there is a larger amount of nonaffine network deformation in oscillatory shear for HDPE melts which has no long chain branches. So we had better consider the network deformation nonaffine when we establish the constitutive equations of polymer melts in oscillatory shear.展开更多
We propose the deep Lagrange method(DLM),which is a new optimization method,in this study.It is based on a deep neural network to solve optimization problems.The method takes the advantage of deep learning artificial ...We propose the deep Lagrange method(DLM),which is a new optimization method,in this study.It is based on a deep neural network to solve optimization problems.The method takes the advantage of deep learning artificial neural networks to find the optimal values of the optimization function instead of solving optimization problems by calculating sensitivity analysis.The DLM method is non-linear and could potentially deal with nonlinear optimization problems.Several test cases on sizing optimization and shape optimization are performed,and their results are then compared with analytical and numerical solutions.展开更多
This paper addresses the problem of the input design of large-scale complex networks.Two types of network components,redundant inaccessible strongly connected component(RISCC)and intermittent inaccessible strongly con...This paper addresses the problem of the input design of large-scale complex networks.Two types of network components,redundant inaccessible strongly connected component(RISCC)and intermittent inaccessible strongly connected component(IISCC)are defined,and a subnetwork called a driver network is developed.Based on these,an efficient method is proposed to find the minimum number of controlled nodes to achieve structural complete controllability of a network,in the case that each input can act on multiple state nodes.The range of the number of input nodes to achieve minimal control,and the configuration method(the connection between the input nodes and the controlled nodes)are presented.All possible input solutions can be obtained by this method.Moreover,we give an example and some experiments on real-world networks to illustrate the effectiveness of the method.展开更多
基金supported by the National Natural Science Foundation of China(No.52308332)the General Scientific Research Project of the Education Department of Zhejiang Province(No.Y202455824).
文摘This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge components are prone to cracks and other defects,severely compromising structural safety and service life.Traditional inspection methods relying on manual visual assessment or vehicle-mounted sensors suffer from low efficiency,strong subjectivity,and high costs,while conventional image processing techniques and early deep learning models(e.g.,UNet,Faster R-CNN)still performinadequately in complex environments(e.g.,varying illumination,noise,false cracks)due to poor perception of fine cracks andmulti-scale features,limiting practical application.To address these challenges,this paper proposes CACNN-Net(CBAM-Augmented CNN),a novel dual-encoder architecture that innovatively couples a CNN for local detail extraction with a CBAM-Transformer for global context modeling.A key contribution is the dedicated Feature FusionModule(FFM),which strategically integratesmulti-scale features and focuses attention on crack regions while suppressing irrelevant noise.Experiments on bridge crack datasets demonstrate that CACNNNet achieves a precision of 77.6%,a recall of 79.4%,and an mIoU of 62.7%.These results significantly outperform several typical models(e.g.,UNet-ResNet34,Deeplabv3),confirming their superior accuracy and robust generalization,providing a high-precision automated solution for bridge crack detection and a novel network design paradigm for structural surface defect identification in complex scenarios,while future research may integrate physical features like depth information to advance intelligent infrastructure maintenance and digital twin management.
基金funded by National Research Council of Thailand(contract No.N42A671047).
文摘Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.
基金the financial support provided by the National Natural Science Foundation of China(grant no.22373032)the open research fund of Songshan Lake Materials Laboratory(grant no.2023SLABFK06)。
文摘With the rapid growth of technologies requiring high-power energy storage,achieving long-term cyclic stability under ultra-high current density is a key challenge.Aqueous zinc-ion batteries(AZIBs)are promising candidates due to their intrinsic safety and low cost,but they suffer from severe interfacial instability at rates exceeding 10 mA cm^(-2),which drastically shortens their cycle life.Inspired by theoretical calculations,triglyme(TGDE)additive with strong electron-donating groups into Zn(OTf)_(2) electrolytes effectively disrupts the hydrogen-bond network among free water molecules,while the weak coordination of TGDE with Zn^(2+)promotes the entry of OTf-into the primary Zn^(2+)solvated sheath,thus decreasing the coordination number of water with Zn^(2+).As such,the hydrogen-bond network and the bulk solvated structure are reconstructed with better stability.Moreover,the strong adsorption of TGDE lying on the Zn(002)surface would induce Zn depositions along(002)together with the reduced exposed surface,further effectively inhibiting side reactions.Likewise,TGDE electrolyte induces the formation of such ZnF_(2)-ZnS dual-layer solid electrolyte interface(SEI)with superior chemical stability and ionic conductivity,thereby regulating Zn^(2+)flux with dendrite-free depositions.Based on this electrolyte,Zn‖Zn cells can be stably cycled for 1300 h at a limit of 10 mA cm^(-2) and 10 mAh cm^(-2).The assembled Zn‖V_(2)O_(5) full cells still maintain 99.9%capacity retention after 1000 cycles at 10 A g^(-1).This work provides a feasible approach for designing aqueous electrolytes to reconstruct the hydrogen-bond network and solvated structure,which can be extended to the applications of high-rate and high-temperature scenarios.
基金Project supported by the National Natural Science Foun-dation of China(Grant No.62373197)the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province,China(Grant No.23KJB120010)+1 种基金the Industry-University-Research Cooperation Project of Jiangsu Province,China(Grant No.BY20251038)the Cultivation and In-cubation Project of the College of Automation,Nanjing Uni-versity of Posts and Telecommunications.
文摘Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to the unique functional attributes and interaction patterns inherent to different layers.This paper addresses the critical question of whether structural information from a known layer can be used to reconstruct the unknown intralayer structure of a target layer within general weighted output-coupling multilayer networks.Building upon the generalized synchronization principle,we propose an innovative reconstruction method that incorporates two essential components in the design of structure observers,the cross-layer coupling modulator and the structural divergence term.A key advantage of the proposed reconstruction method lies in its flexibility to freely designate both the unknown target layer and the known reference layer from the general weighted output-coupling multilayer network.The reduced dependency on full-state observability enables more deployment in engineering applications with partial measurements.Numerical simulations are conducted to validate the effectiveness of the proposed structure reconstruction method.
基金supported by the National Natural Science Foundation of China(Nos.12422207 and 12372199).
文摘An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.
基金supported by the Beijing Education Commission Science and Technology Project(No.KM201811417005)the National Natural Science Foundation of China(No.62173237)+6 种基金the Aeronautical Science Foundation of China(No.20240055054001)the Open Fund of State Key Laboratory of Satellite Navigation System and Equipment Technology(No.CEPNT2023A01)Joint Fund of Ministry of Natural Resources Key Laboratory of Spatiotemporal Perception and Intelligent Processing(No.232203)the Civil Aviation Flight Technology and Flight Safety Engineering Technology Research Center of Sichuan(No.GY2024-02B)the Applied Basic Research Programs of Liaoning Province(No.2025JH2/101300011)the General Project of Liaoning Provincial Education Department(No.20250054)Research on Safety Intelligent Management Technology and Systems for Mixed Operations of General Aviation Aircraft in Low-Altitude Airspace(No.310125011).
文摘Software systems play increasing important roles in modern society,and the ability against attacks is of great practical importance to crucial software systems,resulting in that the structure and robustness of software systems have attracted a tremendous amount of interest in recent years.In this paper,based on the source code of Tar and MySQL,we propose an approach to generate coupled software networks and construct three kinds of directed software networks:The function call network,the weakly coupled network and the strongly coupled network.The structural properties of these complex networks are extensively investigated.It is found that the average influence and the average dependence for all functions are the same.Moreover,eight attacking strategies and two robustness indicators(the weakly connected indicator and the strongly connected indicator)are introduced to analyze the robustness of software networks.This shows that the strongly coupled network is just a weakly connected network rather than a strongly connected one.For MySQL,high in-degree strategy outperforms other attacking strategies when the weakly connected indicator is used.On the other hand,high out-degree strategy is a good choice when the strongly connected indicator is adopted.This work will highlight a better understanding of the structure and robustness of software networks.
基金National Natural Science Foundation of China(Grant Nos.52408314,52278292)Chongqing Outstanding Youth Science Foundation(Grant No.CSTB2023NSCQ-JQX0029)+1 种基金Science and Technology Project of Sichuan Provincial Transportation Department(Grant No.2023-ZL-03)Science and Technology Project of Guizhou Provincial Transportation Department(Grant No.2024-122-018).
文摘Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration responses with complex features,resulting in a lower reconstruction accuracy.This paper addresses this challenge by leveraging the advanced feature extraction and learning capabilities of fully convolutional networks(FCN)to achieve precise reconstruction of acceleration responses.In the designed network architecture,the incorporation of skip connections preserves low-level details of the network,greatly facilitating the flow of information and improving training efficiency and accuracy.Dropout techniques are employed to reduce computational load and enhance feature extraction.The proposed FCN model automatically extracts high-level features from the input data and establishes a nonlinearmapping relationship between the input and output responses.Finally,the accuracy of the FCN for structural response reconstructionwas evaluated using acceleration data from an experimental arch rib and comparedwith several traditional methods.Additionally,this approach was applied to reconstruct actual acceleration responses measured by an SHM system on a long-span bridge.Through parameter analysis,the feasibility and accuracy of aspects such as available response positions,the number of available channels,and multi-channel response reconstruction were explored.The results indicate that this method exhibits high-precision response reconstruction capability in both time and frequency domains.,with performance surpassing that of other networks,confirming its effectiveness in reconstructing responses under various sensor data loss scenarios.
基金funded by the Key Program of the National Natural Science Foundation of China(U2341235)Youth Fund for Basic Research Program of Jiangnan University(JUSRP123003)+2 种基金Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1237)the National Key R&D Program of China(2018YFA0702800)Key Technologies R&D Program of CNBM(2023SJYL01).
文摘Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.
基金Supported by the National Natural Science Foundation of China,No.81871081 and No.62201265the Fundamental Research Funds for the Central Universities,No.NJ2024029-14the Talent Support Programs of Wuxi Health Commission,No.BJ2023085,No.FZXK2021012,and No.M202358.
文摘BACKGROUND Depression,non-suicidal self-injury(NSSI),and suicide attempts(SA)often co-occur during adolescence and are associated with long-term adverse health outcomes.Unfortunately,neural mechanisms underlying self-injury and SA are poorly understood in depressed adolescents but likely relate to the structural abnormalities in brain regions.AIM To investigate structural network communication within large-scale brain networks in adolescents with depression.METHODS We constructed five distinct network communication models to evaluate structural network efficiency at the whole-brain level in adolescents with depression.Diffusion magnetic resonance imaging data were acquired from 32 healthy controls and 85 depressed adolescents,including 17 depressed adolescents without SA or NSSI(major depressive disorder group),27 depressed adolescents with NSSI but no SA(NSSI group),and 41 depressed adolescents with SA and NSSI(NSSI+SA group).RESULTS Significant differences in structural network communication were observed across the four groups,involving spatially widespread brain regions,particularly encompassing cortico-cortical connections(e.g.,dorsal posterior cingulate gyrus and the right ventral posterior cingulate gyrus;connections based on precentral gyrus)and cortico-subcortical circuits(e.g.,the nucleus accumbens-frontal circuit).In addition,we examined whether compromised communication efficiency was linked to clinical symptoms in the depressed adolescents.We observed significant correlations between network communication efficiencies and clinical scale scores derived from depressed adolescents with NSSI and SA.CONCLUSION This study provides evidence of structural network communication differences in depressed adolescents with NSSI and SA,highlighting impaired neuroanatomical communication efficiency as a potential contributor to their symptoms.These findings offer new insights into the pathophysiological mechanisms underlying the comorbidity of NSSI and SA in adolescent depression.
基金supported by the National Natural Science Foundation of China(Nos.12072118 and12372029)。
文摘This paper presents the variational physics-informed neural network(VPINN)as an effective tool for static structural analyses.One key innovation includes the construction of the neural network solution as an admissible function of the boundary-value problem(BVP),which satisfies all geometrical boundary conditions.We then prove that the admissible neural network solution also satisfies natural boundary conditions,and therefore all boundary conditions,when the stationarity condition of the variational principle is met.Numerical examples are presented to show the advantages and effectiveness of the VPINN in comparison with the physics-informed neural network(PINN).Another contribution of the work is the introduction of Gaussian approximation of the Dirac delta function,which significantly enhances the ability of neural networks to handle singularities,as demonstrated by the examples with concentrated support conditions and loadings.It is hoped that these structural examples are so convincing that engineers would adopt the VPINN method in their structural design practice.
基金financially supported by the National Key R&D Program of China(Grant No.2022YFE0207300)National Natural Science Foundation of China(Grant Nos.22179142 and 22075314)+1 种基金Jiangsu Funding Program for Excellent Postdoctoral Talent(Grant No.2024ZB051 and 2023ZB836)the technical support for Nano-X from Suzhou Institute of Nano-Tech and Nano-Bionics,Chinese Academy of Sciences(SINANO).
文摘Solid polymer electrolytes(SPEs)have attracted much attention for their safety,ease of packaging,costeffectiveness,excellent flexibility and stability.Poly-dioxolane(PDOL)is one of the most promising matrix materials of SPEs due to its remarkable compatibility with lithium metal anodes(LMAs)and suitability for in-situ polymerization.However,poor thermal stability,insufficient ionic conductivity and narrow electrochemical stability window(ESW)hinder its further application in lithium metal batteries(LMBs).To ameliorate these problems,we have successfully synthesized a polymerized-ionic-liquid(PIL)monomer named DIMTFSI by modifying DOL with imidazolium cation coupled with TFSI^(-)anion,which simultaneously inherits the lipophilicity of DOL,high ionic conductivity of imidazole,and excellent stability of PILs.Then the tridentate crosslinker trimethylolpropane tris[3-(2-methyl-1-aziridine)propionate](TTMAP)was introduced to regulate the excessive Li^(+)-O coordination and prepare a flame-retardant SPE(DT-SPE)with prominent thermal stability,wide ESW,high ionic conductivity and abundant Lit transference numbers(t_(Li+)).As a result,the LiFePO_(4)|DT-SPE|Li cell exhibits a high initial discharge specific capacity of 149.60 mAh g^(-1)at 0.2C and 30℃with a capacity retention rate of 98.68%after 500 cycles.This work provides new insights into the structural design of PIL-based electrolytes for long-cycling LMBs with high safety and stability.
文摘Neural-Network Response Surfaces （NNRS） is applied to replace the actual expensive finite element analysis during the composite structural optimization process. The Orthotropic Experiment Method （OEM） is used to select the most appropriate design samples for network training. The trained response surfaces can either be objective function or constraint conditions. Together with other conven- tional constraints, an optimization model is then set up and can be solved by Genetic Algorithm （GA）. This allows the separation between design analysis modeling and optimization searching. Through an example of a hat-stiffened composite plate design, the weight response surface is constructed to be objective function, and strength and buckling response surfaces as constraints; and all of them are trained through NASTRAN finite element analysis. The results of optimization study illustrate that the cycles of structural analysis ean be remarkably reduced or even eliminated during the optimization, thus greatly raising the efficiency of optimization process. It also observed that NNRS approximation can achieve equal or even better accuracy than conventional functional response surfaces.
基金National High-tech Research and Development Program of China(2007AA03Z117)National Natural Science Foundation of China(50830201)Graduate Education Innovation Project of Nanjing University of Aeronautics and Astronautics of China(BCXJ09-01).
文摘The active Lamb wave and piezoelectric transducer(PZT)-based structural health monitoring(SHM)technology is a kind of efficient approach to estimate the health state of aircraft structure.In practical applications,PZT networks are needed to monitor large scale structures.Scanning many of the different PZT actuator-sensor channels within these PZT networks to achieve on-line SHM task is important.Based on a peripheral component interconnect extensions for instrumentation(PXI)platform,an active Lamb wave and PZT network-based integrated multi-channel scanning system(PXI-ISS)is developed for the purpose of practical applications of SHM,which is compact and portable,and can scan large numbers of actuator-sensor channels and perform damage assessing automatically.A PXI-based 4 channels gain-programmable charge amplifier,an external scanning module with 276 actuator-sensor channels and integrated SHM software are proposed and discussed in detail.The experimental research on a carbon fiber composite wing box of an unmanned aerial vehicle(UAV)for verifying the functions of the PXI-ISS is mainly discussed,including the design of PZTs layer,the method of excitation frequency selection,functional test of damage imaging,stability test of the PXI-ISS,and the loading effect on signals.The experimental results have verified the stability and damage functions of this system.
基金Project(20141996018)supported by Aerospace Science Foundation of ChinaProject(2012JZ8005)supported by the Natural Science Fundamental Research Planned Project of Shanxi Province,China
文摘A method of system structural analysis based on decision making trial and evaluation laboratory together with interpretative structural modeling(DEMATEL-ISM) and entropy is proposed to clarify system structure of communication networks and analyze mutual influencing degree between different networks.Mutual influencing degree and importance degree of elements are both considered to determine weights of elements,and the entropy of expert judgment results is used to omit unimportant influence relation and simplify system structure.Structural analysis on communication networks system shows that the proposed method can quantificationally present weights and mutual influencing degree of elements,and reasonably simplify system structure.The results indicate the rationality and feasibility of the method.
基金supported by the National Natural Science Foundation of China(51875465)
文摘The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.
基金the National High Technology Research and Development Program (863) of China (No. 2006AA11Z209)
文摘The purpose of this paper is to identify the critical road sections and intersections in a road network which have great influence on the normal transport functions of the road network and to optimize the road network structure by reducing its vulnerability. In this paper, the framework of road network structural vulnerability measurement and improvement model is proposed. The network efficiency model is used to define road network structural vulnerability. Shanghai freeway network is analyzed based on this model. We find that using this model the critical components of the road network can be identified. Two methods which are increasing connections and rewiring are proposed to optimize the road network structural vulnerability and the results can be used to reduce the network vulnerability. The measurement method that we put forward for structure vulnerability is useful and important to optimize road network structure.
基金financially supported by the National Natural Science Foundation of China(Nos.52233001,51927805,and 52173110)the Innovation Program of Shanghai Municipal Education Commission(No.2023ZKZD07)the Shanghai Rising-Star Program(No.22QA1401200)。
文摘Cholesteric liquid crystals(CLCs)exhibit unique helical superstructures that selectively reflect circularly polarized light,enabling them to dynamically respond to environmental changes with tunable structural colors.This dynamic color-changing capability is crucial for applications that require adaptable optical properties,positioning CLCs as key materials in advanced photonic technologies.This review focuses on the mechanisms of dynamic color tuning in CLCs across various forms,including small molecules,cholesteric liquid crystal elastomers(CLCEs),and cholesteric liquid crystal networks(CLCNs),and emphasizes the distinct responsive coloration each structure provides.Key developments in photochromic mechanisms based on azobenzene,dithienylethene,and molecular motor switches,are discussed for their roles in enhancing the stability and tuning range of CLCs.We examine the color-changing behaviors of CLCEs under mechanical stimuli and CLCNs under swelling,highlighting the advantages of each form.Following this,applications of dynamic color-tuning CLCs in information encryption,adaptive camouflage,and smart sensing technologies are explored.The review concludes with an outlook on current challenges and future directions in CLC research,particularly in biomimetic systems and dynamic photonic devices,aiming to broaden their functional applications and impact.
文摘We propose molten polymer's entanglement network deformation to be nonaffine and use transient network structural theory with the revised Liu's kinetics rate equation and the revised upper convected Maxwell constitutive equation to establish a nonaffine network structural constitutive model for studying the rheological behavior of molten Low Density Polyethylene (LDPE) and High Density Polyethylene (HDPE) in oscillatory shear. As a result, when the strain amplitude or frequency increases, the shear stress amplitude increases. At the same time, the accuracy of the nonaffine network model is higher than that of affine network model. It is clear that there is a small amount of nonaffine network deformation for LDPE melts which have long chain branches, and there is a larger amount of nonaffine network deformation in oscillatory shear for HDPE melts which has no long chain branches. So we had better consider the network deformation nonaffine when we establish the constitutive equations of polymer melts in oscillatory shear.
文摘We propose the deep Lagrange method(DLM),which is a new optimization method,in this study.It is based on a deep neural network to solve optimization problems.The method takes the advantage of deep learning artificial neural networks to find the optimal values of the optimization function instead of solving optimization problems by calculating sensitivity analysis.The DLM method is non-linear and could potentially deal with nonlinear optimization problems.Several test cases on sizing optimization and shape optimization are performed,and their results are then compared with analytical and numerical solutions.
基金supported in part by the National Natural Science Foundation of China(U1808205,62173079)the Natural Science Foundation of Hebei Province of China(F2000501005)。
文摘This paper addresses the problem of the input design of large-scale complex networks.Two types of network components,redundant inaccessible strongly connected component(RISCC)and intermittent inaccessible strongly connected component(IISCC)are defined,and a subnetwork called a driver network is developed.Based on these,an efficient method is proposed to find the minimum number of controlled nodes to achieve structural complete controllability of a network,in the case that each input can act on multiple state nodes.The range of the number of input nodes to achieve minimal control,and the configuration method(the connection between the input nodes and the controlled nodes)are presented.All possible input solutions can be obtained by this method.Moreover,we give an example and some experiments on real-world networks to illustrate the effectiveness of the method.
