Rail weld irregularities are one of the primary excitation sources for vehicle-track interaction dynamics in modern high-speed railways.They can cause significant wheel-rail dynamic interactions,leading to wheel-rail ...Rail weld irregularities are one of the primary excitation sources for vehicle-track interaction dynamics in modern high-speed railways.They can cause significant wheel-rail dynamic interactions,leading to wheel-rail noise,component damage,and deterioration.Few researchers have employed the vehicle-track interaction dynamic model to study the dynamic interactions between wheel and rail induced by rail weld geometry irregularities.However,the cosine wave model used to simulate rail weld irregularities mainly focuses on the maximum value and neglects the geometric shape.In this study,novel theoretical models were developed for three categories of rail weld irregularities,based on measurements of the high-speed railway from Beijing to Shanghai.The vertical dynamic forces in the time and frequency domains were compared under different running speeds.These forces generated by the rail weld irregularities that were measured and modeled,respectively,were compared to validate the accuracy of the proposed model.Finally,based on the numerical study,the impact force due to rail weld irrregularity is modeled using an Artificial Neural Network(ANN),and the optimum combination of parameters for this model is found.The results showed that the proposed model provided a more accurate wheel/rail dynamic evaluation caused by rail weld irregularities than that established in the literature.The ANN model used in this paper can effectively predict the impact force due to rail weld irrregularity while reducing the computation time.展开更多
Revealing the combined influence of interfacial damage and nonlinear factors on the forced vibration is significant for the stability design of fluid-conveying pipes, which are usually assembled in aircraft. The nonli...Revealing the combined influence of interfacial damage and nonlinear factors on the forced vibration is significant for the stability design of fluid-conveying pipes, which are usually assembled in aircraft. The nonlinear forced resonance of fluid-conveying layered pipes with a weak interface and a movable boundary under the external excitation is studied. The pipe is simply supported at both ends, with one end subject to a viscoelastic boundary constraint described by KelvinVoigt model. The weak interface in the pipe is considered in the refined displacement field of the layered pipe employing the interfacial cohesive law. The governing equations are derived by Hamilton's variational principle. Geometric nonlinearities including nonlinear curvature, longitudinal inertia nonlinearity and nonlinear constraint force are comprehensively considered during the theoretical derivation. Amplitude-frequency bifurcation diagrams are obtained utilizing a perturbation-Incremental Harmonic Balance Method(IHBM). Results show that interfacial damage and viscoelastic constraints from boundary and foundation have an important influence on the linear and nonlinear dynamic behavior of the system.展开更多
In this research,we introduce an innovative approach that combines the Continuum Damage Mechanics-Finite Element Method(CDM-FEM)with the Particle Swarm Optimization(PSO)-based technique,to predict the Medium-Low-Cycle...In this research,we introduce an innovative approach that combines the Continuum Damage Mechanics-Finite Element Method(CDM-FEM)with the Particle Swarm Optimization(PSO)-based technique,to predict the Medium-Low-Cycle Fatigue(MLCF)life of perforated structures.First,fatigue tests are carried out on three center-perforated structures,aiming to assess their fatigue life under various strengthening conditions.These tests reveal significant variations in fatigue life,accompanied by an examination of crack initiation through the analysis of fatigue fracture surfaces.Second,an innovative fatigue life prediction methodology is applied to perforated structures,which not only forecasts the initiation of fatigue cracks but also traces the progression of damage within these structures.It leverages an elastoplastic constitutive model integrated with damage and a damage evolution model under cyclic loads.The accuracy of this approach is validated by comparison with test results,falling within the three times error band.Finally,we explore the impact of various strengthening techniques,including cross-sectional reinforcement and cold expansion,on the fatigue life and damage evolution of these structures.This is achieved through an in-depth comparative analysis of both experimental data and computational predictions,which provides valuable insights into the behavior of perforated structures under fatigue conditions in practical applications.展开更多
CO_(2)flooding for enhanced oil recovery(EOR)not only enables underground carbon storage but also plays a critical role in tertiary oil recovery.However,its displacement efficiency is constrained by whether CO_(2)and ...CO_(2)flooding for enhanced oil recovery(EOR)not only enables underground carbon storage but also plays a critical role in tertiary oil recovery.However,its displacement efficiency is constrained by whether CO_(2)and crude oil achieve miscibility,necessitating precise prediction of the minimum miscibility pressure(MMP)for CO_(2)-oil systems.Traditional methods,such as experimental measurements and empirical correlations,face challenges including time-consuming procedures and limited applicability.In contrast,artificial intelligence(AI)algorithms have emerged as superior alternatives due to their efficiency,broad applicability,and high prediction accuracy.This study employs four AI algorithms—Random Forest Regression(RFR),Genetic Algorithm Based Back Propagation Artificial Neural Network(GA-BPNN),Support Vector Regression(SVR),and Gaussian Process Regression(GPR)—to establish predictive models for CO_(2)-oil MMP.A comprehensive database comprising 151 data entries was utilized for model development.The performance of these models was rigorously evaluated using five distinct statistical metrics and visualized comparisons.Validation results confirm their accuracy.Field applications demonstrate that all four models are effective for predicting MMP in ultra-deep reservoirs(burial depth>5000 m)with complex crude oil compositions.Among them,the RFR and GA-BPNN models outperform SVR and GPR,achieving root mean square errors(RMSE)of 0.33%and 2.23%,and average absolute percentage relative errors(AAPRE)of 0.01%and 0.04%,respectively.Sensitivity analysis of MMP-influencing factors reveals that reservoir temperature(T_(R))exerts the most significant impact on MMP,while Xint(mole fraction of intermediate oil components,including C_(2)-C_(4),CO_(2),and H_(2)S)exhibits the least influence.展开更多
In order to reduce power consumption of sensor nodes and extend network survival time in the wireless sensor network （WSN）, sensor nodes are scheduled in an active or dormant mode. A chain-type WSN is fundamental y ...In order to reduce power consumption of sensor nodes and extend network survival time in the wireless sensor network （WSN）, sensor nodes are scheduled in an active or dormant mode. A chain-type WSN is fundamental y different from other types of WSNs, in which the sensor nodes are deployed along elongated geographic areas and form a chain-type network topo-logy structure. This paper investigates the node scheduling prob-lem in the chain-type WSN. Firstly, a node dormant scheduling mode is analyzed theoretical y from geographic coverage, and then three neighboring nodes scheduling criteria are proposed. Sec-ondly, a hybrid coverage scheduling algorithm and dead areas are presented. Final y, node scheduling in mine tunnel WSN with uniform deployment （UD）, non-uniform deployment （NUD） and op-timal distribution point spacing （ODS） is simulated. The results show that the node scheduling with UD and NUD, especial y NUD, can effectively extend the network survival time. Therefore, a strat-egy of adding a few mobile nodes which activate the network in dead areas is proposed, which can further extend the network survival time by balancing the energy consumption of nodes.展开更多
A new continuum damage mechanics model for fretting fatigue life prediction is established. In this model, the damage evolution rate is described by two kinds of quantities. One is associated with the cyclic stress ch...A new continuum damage mechanics model for fretting fatigue life prediction is established. In this model, the damage evolution rate is described by two kinds of quantities. One is associated with the cyclic stress characteristics obtained by the finite element （FE） analysis, and the other is associated with the material fatigue property identified from the fatigue test data of standard specimens. The wear is modeled by the energy wear law to simulate the contact geometry evolution. A two-dimensional （2D） plane strain FE implementation of the damage mechanics model and the energy wear model is presented in the platform of ABAQUS to simulate the evolutions of the fatigue damage and the wear scar. The effect of the specimen thickness is also investigated. The predicted results of the crack initiation site and the fretting fatigue life agree well with available experimental data. Comparisons are made with the critical plane Smith- Watson-Topper （SWT） method.展开更多
An approach based on continuum damage mechanics to fatigue life prediction for structures is proposed. A new fatigue damage evolution equation is developed, in which the pa- rameters are obtained in a simple way with ...An approach based on continuum damage mechanics to fatigue life prediction for structures is proposed. A new fatigue damage evolution equation is developed, in which the pa- rameters are obtained in a simple way with reference to the experimental results of fatigue tests on standard specimens. With the utilization of APDL language on the ANSYS platform, a finite element implementation is presented to perform coupling operation on damage evolution of mate- rial and stress redistribution. The fatigue lives of some notched specimens and a Pitch-change-link are predicted by using the above approach. The calculated results are validated with experimental data.展开更多
The fatigue life prediction for components is a difficult task since many factors can affect the final fatigue life. Based on the damage evolution equation of Lemaitre and Desmorat, a revised two-scale damage evolutio...The fatigue life prediction for components is a difficult task since many factors can affect the final fatigue life. Based on the damage evolution equation of Lemaitre and Desmorat, a revised two-scale damage evolution equation for high cycle fatigue is presented according to the experimental data, in which factors such as the stress amplitude and mean stress are taken into account. Then, a method is proposed to obtain the material parameters of the revised equation from the present fatigue experimental data. Finally, with the utilization of the ANSYS parametric design language （APDL） on the ANSYS platform, the coupling effect between the fatigue damage of materials and the stress distribution in structures is taken into account, and the fatigue life of specimens is predicted. The outcome shows that the numerical prediction is in accord with the experimental results, indicating that the revised two-scale damage evolution model can be well applied for the high cycle fatigue life prediction under uniaxial loading.展开更多
To consider the anisotropic damage in fatigue, an improved boom-panel model is presented to simulate a representative volume element (RVE) in the framework of continuum damage mechanics. The anisotropic damage state o...To consider the anisotropic damage in fatigue, an improved boom-panel model is presented to simulate a representative volume element (RVE) in the framework of continuum damage mechanics. The anisotropic damage state of the RVE is described by the continuity extents of booms and panels, whose damage evolutions are assumed to be isotropic. The numerical implementation is proposed on the basis of damage mechanics and the finite element method. Finally, the approach is applied to the fatigue life prediction of 2A12-T4 aluminium alloy specimen under cyclic loading of tension-torsion. The results indicate a good agreement with the experimental data.展开更多
Presently,the environmental pollution problem,especially that of the small and medium-sized enterprises,has become a bottleneck restricting the high-quality development of China’s economy.It is imperative to accelera...Presently,the environmental pollution problem,especially that of the small and medium-sized enterprises,has become a bottleneck restricting the high-quality development of China’s economy.It is imperative to accelerate the construction of a collaborative governance system with multiple subject participation.The behavior modes of Environmental Non-governmental Organizations(ENGOs),a key player in environmental care,in collaborating with core enterprises to control pollution from small and medium-sized suppliers should be explored.Therefore,we constructed a collaborative governance system consisting of ENGOs,focal firms and small and medium-sized suppliers.Then a two-stage game model was established to analyze the strategies of the governance system.Specifically,we studied the collaborative green cooperation strategy and antagonistic pressure supervision strategy between ENGOs and focal firms,analyzed factors influencing the strategic choices of each subject,and gave the advantages of the cooperation strategies through further comparisons.The results showed that:Under the cooperation strategy,ENGOs provided knowledge of environmental protection to the focal firms,then both the audit efforts of focal firms and the environmental protection efforts of small and medium-sized suppliers were effectively improved.Since ENGOs could not fully obtain the pollution information of small and medium-sized suppliers or accurately trace it from their downstream focal firms,it was difficult to drive the supply chain’s endogenous governance by this external monitoring of ENGOs,making the effect of pressure supervision strategy limited.The effectiveness of green cooperation strategy was positively correlated with the knowledge absorption capacity of focal firms,the unit product revenue,and the focal firms'violation penalties for small and medium-sized suppliers.When ENGOs'violation penalties for small and medium-sized suppliers were higher,or the reputation loss of focal firms was higher,more unfavorable conditions of green cooperation strategy could be achieved.Accordingly,ENGOs should choose to cooperate with focal firms with strong knowledge absorption ability and high profit per product;focal firms should learn environmental protection knowledge to improve the screening standards and review capabilities,and promote the achievement of green cooperation strategy conditions by strengthening active information disclosure;the government should promote the collaboration between ENGOs and focal firms by issuing environmental guidelines.展开更多
Effective risk response decision-making in complex contexts often necessitates addressing multiple interacting risk events under resource constraints.However,existing models have predominantly focused on single-type o...Effective risk response decision-making in complex contexts often necessitates addressing multiple interacting risk events under resource constraints.However,existing models have predominantly focused on single-type or pairwise interactions and incorporated only a limited range of influencing factors,thus facing substantial challenges in accurately capturing high-dimensional,heterogeneous interactions while maintaining computational tractability in large-scale problems.To overcome these limitations,this paper proposes a more reliable risk response decision-making framework capable of delivering precise solutions for large-scale decision problems.Specifically,the main contributions of this study encompass the following three aspects:(1)A comprehensive risk response decision model is developed to concurrently integrate multiple types of risk interactions and critical factors,thereby better reflecting real-world conditions and enhancing decision quality.(2)To mitigate the computational burden arising from the exponential growth of nonlinear terms,an equivalent formulation based on risk scenario decomposition is introduced,enabling the precise resolution of large-scale instances.(3)A Dynamic Infeasibility-Guided Genetic Algorithm is designed to efficiently generate high-quality solutions for extra-large-scale problems.Experimental evaluations demonstrate that the proposed algorithm consistently outperforms traditional approaches in terms of solution quality,stability,and computational efficiency across thousand-dimensional problem instances,underscoring its strong performance and scalability.This research provides a scalable and effective solution framework for complex risk response decision-making,offering valuable insights for large-scale risk management applications.展开更多
基金supported by Natural Science Foundation of China(52178441)the Scientific Research Projects of the China Academy of Railway Sciences Co.,Ltd.(Grant No.2022YJ043).
文摘Rail weld irregularities are one of the primary excitation sources for vehicle-track interaction dynamics in modern high-speed railways.They can cause significant wheel-rail dynamic interactions,leading to wheel-rail noise,component damage,and deterioration.Few researchers have employed the vehicle-track interaction dynamic model to study the dynamic interactions between wheel and rail induced by rail weld geometry irregularities.However,the cosine wave model used to simulate rail weld irregularities mainly focuses on the maximum value and neglects the geometric shape.In this study,novel theoretical models were developed for three categories of rail weld irregularities,based on measurements of the high-speed railway from Beijing to Shanghai.The vertical dynamic forces in the time and frequency domains were compared under different running speeds.These forces generated by the rail weld irregularities that were measured and modeled,respectively,were compared to validate the accuracy of the proposed model.Finally,based on the numerical study,the impact force due to rail weld irrregularity is modeled using an Artificial Neural Network(ANN),and the optimum combination of parameters for this model is found.The results showed that the proposed model provided a more accurate wheel/rail dynamic evaluation caused by rail weld irregularities than that established in the literature.The ANN model used in this paper can effectively predict the impact force due to rail weld irrregularity while reducing the computation time.
文摘Revealing the combined influence of interfacial damage and nonlinear factors on the forced vibration is significant for the stability design of fluid-conveying pipes, which are usually assembled in aircraft. The nonlinear forced resonance of fluid-conveying layered pipes with a weak interface and a movable boundary under the external excitation is studied. The pipe is simply supported at both ends, with one end subject to a viscoelastic boundary constraint described by KelvinVoigt model. The weak interface in the pipe is considered in the refined displacement field of the layered pipe employing the interfacial cohesive law. The governing equations are derived by Hamilton's variational principle. Geometric nonlinearities including nonlinear curvature, longitudinal inertia nonlinearity and nonlinear constraint force are comprehensively considered during the theoretical derivation. Amplitude-frequency bifurcation diagrams are obtained utilizing a perturbation-Incremental Harmonic Balance Method(IHBM). Results show that interfacial damage and viscoelastic constraints from boundary and foundation have an important influence on the linear and nonlinear dynamic behavior of the system.
基金support from the National Natural Science Foundation of China(No.12472072)the Fundamental Research Funds for the Central Universities,China.
文摘In this research,we introduce an innovative approach that combines the Continuum Damage Mechanics-Finite Element Method(CDM-FEM)with the Particle Swarm Optimization(PSO)-based technique,to predict the Medium-Low-Cycle Fatigue(MLCF)life of perforated structures.First,fatigue tests are carried out on three center-perforated structures,aiming to assess their fatigue life under various strengthening conditions.These tests reveal significant variations in fatigue life,accompanied by an examination of crack initiation through the analysis of fatigue fracture surfaces.Second,an innovative fatigue life prediction methodology is applied to perforated structures,which not only forecasts the initiation of fatigue cracks but also traces the progression of damage within these structures.It leverages an elastoplastic constitutive model integrated with damage and a damage evolution model under cyclic loads.The accuracy of this approach is validated by comparison with test results,falling within the three times error band.Finally,we explore the impact of various strengthening techniques,including cross-sectional reinforcement and cold expansion,on the fatigue life and damage evolution of these structures.This is achieved through an in-depth comparative analysis of both experimental data and computational predictions,which provides valuable insights into the behavior of perforated structures under fatigue conditions in practical applications.
文摘CO_(2)flooding for enhanced oil recovery(EOR)not only enables underground carbon storage but also plays a critical role in tertiary oil recovery.However,its displacement efficiency is constrained by whether CO_(2)and crude oil achieve miscibility,necessitating precise prediction of the minimum miscibility pressure(MMP)for CO_(2)-oil systems.Traditional methods,such as experimental measurements and empirical correlations,face challenges including time-consuming procedures and limited applicability.In contrast,artificial intelligence(AI)algorithms have emerged as superior alternatives due to their efficiency,broad applicability,and high prediction accuracy.This study employs four AI algorithms—Random Forest Regression(RFR),Genetic Algorithm Based Back Propagation Artificial Neural Network(GA-BPNN),Support Vector Regression(SVR),and Gaussian Process Regression(GPR)—to establish predictive models for CO_(2)-oil MMP.A comprehensive database comprising 151 data entries was utilized for model development.The performance of these models was rigorously evaluated using five distinct statistical metrics and visualized comparisons.Validation results confirm their accuracy.Field applications demonstrate that all four models are effective for predicting MMP in ultra-deep reservoirs(burial depth>5000 m)with complex crude oil compositions.Among them,the RFR and GA-BPNN models outperform SVR and GPR,achieving root mean square errors(RMSE)of 0.33%and 2.23%,and average absolute percentage relative errors(AAPRE)of 0.01%and 0.04%,respectively.Sensitivity analysis of MMP-influencing factors reveals that reservoir temperature(T_(R))exerts the most significant impact on MMP,while Xint(mole fraction of intermediate oil components,including C_(2)-C_(4),CO_(2),and H_(2)S)exhibits the least influence.
基金supported by the China Doctoral Discipline New Teacher Foundation(200802901507)the Sichuan Province Basic Research Plan Project(2013JY0165)the Cultivating Programme of Excellent Innovation Team of Chengdu University of Technology(KYTD201301)
文摘In order to reduce power consumption of sensor nodes and extend network survival time in the wireless sensor network （WSN）, sensor nodes are scheduled in an active or dormant mode. A chain-type WSN is fundamental y different from other types of WSNs, in which the sensor nodes are deployed along elongated geographic areas and form a chain-type network topo-logy structure. This paper investigates the node scheduling prob-lem in the chain-type WSN. Firstly, a node dormant scheduling mode is analyzed theoretical y from geographic coverage, and then three neighboring nodes scheduling criteria are proposed. Sec-ondly, a hybrid coverage scheduling algorithm and dead areas are presented. Final y, node scheduling in mine tunnel WSN with uniform deployment （UD）, non-uniform deployment （NUD） and op-timal distribution point spacing （ODS） is simulated. The results show that the node scheduling with UD and NUD, especial y NUD, can effectively extend the network survival time. Therefore, a strat-egy of adding a few mobile nodes which activate the network in dead areas is proposed, which can further extend the network survival time by balancing the energy consumption of nodes.
基金supported by the National Natural Science Foundation of China(No.11002010)
文摘A new continuum damage mechanics model for fretting fatigue life prediction is established. In this model, the damage evolution rate is described by two kinds of quantities. One is associated with the cyclic stress characteristics obtained by the finite element （FE） analysis, and the other is associated with the material fatigue property identified from the fatigue test data of standard specimens. The wear is modeled by the energy wear law to simulate the contact geometry evolution. A two-dimensional （2D） plane strain FE implementation of the damage mechanics model and the energy wear model is presented in the platform of ABAQUS to simulate the evolutions of the fatigue damage and the wear scar. The effect of the specimen thickness is also investigated. The predicted results of the crack initiation site and the fretting fatigue life agree well with available experimental data. Comparisons are made with the critical plane Smith- Watson-Topper （SWT） method.
基金supported by the National Natural Science Foundation of China(No.11002010)
文摘An approach based on continuum damage mechanics to fatigue life prediction for structures is proposed. A new fatigue damage evolution equation is developed, in which the pa- rameters are obtained in a simple way with reference to the experimental results of fatigue tests on standard specimens. With the utilization of APDL language on the ANSYS platform, a finite element implementation is presented to perform coupling operation on damage evolution of mate- rial and stress redistribution. The fatigue lives of some notched specimens and a Pitch-change-link are predicted by using the above approach. The calculated results are validated with experimental data.
文摘The fatigue life prediction for components is a difficult task since many factors can affect the final fatigue life. Based on the damage evolution equation of Lemaitre and Desmorat, a revised two-scale damage evolution equation for high cycle fatigue is presented according to the experimental data, in which factors such as the stress amplitude and mean stress are taken into account. Then, a method is proposed to obtain the material parameters of the revised equation from the present fatigue experimental data. Finally, with the utilization of the ANSYS parametric design language （APDL） on the ANSYS platform, the coupling effect between the fatigue damage of materials and the stress distribution in structures is taken into account, and the fatigue life of specimens is predicted. The outcome shows that the numerical prediction is in accord with the experimental results, indicating that the revised two-scale damage evolution model can be well applied for the high cycle fatigue life prediction under uniaxial loading.
基金Project supported by the National Natural Science Foundation of China(No.11102008)
文摘To consider the anisotropic damage in fatigue, an improved boom-panel model is presented to simulate a representative volume element (RVE) in the framework of continuum damage mechanics. The anisotropic damage state of the RVE is described by the continuity extents of booms and panels, whose damage evolutions are assumed to be isotropic. The numerical implementation is proposed on the basis of damage mechanics and the finite element method. Finally, the approach is applied to the fatigue life prediction of 2A12-T4 aluminium alloy specimen under cyclic loading of tension-torsion. The results indicate a good agreement with the experimental data.
文摘Presently,the environmental pollution problem,especially that of the small and medium-sized enterprises,has become a bottleneck restricting the high-quality development of China’s economy.It is imperative to accelerate the construction of a collaborative governance system with multiple subject participation.The behavior modes of Environmental Non-governmental Organizations(ENGOs),a key player in environmental care,in collaborating with core enterprises to control pollution from small and medium-sized suppliers should be explored.Therefore,we constructed a collaborative governance system consisting of ENGOs,focal firms and small and medium-sized suppliers.Then a two-stage game model was established to analyze the strategies of the governance system.Specifically,we studied the collaborative green cooperation strategy and antagonistic pressure supervision strategy between ENGOs and focal firms,analyzed factors influencing the strategic choices of each subject,and gave the advantages of the cooperation strategies through further comparisons.The results showed that:Under the cooperation strategy,ENGOs provided knowledge of environmental protection to the focal firms,then both the audit efforts of focal firms and the environmental protection efforts of small and medium-sized suppliers were effectively improved.Since ENGOs could not fully obtain the pollution information of small and medium-sized suppliers or accurately trace it from their downstream focal firms,it was difficult to drive the supply chain’s endogenous governance by this external monitoring of ENGOs,making the effect of pressure supervision strategy limited.The effectiveness of green cooperation strategy was positively correlated with the knowledge absorption capacity of focal firms,the unit product revenue,and the focal firms'violation penalties for small and medium-sized suppliers.When ENGOs'violation penalties for small and medium-sized suppliers were higher,or the reputation loss of focal firms was higher,more unfavorable conditions of green cooperation strategy could be achieved.Accordingly,ENGOs should choose to cooperate with focal firms with strong knowledge absorption ability and high profit per product;focal firms should learn environmental protection knowledge to improve the screening standards and review capabilities,and promote the achievement of green cooperation strategy conditions by strengthening active information disclosure;the government should promote the collaboration between ENGOs and focal firms by issuing environmental guidelines.
基金funded by the National Natural Science Foundation of China(Grant numbers:72271144,72134004)China Postdoctoral Science Foundation(Grant number:2022T150379)Project of Young Talents Team for Philosophy and Social Sciences in Shandong Province(Grant number:2024-QNRC-01).
文摘Effective risk response decision-making in complex contexts often necessitates addressing multiple interacting risk events under resource constraints.However,existing models have predominantly focused on single-type or pairwise interactions and incorporated only a limited range of influencing factors,thus facing substantial challenges in accurately capturing high-dimensional,heterogeneous interactions while maintaining computational tractability in large-scale problems.To overcome these limitations,this paper proposes a more reliable risk response decision-making framework capable of delivering precise solutions for large-scale decision problems.Specifically,the main contributions of this study encompass the following three aspects:(1)A comprehensive risk response decision model is developed to concurrently integrate multiple types of risk interactions and critical factors,thereby better reflecting real-world conditions and enhancing decision quality.(2)To mitigate the computational burden arising from the exponential growth of nonlinear terms,an equivalent formulation based on risk scenario decomposition is introduced,enabling the precise resolution of large-scale instances.(3)A Dynamic Infeasibility-Guided Genetic Algorithm is designed to efficiently generate high-quality solutions for extra-large-scale problems.Experimental evaluations demonstrate that the proposed algorithm consistently outperforms traditional approaches in terms of solution quality,stability,and computational efficiency across thousand-dimensional problem instances,underscoring its strong performance and scalability.This research provides a scalable and effective solution framework for complex risk response decision-making,offering valuable insights for large-scale risk management applications.
