Online First
Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes/issues, but are citable by Digital Object Identifier (DOI).
Display Method:
, Available online , doi: 10.11883/bzycj-2025-0072
Read OL
PDF 
Cited By
Abstract:
In order to improve the explosion resistance of the blast wall, it is proposed to combine the negative Poisson’s ratio structure with ultra-high toughness cementitious composites (UHTCC), and through a combination of the explosion experiment and numerical simulation, the anti-explosive property of the negative Poisson’s ratio slab has been studied, in order to prove the superiority of the anti-explosive properties of the negative Poisson’s ratio UHTCC slab. Firstly, the construction of a negative Poisson’s ratio structural slab was realized by using concrete 3D printing technology and optimizing the printing path, which verified the constructability of the negative Poisson’s ratio structural slab and the negative Poisson’s slab was subjected to a contact explosion test. Using LS-DYNA software, a finite element model of fluid-solid coupling was established in accordance with the explosion test conditions and the finite element model was verified by comparison of the slab damage pattern of the contact explosion test and the slab damage pattern of the simulation. On this basis, the finite element model which has been verified was used to simulate and analyze the effects of different materials of slabs(concrete and UHTCC), different structures of slabs(negative Poisson’s ratio structure, positive Poisson’s ratio structure and solid structure), different cell concave angles and different solid layer thickness ratios on the anti-explosive properties of the negative Poisson’s structural slab under contact explosion. By comparing the slab damage patterns and the ability of energy absorption which was determined by the value of the air overpressure behind the slabs, the design of a negative Poisson’s ratio structure target plate with the best anti-explosive properties was obtained. The results show that: (1) Due to the high toughness, explosion resistance of UHTCC slabs is significantly better than the concrete slabs.The UHTCC slabs all remained intact and the concrete target slabs are all penetrated. (2) Negative Poisson’s ratio slab has the best ability to absorb energy during three kinds of structures, while the solid slab is more able to maintain the structural integrity. (3) When the negative Poisson’s ratio of the cell concave angle is 61°, the structure has optimal explosion resistance, and smaller and larger angle both reduce the explosion resistance of structure. (4) When the thickness of the negative Poisson’s ratio structure is too large as a proportion of the total thickness, the slab is severely damaged. Increasing the solid layer thickness of the backburst surface of the slab or increasing the solid layer thickness of the explosion-facing surface and the backburst surface at the same time is conducive to weakening of the blast shock wave and improving structural integrity. This study confirmed the superiority of the explosion resistance of negative Poisson’s ratio UHTCC slab, and provides a theoretical basis for the design of blast walls based on negative Poisson’s ratio structure.
In order to improve the explosion resistance of the blast wall, it is proposed to combine the negative Poisson’s ratio structure with ultra-high toughness cementitious composites (UHTCC), and through a combination of the explosion experiment and numerical simulation, the anti-explosive property of the negative Poisson’s ratio slab has been studied, in order to prove the superiority of the anti-explosive properties of the negative Poisson’s ratio UHTCC slab. Firstly, the construction of a negative Poisson’s ratio structural slab was realized by using concrete 3D printing technology and optimizing the printing path, which verified the constructability of the negative Poisson’s ratio structural slab and the negative Poisson’s slab was subjected to a contact explosion test. Using LS-DYNA software, a finite element model of fluid-solid coupling was established in accordance with the explosion test conditions and the finite element model was verified by comparison of the slab damage pattern of the contact explosion test and the slab damage pattern of the simulation. On this basis, the finite element model which has been verified was used to simulate and analyze the effects of different materials of slabs(concrete and UHTCC), different structures of slabs(negative Poisson’s ratio structure, positive Poisson’s ratio structure and solid structure), different cell concave angles and different solid layer thickness ratios on the anti-explosive properties of the negative Poisson’s structural slab under contact explosion. By comparing the slab damage patterns and the ability of energy absorption which was determined by the value of the air overpressure behind the slabs, the design of a negative Poisson’s ratio structure target plate with the best anti-explosive properties was obtained. The results show that: (1) Due to the high toughness, explosion resistance of UHTCC slabs is significantly better than the concrete slabs.The UHTCC slabs all remained intact and the concrete target slabs are all penetrated. (2) Negative Poisson’s ratio slab has the best ability to absorb energy during three kinds of structures, while the solid slab is more able to maintain the structural integrity. (3) When the negative Poisson’s ratio of the cell concave angle is 61°, the structure has optimal explosion resistance, and smaller and larger angle both reduce the explosion resistance of structure. (4) When the thickness of the negative Poisson’s ratio structure is too large as a proportion of the total thickness, the slab is severely damaged. Increasing the solid layer thickness of the backburst surface of the slab or increasing the solid layer thickness of the explosion-facing surface and the backburst surface at the same time is conducive to weakening of the blast shock wave and improving structural integrity. This study confirmed the superiority of the explosion resistance of negative Poisson’s ratio UHTCC slab, and provides a theoretical basis for the design of blast walls based on negative Poisson’s ratio structure.
, Available online , doi: 10.11883/bzycj-2024-0428
Abstract:
In order to study the distribution of blast wave load of building surface under surface burst, firstly, the fine scaled experiments under laboratory environment were conducted. The blast wave pressure-time curves on the surface of building model under the situation of surface burst of spherical charge as well as the distribution law of blast wave characteristic parameters were obtained. Subsequently, the numerical simulation method of blast wave propagation was developed and verified by the experimental data. Through simulation, the blast load distribution and time-histories of blast pressure on the rear face of building were analyzed. Finally, the theoretical method based on blast wave time-history analysis and superposition rule was proposed, and the quantitative analysis model of the blast load distribution on the rear face of building which was verified by numerical results was obtained. The results show that the maximum blast load on the front face of building located at the bottom of the building, which the overall distribution was relatively uniform. The blast load on the rear face of building was mainly concentrated on the two sides of the top angle and the central axis, which was formed by the superposition of the diffraction waves from top and side edges, and the maximum overpressure occurred at the intersection position of different diffraction shock waves, which is affected by the building size and explosion distance.
In order to study the distribution of blast wave load of building surface under surface burst, firstly, the fine scaled experiments under laboratory environment were conducted. The blast wave pressure-time curves on the surface of building model under the situation of surface burst of spherical charge as well as the distribution law of blast wave characteristic parameters were obtained. Subsequently, the numerical simulation method of blast wave propagation was developed and verified by the experimental data. Through simulation, the blast load distribution and time-histories of blast pressure on the rear face of building were analyzed. Finally, the theoretical method based on blast wave time-history analysis and superposition rule was proposed, and the quantitative analysis model of the blast load distribution on the rear face of building which was verified by numerical results was obtained. The results show that the maximum blast load on the front face of building located at the bottom of the building, which the overall distribution was relatively uniform. The blast load on the rear face of building was mainly concentrated on the two sides of the top angle and the central axis, which was formed by the superposition of the diffraction waves from top and side edges, and the maximum overpressure occurred at the intersection position of different diffraction shock waves, which is affected by the building size and explosion distance.
, Available online , doi: 10.11883/bzycj-2025-0108
Abstract:
In order to investigate the impact of target damage on projectile penetration performance, a series of penetration experiments were conducted on a concrete target utilising a former jet and a subsequent kinetic energy projectile. The critical factors influencing the performance of pre-damaged concrete penetrated by the projectile were analyzed. The relationship between the strength of the concrete materials in the pre-damaged concrete target was determined. Based on this, a semi-empirical model of projectile penetration of pre-damaged concrete was established by combining the aforementioned cavity expansion theory with the results of the preceding analysis. The impact of projectile and target parameters on the performance of secondary penetration of the projectile was then analyzed. The findings indicate that the impact of pre-damaged concrete on the depth of projectile penetration is contingent upon the discrepancy in crater volume and concrete damage. It can be posited that the damage to the target is the predominant influencing factor. When there is a finite-length damage zone within the concrete target and the diameter of the cavity of the target is between 0.3 and 0.5 time the diameter of the projectile, the effect is even less pronounced. When a finite-length damage zone exists within the target, the pre-damage cavity is 0.3-0.5 times the diameter of the projectile. In this instance, the gain in depth of penetration is most pronounced. In the event of penetrating damage to the target, a ratio of 0.3 between the diameter of the target tunnel and that of the projectile is observed. The difference in penetration depth between the pre-damaged target and the pre-drilled target is found to be greater, with a gradual increase in this difference as the ratio increases further. When the damage state of the target is certain, decreasing the projectile diameter or increasing the CRH of the ogive-nosed projectile is more advantageous to increase the depth of penetration.
In order to investigate the impact of target damage on projectile penetration performance, a series of penetration experiments were conducted on a concrete target utilising a former jet and a subsequent kinetic energy projectile. The critical factors influencing the performance of pre-damaged concrete penetrated by the projectile were analyzed. The relationship between the strength of the concrete materials in the pre-damaged concrete target was determined. Based on this, a semi-empirical model of projectile penetration of pre-damaged concrete was established by combining the aforementioned cavity expansion theory with the results of the preceding analysis. The impact of projectile and target parameters on the performance of secondary penetration of the projectile was then analyzed. The findings indicate that the impact of pre-damaged concrete on the depth of projectile penetration is contingent upon the discrepancy in crater volume and concrete damage. It can be posited that the damage to the target is the predominant influencing factor. When there is a finite-length damage zone within the concrete target and the diameter of the cavity of the target is between 0.3 and 0.5 time the diameter of the projectile, the effect is even less pronounced. When a finite-length damage zone exists within the target, the pre-damage cavity is 0.3-0.5 times the diameter of the projectile. In this instance, the gain in depth of penetration is most pronounced. In the event of penetrating damage to the target, a ratio of 0.3 between the diameter of the target tunnel and that of the projectile is observed. The difference in penetration depth between the pre-damaged target and the pre-drilled target is found to be greater, with a gradual increase in this difference as the ratio increases further. When the damage state of the target is certain, decreasing the projectile diameter or increasing the CRH of the ogive-nosed projectile is more advantageous to increase the depth of penetration.
, Available online , doi: 10.11883/bzycj-2025-0103
Abstract:
To accurately characterize the stress-strain constitutive relationship of metal materials under high strain-rate conditions, a novel, high-precision constitutive-relationship-prediction model based on Graph Neural Networks (GNNs) and Kolmogorov-Arnold Networks (KANs) was developed. Traditional Johnson-Cook (JC) models often fail to account for the coupling effects among temperature, strain rate, and strain, all of which are crucial for describing the dynamic behavior of materials under extreme conditions. This limitation was addressed by constructing graph-structured data in the GNN model to capture the nonlinear correlations of multidimensional parameters and by leveraging the Kolmogorov-Arnold theorem in the KAN model to achieve precise mapping of high-dimensional input spaces. The research methodology involved several key steps. Experimental data from ODS copper subjected to high-strain-rate compression were collected using a split Hopkinson pressure bar (SHPB) system and subsequently preprocessed. The dataset included temperature, strain rate, strain, and stress. In the GNN model, when temperature and strain rate were held constant, nodes were connected sequentially based on strain values to form edges. When temperature was held constant, a reasonable threshold was established between nodes with adjacent strain rates, and nodes within this threshold were connected to form edges. The GNN employed a Message Passing Neural Network (MPNN) architecture to learn and predict material properties. Model parameters were optimized using the Adam optimizer, with the Root Mean Squared Error (RMSE) serving as the loss function. The KAN model was constructed based on the Kolmogorov-Arnold representation theorem and consisted of multiple KAN-Linear layers. Each KAN-Linear unit included base weights and spline weights. Base weights handled linear relationships through traditional linear transformations, while spline weights managed nonlinear mappings via B-spline interpolation. Both models were trained on the preprocessed dataset, and their performance was evaluated using metrics such as the Mean Relative Error (MRE), Root Mean Squared Error (RMSE), and the coefficient of determination (R2). The GNN model achieved an average MRE of 9.2% with an R2 value exceeding 0.95, while the KAN model recorded an MRE of 9.1% with a similar R2 value. Both models significantly outperformed the JC model, which had an MRE of 38% and an R2 value of 0.75. Furthermore, the predictive capabilities of the GNN and KAN models were validated through finite element simulations. The simulation results demonstrated that the stress-strain distributions predicted by the GNN and KAN models were more consistent with theoretical expectations compared to those predicted by the JC model, particularly in capturing the material's softening phase. The findings highlight the potential of integrating advanced machine - learning techniques, such as GNNs and KANs, into the field of materials science to enhance the accuracy and efficiency of constitutive modeling. These models offer a promising alternative to traditional empirical models and hold significant implications for engineering applications in aerospace, automotive, and other industries where materials are subjected to high strain rates.
To accurately characterize the stress-strain constitutive relationship of metal materials under high strain-rate conditions, a novel, high-precision constitutive-relationship-prediction model based on Graph Neural Networks (GNNs) and Kolmogorov-Arnold Networks (KANs) was developed. Traditional Johnson-Cook (JC) models often fail to account for the coupling effects among temperature, strain rate, and strain, all of which are crucial for describing the dynamic behavior of materials under extreme conditions. This limitation was addressed by constructing graph-structured data in the GNN model to capture the nonlinear correlations of multidimensional parameters and by leveraging the Kolmogorov-Arnold theorem in the KAN model to achieve precise mapping of high-dimensional input spaces. The research methodology involved several key steps. Experimental data from ODS copper subjected to high-strain-rate compression were collected using a split Hopkinson pressure bar (SHPB) system and subsequently preprocessed. The dataset included temperature, strain rate, strain, and stress. In the GNN model, when temperature and strain rate were held constant, nodes were connected sequentially based on strain values to form edges. When temperature was held constant, a reasonable threshold was established between nodes with adjacent strain rates, and nodes within this threshold were connected to form edges. The GNN employed a Message Passing Neural Network (MPNN) architecture to learn and predict material properties. Model parameters were optimized using the Adam optimizer, with the Root Mean Squared Error (RMSE) serving as the loss function. The KAN model was constructed based on the Kolmogorov-Arnold representation theorem and consisted of multiple KAN-Linear layers. Each KAN-Linear unit included base weights and spline weights. Base weights handled linear relationships through traditional linear transformations, while spline weights managed nonlinear mappings via B-spline interpolation. Both models were trained on the preprocessed dataset, and their performance was evaluated using metrics such as the Mean Relative Error (MRE), Root Mean Squared Error (RMSE), and the coefficient of determination (R2). The GNN model achieved an average MRE of 9.2% with an R2 value exceeding 0.95, while the KAN model recorded an MRE of 9.1% with a similar R2 value. Both models significantly outperformed the JC model, which had an MRE of 38% and an R2 value of 0.75. Furthermore, the predictive capabilities of the GNN and KAN models were validated through finite element simulations. The simulation results demonstrated that the stress-strain distributions predicted by the GNN and KAN models were more consistent with theoretical expectations compared to those predicted by the JC model, particularly in capturing the material's softening phase. The findings highlight the potential of integrating advanced machine - learning techniques, such as GNNs and KANs, into the field of materials science to enhance the accuracy and efficiency of constitutive modeling. These models offer a promising alternative to traditional empirical models and hold significant implications for engineering applications in aerospace, automotive, and other industries where materials are subjected to high strain rates.
, Available online , doi: 10.11883/bzycj-2025-0290
Abstract:
Hypergravity centrifuge model testing serves as an effective method for simulating prototype explosion effects, whose successful application relies on soil simulants capable of replicating the dynamic response of in-situ soil. To address the challenges of particle size effects and material similarity in centrifuge modeling of explosions in sandy gravel, this study aims to establish a systematic methodology for the preparation and validation of such simulants. Through theoretical analysis, the soil key parameters governing ground shock effects under explosions were identified as density and wave velocity (wave impedance), which are fundamentally controlled by the soil's gradation characteristics. Based on this premise, twelve types of simulants with varying maximum particle sizes were systematically prepared using four scaling methods: the removal method, equal quantity replacement method, similar gradation method, and hybrid method. Through void ratio tests and bender element testing under effective confining pressure, quantitative relationships were revealed between the extreme void ratios of sandy gravel and its fines content and mean particle size. Based on this, an empirical predictive model for the small strain elastic modulus was established. Comparison of the model-predicted wave velocities with in-situ measured data indicates that the coefficient of uniformity, fines content, and mean particle size are the key controlling indices for achieving dynamic similarity in sandy gravel under explosion loading. Among these, the simulant prepared by the equal quantity replacement method, with a maximum particle size of 10 mm, demonstrated the closest equivalence to the in-situ soil in terms of the aforementioned indices. Hypergravity centrifuge explosion tests using this equivalent simulant further verified that the attenuation law of normalized peak accelerations within the source plane corresponds highly consistently with the in-situ data. This research confirms that by controlling key gradation indices and employing the equal quantity replacement method, it is possible to successfully prepare simulants that are equivalent to in-situ sandy gravel in their dynamic response to explosions. This provides a practical and effective technical pathway for centrifuge model testing in related fields.
Hypergravity centrifuge model testing serves as an effective method for simulating prototype explosion effects, whose successful application relies on soil simulants capable of replicating the dynamic response of in-situ soil. To address the challenges of particle size effects and material similarity in centrifuge modeling of explosions in sandy gravel, this study aims to establish a systematic methodology for the preparation and validation of such simulants. Through theoretical analysis, the soil key parameters governing ground shock effects under explosions were identified as density and wave velocity (wave impedance), which are fundamentally controlled by the soil's gradation characteristics. Based on this premise, twelve types of simulants with varying maximum particle sizes were systematically prepared using four scaling methods: the removal method, equal quantity replacement method, similar gradation method, and hybrid method. Through void ratio tests and bender element testing under effective confining pressure, quantitative relationships were revealed between the extreme void ratios of sandy gravel and its fines content and mean particle size. Based on this, an empirical predictive model for the small strain elastic modulus was established. Comparison of the model-predicted wave velocities with in-situ measured data indicates that the coefficient of uniformity, fines content, and mean particle size are the key controlling indices for achieving dynamic similarity in sandy gravel under explosion loading. Among these, the simulant prepared by the equal quantity replacement method, with a maximum particle size of 10 mm, demonstrated the closest equivalence to the in-situ soil in terms of the aforementioned indices. Hypergravity centrifuge explosion tests using this equivalent simulant further verified that the attenuation law of normalized peak accelerations within the source plane corresponds highly consistently with the in-situ data. This research confirms that by controlling key gradation indices and employing the equal quantity replacement method, it is possible to successfully prepare simulants that are equivalent to in-situ sandy gravel in their dynamic response to explosions. This provides a practical and effective technical pathway for centrifuge model testing in related fields.
, Available online , doi: 10.11883/bzycj-2025-0288
Abstract:
Strut-based lattice metamaterials are a category of ultra-lightweight, load-bearing, and energy-absorbing materials with broad application prospects in fields such as impact protection, aerospace engineering, and lightweight structural design. Benefiting from their unique periodic architectures and adjustable meso-structural parameters, these materials exhibit exceptional mechanical tunability and multifunctional potential. However, due to the extensive parameter space of mesoscopic configurations and the highly nonlinear correlation between the structural geometry and the mechanical response, the optimization of mechanical performance for lattice metamaterials remains a formidable challenge. Based on the meso-structural characteristics of strut-based lattice metamaterials, an efficient rapid digital modeling method was proposed. A Python script coupled with Abaqus software was utilized for the rapid modeling of truss lattice metamaterials and fast calculations about the mechanical properties of the metamaterials. Based on the calculation results, a machine learning dataset was constructed. Three types of truss lattice structures were randomly selected and additively manufactured. Quasi-static compression tests on these three lattice structures were conducted using a universal testing machine to verify the reliability of the dataset. Subsequently, an artificial neural network (ANN) was trained to rapidly predict the mechanical properties of the truss lattice metamaterials. Focusing on the load-bearing capacity, energy absorption capability, and the concurrent optimization of both, a non-dominated sorting genetic algorithm II (NSGA-Ⅱ) was employed. The well-trained ANN served as a surrogate model embedded within NSGA-II. Lattice configurations that exhibited high load-bearing capacity and superior energy absorption characteristics were generated by the optimization process. These configurations also achieved a balance between load-bearing and energy-absorption performance, facilitating the optimization design of truss lattice metamaterials. Additionally, simulation validations confirmed the reliability of the optimization outcomes, demonstrating the effectiveness of integrating ANN with evolutionary algorithms for the advanced design of metamaterials. By integrating machine learning with numerical simulations, the computational cost of optimization design was effectively reduced, offering support for the rapid performance optimization and customized design of complex lattice metamaterials.
Strut-based lattice metamaterials are a category of ultra-lightweight, load-bearing, and energy-absorbing materials with broad application prospects in fields such as impact protection, aerospace engineering, and lightweight structural design. Benefiting from their unique periodic architectures and adjustable meso-structural parameters, these materials exhibit exceptional mechanical tunability and multifunctional potential. However, due to the extensive parameter space of mesoscopic configurations and the highly nonlinear correlation between the structural geometry and the mechanical response, the optimization of mechanical performance for lattice metamaterials remains a formidable challenge. Based on the meso-structural characteristics of strut-based lattice metamaterials, an efficient rapid digital modeling method was proposed. A Python script coupled with Abaqus software was utilized for the rapid modeling of truss lattice metamaterials and fast calculations about the mechanical properties of the metamaterials. Based on the calculation results, a machine learning dataset was constructed. Three types of truss lattice structures were randomly selected and additively manufactured. Quasi-static compression tests on these three lattice structures were conducted using a universal testing machine to verify the reliability of the dataset. Subsequently, an artificial neural network (ANN) was trained to rapidly predict the mechanical properties of the truss lattice metamaterials. Focusing on the load-bearing capacity, energy absorption capability, and the concurrent optimization of both, a non-dominated sorting genetic algorithm II (NSGA-Ⅱ) was employed. The well-trained ANN served as a surrogate model embedded within NSGA-II. Lattice configurations that exhibited high load-bearing capacity and superior energy absorption characteristics were generated by the optimization process. These configurations also achieved a balance between load-bearing and energy-absorption performance, facilitating the optimization design of truss lattice metamaterials. Additionally, simulation validations confirmed the reliability of the optimization outcomes, demonstrating the effectiveness of integrating ANN with evolutionary algorithms for the advanced design of metamaterials. By integrating machine learning with numerical simulations, the computational cost of optimization design was effectively reduced, offering support for the rapid performance optimization and customized design of complex lattice metamaterials.
, Available online , doi: 10.11883/bzycj-2025-0243
Abstract:
In the field of material dynamic mechanical properties research, it is significant to obtain reliable data of materials under complex stress states. To address the challenge of achieving a stable stress ratio during combined loading, this work developed a novel device based on the electromagnetic Hopkinson bar (ESHB) platform. This device uniquely enables unilateral synchronous tension/compression-torsion combined dynamic loading. The paper detailed the device’s configuration and loading principles. The core innovation of this device is the independent generation of trapezoidal tensile/compressive and torsional stress waves. A multi-circuit pulse shaper produced tensile/compressive waves, while shear waves were generated using an electromagnetic clamp with torque storage. Crucially, a high-precision digital delay generator (DDG) ensured wave synchronization. With triggering accuracy within 0.1 μs, it controlled the arrival time difference of these distinct waves at the specimen to within 5 μs. This overcame the challenge posed by their different propagation velocities. Additionally, it described the synchronization control methodology and the wave propagation analysis essential for timing calculations. To validate the apparatus, dynamic tension-torsion experiments were conducted on CoCrFeMnNi high-entropy alloy specimens. The results show that the device is highly reliable and effective. It successfully achieved a stable stress ratio of approximately 1.7 throughout the loading duration. Furthermore, the experiments conclusively showed a key finding. Trapezoidal wave loading significantly enhances stress-ratio stability during combined dynamic loading. This improvement contrasts with the effect of traditional sinusoidal wave loading. This advancement offers a robust and controllable experimental method. It enables the study of materials’ dynamic mechanical responses under complex stress states. These states involve high-strain rates and multiaxial loading. This capability is especially valuable for aerospace, impact engineering, and materials science applications. The successful implementation of constant stress-ratio loading opens avenues for more accurate characterization of material yield criteria and failure mechanisms under dynamic multiaxial conditions.
In the field of material dynamic mechanical properties research, it is significant to obtain reliable data of materials under complex stress states. To address the challenge of achieving a stable stress ratio during combined loading, this work developed a novel device based on the electromagnetic Hopkinson bar (ESHB) platform. This device uniquely enables unilateral synchronous tension/compression-torsion combined dynamic loading. The paper detailed the device’s configuration and loading principles. The core innovation of this device is the independent generation of trapezoidal tensile/compressive and torsional stress waves. A multi-circuit pulse shaper produced tensile/compressive waves, while shear waves were generated using an electromagnetic clamp with torque storage. Crucially, a high-precision digital delay generator (DDG) ensured wave synchronization. With triggering accuracy within 0.1 μs, it controlled the arrival time difference of these distinct waves at the specimen to within 5 μs. This overcame the challenge posed by their different propagation velocities. Additionally, it described the synchronization control methodology and the wave propagation analysis essential for timing calculations. To validate the apparatus, dynamic tension-torsion experiments were conducted on CoCrFeMnNi high-entropy alloy specimens. The results show that the device is highly reliable and effective. It successfully achieved a stable stress ratio of approximately 1.7 throughout the loading duration. Furthermore, the experiments conclusively showed a key finding. Trapezoidal wave loading significantly enhances stress-ratio stability during combined dynamic loading. This improvement contrasts with the effect of traditional sinusoidal wave loading. This advancement offers a robust and controllable experimental method. It enables the study of materials’ dynamic mechanical responses under complex stress states. These states involve high-strain rates and multiaxial loading. This capability is especially valuable for aerospace, impact engineering, and materials science applications. The successful implementation of constant stress-ratio loading opens avenues for more accurate characterization of material yield criteria and failure mechanisms under dynamic multiaxial conditions.
, Available online , doi: 10.11883/bzycj-2025-0040
Abstract:
Against the backdrop of rising global terrorism and industrial accidents, research on infrastructure safety under blast impact has become critically urgent. As a pivotal approach for investigating dynamic responses and damage characteristics of materials and structures subjected to explosive loading, the equivalent blast-loading techniques, which show safe, efficient, and accurate, have emerged as both a research frontier and challenge. This review synthesizes advancements in equivalent blast-loading techniques for far-field explosion simulation, encompassing explosive-driven shock tubes, high-pressure gas-driven shock tubes, drop-weight impact testing machines, and hydraulically-actuated simulators. While each technique exhibits distinct advantages and limitations in simulating blast shockwaves, all strive to establish controlled and secure experimental environments that reproduce high-velocity air flow fields and pressure waves generated by explosions. Through comparative assessment, their performance in load replication fidelity, applicability, and operational efficiency are elucidated, alongside discussions on implementation challenges and potential. Finally, a novel blast simulation technique leveraging liquid-gas phase-transition-driven expansion is introduced and the follow-up research directions are prospected.
Against the backdrop of rising global terrorism and industrial accidents, research on infrastructure safety under blast impact has become critically urgent. As a pivotal approach for investigating dynamic responses and damage characteristics of materials and structures subjected to explosive loading, the equivalent blast-loading techniques, which show safe, efficient, and accurate, have emerged as both a research frontier and challenge. This review synthesizes advancements in equivalent blast-loading techniques for far-field explosion simulation, encompassing explosive-driven shock tubes, high-pressure gas-driven shock tubes, drop-weight impact testing machines, and hydraulically-actuated simulators. While each technique exhibits distinct advantages and limitations in simulating blast shockwaves, all strive to establish controlled and secure experimental environments that reproduce high-velocity air flow fields and pressure waves generated by explosions. Through comparative assessment, their performance in load replication fidelity, applicability, and operational efficiency are elucidated, alongside discussions on implementation challenges and potential. Finally, a novel blast simulation technique leveraging liquid-gas phase-transition-driven expansion is introduced and the follow-up research directions are prospected.
, Available online , doi: 10.11883/bzycj-2025-0282
Abstract:
Inspired by the hybrid topology design that integrates Miura origami and star-shaped honeycomb, this study proposes a novel origami metamaterial sandwich and employs machine learning to predict low-velocity impact response and perform multi-objective optimization. Through drop-weight impact experiments and finite element simulations, the dynamic mechanical response and deformation failure modes of the sandwich under low-velocity impact are systematically investigated. The results demonstrate that the origami-inspired topologies effectively transform the instantaneous complete fracture of traditional honeycombs into progressive crushing failure, thereby significantly enhancing impact resistance. Subsequently, a residual connection-enhanced deep learning model is developed, enabling rapid and precise end-to-end prediction of the complete low-velocity impact response, with computational efficiency substantially surpassing that of finite element simulations. Parameterized analysis based on this model reveals the regulatory mechanisms of key angle parameters on impact response and effective density. Particularly, angle variations induce a load redistribution phenomenon between panel tension-compression deformation and crease bending deformation, allowing the metamaterial to switch between bearing and buffering protective functions. This provides a mechanism basis for actively controlling impact response and failure modes. Furthermore, by integrating reinforcement learning and Pareto front analysis, the trained deep learning model served as a surrogate model to achieve lightweight multi-objective optimization tailored for load-bearing and impact-mitigation protection requirements. At similar effective densities, the metamaterial enables broad-range tuning of peak force, offering significant advantages for developing customized protective structures for diverse scenarios. This research not only establishes a solid foundation for creating customizable high-performance impact protection structures but also advances the field toward a new paradigm of intelligent, on-demand design.
Inspired by the hybrid topology design that integrates Miura origami and star-shaped honeycomb, this study proposes a novel origami metamaterial sandwich and employs machine learning to predict low-velocity impact response and perform multi-objective optimization. Through drop-weight impact experiments and finite element simulations, the dynamic mechanical response and deformation failure modes of the sandwich under low-velocity impact are systematically investigated. The results demonstrate that the origami-inspired topologies effectively transform the instantaneous complete fracture of traditional honeycombs into progressive crushing failure, thereby significantly enhancing impact resistance. Subsequently, a residual connection-enhanced deep learning model is developed, enabling rapid and precise end-to-end prediction of the complete low-velocity impact response, with computational efficiency substantially surpassing that of finite element simulations. Parameterized analysis based on this model reveals the regulatory mechanisms of key angle parameters on impact response and effective density. Particularly, angle variations induce a load redistribution phenomenon between panel tension-compression deformation and crease bending deformation, allowing the metamaterial to switch between bearing and buffering protective functions. This provides a mechanism basis for actively controlling impact response and failure modes. Furthermore, by integrating reinforcement learning and Pareto front analysis, the trained deep learning model served as a surrogate model to achieve lightweight multi-objective optimization tailored for load-bearing and impact-mitigation protection requirements. At similar effective densities, the metamaterial enables broad-range tuning of peak force, offering significant advantages for developing customized protective structures for diverse scenarios. This research not only establishes a solid foundation for creating customizable high-performance impact protection structures but also advances the field toward a new paradigm of intelligent, on-demand design.
, Available online , doi: 10.11883/bzycj-2025-0100
Abstract:
When a high-velocity fragment impacted the fuel tank, hydrodynamic ram occurred. The fuel spurt caused by hydrodynamic ram may result in the ignition or even explosion of the fuel tank, thus threatening the survivability of the high-value target. To study the characteristics of fuel spurt caused by the hydrodynamic ram event, an experiment of a high-velocity fragment impacting a simulated fuel tank was conducted, and the characteristics of velocity and spatial distribution of the fuel spurt were tested and analyzed. In order to quantitatively describe the initial motion velocity of the fuel spurt and the attenuation process of its movement in the air, the specific volume unit within the fuel was defined as fuel mass. The concepts of initial motion velocity v0 and dispersion velocity of the fuel mass were proposed. The process of fuel mass spurting from the penetration orifices was simplified into three stages: (1) the fuel mass was about to spurt out, (2) the fuel mass spurted from the penetration orifices; (3) the fuel mass was moving in the air and gradually became atomized. On this basis, the theoretical model of the distribution of fuel spurt was established. According to the cracks at the penetration orifices and the shape change of the material at the edge of the orifices, the value of the coefficient of discharge was classified, and the influence of the distribution of pressure in the fuel was also taken into account during the calculation. When v0≤737 m/s, the range of Cv is from 0.60 to 0.70. When 737 m/s<v0<906 m/s, Cv ranges from 0.25 to 0.55. When v0≥906 m/s, Cv ranges from 0.75 to 0.95. The research showed that the average error between the calculation results of the fuel spurt axial distance and the experimental results was less than 15%. The error between the calculation results of the corrected theoretical model of radial distance and the experimental results was about 5%. The calculated results of the theoretical model were in good agreement with the experimental results.
When a high-velocity fragment impacted the fuel tank, hydrodynamic ram occurred. The fuel spurt caused by hydrodynamic ram may result in the ignition or even explosion of the fuel tank, thus threatening the survivability of the high-value target. To study the characteristics of fuel spurt caused by the hydrodynamic ram event, an experiment of a high-velocity fragment impacting a simulated fuel tank was conducted, and the characteristics of velocity and spatial distribution of the fuel spurt were tested and analyzed. In order to quantitatively describe the initial motion velocity of the fuel spurt and the attenuation process of its movement in the air, the specific volume unit within the fuel was defined as fuel mass. The concepts of initial motion velocity v0 and dispersion velocity of the fuel mass were proposed. The process of fuel mass spurting from the penetration orifices was simplified into three stages: (1) the fuel mass was about to spurt out, (2) the fuel mass spurted from the penetration orifices; (3) the fuel mass was moving in the air and gradually became atomized. On this basis, the theoretical model of the distribution of fuel spurt was established. According to the cracks at the penetration orifices and the shape change of the material at the edge of the orifices, the value of the coefficient of discharge was classified, and the influence of the distribution of pressure in the fuel was also taken into account during the calculation. When v0≤737 m/s, the range of Cv is from 0.60 to 0.70. When 737 m/s<v0<906 m/s, Cv ranges from 0.25 to 0.55. When v0≥906 m/s, Cv ranges from 0.75 to 0.95. The research showed that the average error between the calculation results of the fuel spurt axial distance and the experimental results was less than 15%. The error between the calculation results of the corrected theoretical model of radial distance and the experimental results was about 5%. The calculated results of the theoretical model were in good agreement with the experimental results.
, Available online , doi: 10.11883/bzycj-2025-0250
Abstract:
Prefabricated building structures have been widely applied in civil engineering due to their advantages of energy conservation, environmental protection, controllable quality, and efficient construction. As the core load-bearing components of prefabricated building structures, precast reinforced concrete (PC) slabs are vulnerable to threats from gas explosions, industrial explosions, and terrorist attacks. To accurately assess the damage state of PC slabs under explosion, enhance structural blast resistance, and reduce casualties, an explosion response dataset of PC slabs was constructed. Six geometric parameters (slab thickness/length/width, steel reinforcement ratio, compressive strength of concrete, etc.) and two explosion load parameters (explosive weight and explosive distance) were selected as input features. Three machine learning algorithms (GPR, RF, and XGBoost) were used to predict the maximum displacement of PC slabs, and their prediction accuracies are compared by root mean square error, coefficient of determination, mean absolute error, scattering index, and comprehensive performance objective function. Furthermore, a damage classification evaluation model based on the support rotation angle damage criterion is proposed. The performance differences of the model under three criteria are analyzed by confusion matrix and five classification indices (accuracy, precision, recall, F1-score, and Kappa coefficient), and compared with simplified models and empirical prediction methods. The research results indicate that in terms of maximum displacement prediction for PC slabs under explosion loads, the XGBoost model demonstrates the best performance among the three machine learning models (GPR、RF and XGBoost). Specifically, the fitting degree of XGBoost is superior to those of GPR and RF models. Meanwhile, and the XGBoost shows the most outstanding comprehensive performance, with a damage recognition accuracy of 92.5%, which demonstrates its high-efficiency in identifying different damage types. The XGBoost-based damage classification evaluation model for PC slabs under explosion loads exhibits powerful performance, providing important references for structural blast resistance design and rapid post-blast damage assessment.
Prefabricated building structures have been widely applied in civil engineering due to their advantages of energy conservation, environmental protection, controllable quality, and efficient construction. As the core load-bearing components of prefabricated building structures, precast reinforced concrete (PC) slabs are vulnerable to threats from gas explosions, industrial explosions, and terrorist attacks. To accurately assess the damage state of PC slabs under explosion, enhance structural blast resistance, and reduce casualties, an explosion response dataset of PC slabs was constructed. Six geometric parameters (slab thickness/length/width, steel reinforcement ratio, compressive strength of concrete, etc.) and two explosion load parameters (explosive weight and explosive distance) were selected as input features. Three machine learning algorithms (GPR, RF, and XGBoost) were used to predict the maximum displacement of PC slabs, and their prediction accuracies are compared by root mean square error, coefficient of determination, mean absolute error, scattering index, and comprehensive performance objective function. Furthermore, a damage classification evaluation model based on the support rotation angle damage criterion is proposed. The performance differences of the model under three criteria are analyzed by confusion matrix and five classification indices (accuracy, precision, recall, F1-score, and Kappa coefficient), and compared with simplified models and empirical prediction methods. The research results indicate that in terms of maximum displacement prediction for PC slabs under explosion loads, the XGBoost model demonstrates the best performance among the three machine learning models (GPR、RF and XGBoost). Specifically, the fitting degree of XGBoost is superior to those of GPR and RF models. Meanwhile, and the XGBoost shows the most outstanding comprehensive performance, with a damage recognition accuracy of 92.5%, which demonstrates its high-efficiency in identifying different damage types. The XGBoost-based damage classification evaluation model for PC slabs under explosion loads exhibits powerful performance, providing important references for structural blast resistance design and rapid post-blast damage assessment.
, Available online , doi: 10.11883/bzycj-2025-0154
Abstract:
Gas leakage and explosion accidents pose a serious threat to public safety. A critical prerequisite for accurately predicting the explosive effects of combustible gas leakage lies in determining the concentration distribution following the leakage. To develop a real-time, full-field spatiotemporal prediction model for combustible gas leakage and diffusion, and to achieve efficient prediction of the equivalent gas cloud volume, a novel graph neural network model based on a dual-neural-network architecture and a multi-stage training strategy, named multi-stage dual graph neural network (MSDGNN), was proposed. The MSDGNN model consists of two synergistic sub-networks: (1) a concentration network (Ncon), which establishes the mapping relationship between the concentration fields of two consecutive timesteps, and (2) a volume network (Nvol), which generates the equivalent gas cloud volume at each timestep to provide a quantitative metric for explosion risk assessment. To further enhance model performance, a multi-stage progressive training strategy was developed to jointly optimize the dual networks. Experimental results demonstrate that compared with mesh-based graph network (MGN), the dual-network architecture effectively decouples the tasks of concentration field prediction and equivalent gas cloud volume prediction. This approach significantly mitigates the interference of weight factors in single-objective loss functions during the training process. The multi-stage training strategy, through stepwise parameter optimization, addresses the issue of insufficient data fitting encountered in traditional methods, significantly reducing the mean absolute percentage error\begin{document}$ {{ \varepsilon }}_{\rm{MAPE}} $\end{document} ![]()
![]()
Gas leakage and explosion accidents pose a serious threat to public safety. A critical prerequisite for accurately predicting the explosive effects of combustible gas leakage lies in determining the concentration distribution following the leakage. To develop a real-time, full-field spatiotemporal prediction model for combustible gas leakage and diffusion, and to achieve efficient prediction of the equivalent gas cloud volume, a novel graph neural network model based on a dual-neural-network architecture and a multi-stage training strategy, named multi-stage dual graph neural network (MSDGNN), was proposed. The MSDGNN model consists of two synergistic sub-networks: (1) a concentration network (Ncon), which establishes the mapping relationship between the concentration fields of two consecutive timesteps, and (2) a volume network (Nvol), which generates the equivalent gas cloud volume at each timestep to provide a quantitative metric for explosion risk assessment. To further enhance model performance, a multi-stage progressive training strategy was developed to jointly optimize the dual networks. Experimental results demonstrate that compared with mesh-based graph network (MGN), the dual-network architecture effectively decouples the tasks of concentration field prediction and equivalent gas cloud volume prediction. This approach significantly mitigates the interference of weight factors in single-objective loss functions during the training process. The multi-stage training strategy, through stepwise parameter optimization, addresses the issue of insufficient data fitting encountered in traditional methods, significantly reducing the mean absolute percentage error
, Available online , doi: 10.11883/bzycj-2025-0134
Abstract:
To explore the anti-penetration abilities of irregular structures made of high-strength alloy steel, a target enhanced with ultra-high-strength spherical structures (UHS-SS) was manufactured in this work. The UHS-SS is fabricated from ultra-high-strength steel (UHSS) and mechanically anchored to the target via threaded high-tensile rods, ensuring structural integrity under projectile penetration loading. A series of penetration tests at an impact velocity of 400 m/s was performed using a 125 mm diameter cannon. The yaw-induced projectile deflection was recorded at5000 s−1, and the failure mode and penetration depth of the projectile were obtained. Through a comparative analysis of anti-penetration experimental results between semi-infinite concrete targets and UHS-SS-reinforced targets, the influences of ultra-high mechanical performances and the spherical yaw-inducing structure on the deflection and fragmentation of the projectile were disclosed. The test results reveal that at a penetration velocity of 400 m/s, the dimensionless penetration depth of the UHS-SS target is 0.11, and the penetration resistance of the UHS-SS target is about 9 times that of C40 concrete. The anti-penetration performance of UHS-SS is significantly enhanced in comparison to that of the ordinary concrete target. Furthermore, as the projectile penetrates the UHS-SS target, the resultant force on the projectile is in a different direction from that of the projectile velocity, which can deflect and shatter the projectile. The behavior of ricocheting off the surface, deflection-induced secondary impact, and fragmentation of the projectile occurred during the anti-penetration test of the UHS-SS target, and the maximal deflection angle was 83º during the experiment, preventing the projectile from penetrating the interior of the protective structure. The UHS-SS target has a severe erosion effect on the projectile at a lower speed of 400m/s, which resulted in a mass loss rate of 23.66% in the experiment. Therefore, the risk of a ground-penetrating weapon penetrating the protective works and detonating is significantly reduced．
To explore the anti-penetration abilities of irregular structures made of high-strength alloy steel, a target enhanced with ultra-high-strength spherical structures (UHS-SS) was manufactured in this work. The UHS-SS is fabricated from ultra-high-strength steel (UHSS) and mechanically anchored to the target via threaded high-tensile rods, ensuring structural integrity under projectile penetration loading. A series of penetration tests at an impact velocity of 400 m/s was performed using a 125 mm diameter cannon. The yaw-induced projectile deflection was recorded at
