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  • WANG Yongli, YANG Zerui, ZHU Yi, ZHANG Yongliang
    Journal of Army Engineering University of PLA. 2025, 4(3): 41-48. https://doi.org/10.12018/j.issn.2097-0730.20250120002
    To address urban traffic congestion and the high demands for traffic collaboration, this study proposes a communication cooperation model-multi-agent deep deterministic policy gradient (CCM-MADDPG) algorithm based on a communication cooperation module (CCM). The core of this algorithm lies in the design of the CCM, which explores the deep-level relationships among neighboring agents through an embedding layer and an information extraction module. It dynamically weights and fuses neighbor information with an attention mechanism to avoid redundancy and information averaging. By embedding the CCM into the Actor network, agents integrate neighborhood information for decision-making, leveraging the locality principle to alleviate joint action space challenges while enhancing system stability and collaborative efficiency. Experiments demonstrate that the CCM-MADDPG significantly outperforms baseline models, such as Qmix and FRAP, on both synthetic and real-world dataset, exhibiting exceptional scalability and adaptability in complex road networks. Specifically, in the Manhattan road network, it reduces average delay by approximately 28.8% and 26.3% compared with Qmix and FRAP, respectively. The ablation studies confirm the necessity of CCM's information extraction and attention mechanisms. This algorithm provides an efficient solution for multi-agent collaborative control, with dual potential for urban traffic optimization and applications. Its core contribution resides in the successful integration of the CCM design with the MADDPG framework.
  • LIU Haiyue, WU Tianlong, MAO Zisen
    Journal of Army Engineering University of PLA. 2025, 4(4): 80-87. https://doi.org/10.12018/j.issn.2097-0730.20250125001
    To address the limitations of the existing methods in capturing the complex spatiotemporal features of traffic flow, this study proposes an improved Transformer long short-term memory (LSTM) fusion model aimed at enhancing the prediction accuracy of highway traffic flow. By combining the long-term dependency modeling capability of the LSTM with the global self-attention mechanism of the Transformer, this model utilizes a gated residual network to filter key features, dynamic positional encoding to enhance temporal perception, and a masking mechanism to optimize multi-head attention computation, thereby effectively reducing the model complexity. Based on monitoring data from the Changshen Expressway, the comparative experiments were conducted against baseline models including HA, ARIMA, LSTM, GRN, traditional Transformer, and GRN-Transformer. The results show that, across the 6-step, 12-step, and 24-step prediction tasks, the proposed model achieves significantly lower mean absolute error (MAE) and root mean square error (RMSE) than all the other models. Specifically, for the 24-step prediction, the MAE is reduced by 7.7% compared with the Transformer model, and the RMSE is decreased by 7.2% relative to the GRN-Transformer model, validating the model's superiority in capturing long-term spatiotemporal dependencies and dynamic features. The proposed approach provides a high-precision solution for traffic flow prediction in intelligent transportation systems and can support real-time traffic management and decision-making optimization.
  • SHEN Yan'an, FAN Guobing, ZENG Chengyi
    Journal of Army Engineering University of PLA. 2025, 4(3): 49-56. https://doi.org/10.12018/j.issn.2097-0730.20250116001
    In the context of modern military operations, the network collaboration capability of distributed unmanned combat clusters is of vital importance. Focusing on such cluster collaborative networks, an inductive graph reinforcement learning framework is established, and a heterogeneous network disintegration method integrating inductive graph representation learning and deep reinforcement learning techniques is proposed. The inductive graph representation learning empowers the method with the ability to rapidly generate disintegration effects on the strategies of emerging networks, and by modeling network disintegration as a Markov decision process and combining it with deep reinforcement learning for solving, the disintegration efficacy is significantly improved while reducing the complexity of the problem. Experiments show that the inductive approach is able to transfer among networks of different scales and achieves a better balance between disintegration effectiveness and time consumption; especially in scenarios where node-attack cost constraints are introduced, it exhibits excellent disintegration efficacy and strategy scalability. This research has significant military application value in reducing the complexity of battlefield cognition and improving the timeliness of dynamic decision-making.
  • SUN Feiyan, HAO Wenning, QU Aiyan, JIN Dawei, CHENG Kai
    Journal of Army Engineering University of PLA. 2025, 4(4): 71-79. https://doi.org/10.12018/j.issn.2097-0730.20250108002
    To address the issues of incomplete representation of temporal and spatial features, neglect of inference completion for unknown nodes, and lack of uncertainty estimation, a spatio-temporal data imputation method with uncertainty-aware capabilities, named the graph Transformer neural process (GraphformerNP), is proposed. This method utilizes a local graph convolutional neural network (GCN) and Transformer to learn the joint deterministic representation of spatial and temporal features. By incorporating a neural process, it learns latent variables for missing locations through latent state transitions, enabling the completion of missing values and providing uncertainty estimation. Adequate spatiotemporal feature representation improves the accuracy of imputation, inference completion of unknown nodes compensates for the problem of sparse sensor deployment, and uncertainty estimation enhances the reliability of deployment decisions in real-world critical applications. The experiments on multiple datasets have verified the accuracy and effectiveness of the method, and provided valuable reliability references for uncertainty estimation.
  • WANG Canlin, WANG Junhui, LI Chao, ZHANG Bailin, ZHANG Runxu, XING Haozhe
    Journal of Army Engineering University of PLA. 2025, 4(4): 30-35. https://doi.org/10.12018/j.issn.2097-0730.20250127001
    To address the current challenges of low efficiency in blasting crater expansion and small cavity formation in hard rock, a combined-hole millisecond delay blasting method for cavity formation by hole enlargement is proposed. By drilling a central main hole and surrounding holes, and by rationally designing the charge and initiation sequence, a cubic-meter-level cavity can be formed in hard rock with a single blast. The numerical simulations were used to analyze the blasting damage evolution process and cavity expansion effects for the schemes with 4 and 6 surrounding holes. The feasibility of the technical solution was then verified through field blasting tests. The results of the field tests show that the actual cavity volumes after blasting for the 4-hole and 6-hole schemes were 0.722 m3 and 1.010 m3, respectively, which are slightly smaller than the numerical simulation results. Nevertheless, it is demonstrated that large-volume, cubic-meter-level cavities can be efficiently formed in hard rock using this combined-hole millisecond delay blasting technology. The proposed technology can provide technical support for the rapid creation of cubic-meter-level cavities in hard rock.
  • WU Xuezhen, ZHAO Mingzhu, YE Qing, WANG Gang, FAN Pengxian, JIANG Haiming
    Journal of Army Engineering University of PLA. 2025, 4(4): 14-21. https://doi.org/10.12018/j.issn.2097-0730.20250225001
    Deep transportation tunnels and coal mine roadways in high geostress and complex geological environments are prone to engineering disasters such as large deformation of surrounding rock, coal bump and rockburst. The traditional cable with insufficient deformation capacity and limited impact resistance cannot adapt to the nonlinear large deformation damage characteristics of the deep buried surrounding rocks. A new type of energy-absorbing anchor cable with a shrinkable tube structure is proposed. This design leverages the plastic deformation energy absorption properties of steel to allow for greater deformation while providing high-strength, stable, and pressure-yielding bearing capacity. To explore its impact resistance characteristics, a series of drop hammer impact tests were conducted on the core pressure-yielding component of the anchor cable under varying impact heights, drop hammer weights, and numbers of impacts. The test results show that the shrinkable-tube energy-absorbing anchor cable can absorb energy up to 103 kJ in a single impact, and the working resistance is basically stabilized between 240~280 kN, which has good impact resistance performance. A field application test was carried out in the roadway of 8302 mining face in Xinjulong coal mine. The results indicate that the shrinkable-tube energy-absorbing anchor cable can achieve 120 mm of pressure-yielding deformation without fracturing. This research provides a new technical approach for controlling large deformation and impact disasters in deep rock masses.
  • LIU Chang, WANG Weixi, WANG Zhiteng, JI Cunxiao, XU Jiwei
    Journal of Army Engineering University of PLA. 2025, 4(3): 57-64. https://doi.org/10.12018/j.issn.2097-0730.20250125003
    In the modern battlefield, airborne multifunctional radars are capable of flexibly switching operating modes according to combat requirements, performing various tasks such as airspace scanning and target tracking. The dynamic switches of the operating modes not only serve as a critical basis for assessing threat levels but also pose challenges for electronic reconnaissance. To enhance the recognition efficiency of the operating modes of airborne multifunctional radars, this paper proposes a recognition algorithm based on integrated deep learning. The algorithm integrates multiple deep learning models and dynamically adjusts their weights based on the prediction accuracy of each model. On the premise of ensuring optimal overall performance, the strategy of "sacrificing the minor for the major" is adopted to enhance the predictive accuracy and stability of the integrated model. The simulation results demonstrate that this integrated deep learning method based on the strategy of "sacrificing the minor for the major" significantly enhances recognition efficiency, providing an effective solution for the precise identification of operating modes of airborne multifunctional radars.
  • WANG Yu,MA Jinrong,DONG Enzhi,YAN Hao,WEN Liang
    Journal of Army Engineering University of PLA. 2025, 4(3): 65-74. https://doi.org/10.12018/j.issn.2097-0730.20241125005
    As one of the key technologies of multi-agent unmanned systems, collaborative control has long been trapped in the problem of how to solve the influence of external disturbances.This study addresses the consistency control of multi-agent unmanned systems in the presence of input disturbances and introduces an event-triggered single-network adaptive dynamic programming approach. The proposed control strategy mitigates input disturbance signals by integrating the coupling gain with the optimal solution of the system cost function. Additionally, this research establishes the event-triggered conditions for updating the control strategy and rigorously proves the stability of the consistency state error in the multi-agent nonlinear systems under the event-triggered mechanism. Furthermore, a single-network adaptive dynamic programming algorithm is developed to solve the coupled Hamilton-Jacobi-Bellman (HJB) equation. Leveraging the Lyapunov stability theory, the uniform boundedness of the neural network weight estimation error is validated.Finally, the simulation results demonstrate the effectiveness of the proposed method which provides a reference for enhancing the collaborative capabilities and efficiency of unmanned systems.
  • XIE Chen,WU Binbin,GUO Daoxing
    Journal of Army Engineering University of PLA. 2025, 4(3): 75-84. https://doi.org/10.12018/j.issn.2097-0730.20241124001
    A multi-objective path optimization problem is proposed in order to assist decision-makers to make balanced decisions based on comprehensively considering the flight energy consumption of unmanned aerial vehicles (UAVs) and the timeliness of information from sensor nodes in complex battlefield environments. By tightly coupling multiple sub-problems, such as clustering of ground sensor nodes, optimization of the data upload sequence of nodes within the cluster, waypoint allocation of multiple UAVs, and path optimization of UAVs, a multi-objective multi-UAV clustering location-routing problem (MOMAC-LRP) optimization model that minimizes the total flight time of multiple UAVs and the age of sensor node information is constructed. Additionally, an improved non-dominated sorting genetic algorithmⅡwith task allocation design is proposed to solve this problem, and a CPLEX solver based on non-uniform aggregation weights is designed as a comparison scheme. The simulation results show that the optimized path set generated by the proposed algorithm can more reasonably balance the relationship between different indicators while having performance no lower than that of CPLEX, so it has a higher reference value, providing new ideas for improving the scientificity and effectiveness of decision making.
  • XU Tianhan, ZHANG Xiaohan, JIANG Haiming, WANG Mingyang
    Journal of Army Engineering University of PLA. 2025, 4(4): 22-29. https://doi.org/10.12018/j.issn.2097-0730.20250204001
    The penetration depth of earth-penetrating projectiles into layered protective structures is a key issue in protective engineering design. In practice, the projectile usually penetrates the target with an incidence angle, and the protective structure is typically composed of multi-layered materials. The relevant theories and empirical formulas for penetration depth are relatively complex, which are not conducive to engineering applications. Based on the motion equation of the projectile, the motion deflection law of the projectile during oblique penetration was analyzed and a safety-biased calculation formula for oblique penetration was presented. The concept of equivalent thickness was introduced to convert the equivalent thickness of multi-layered media through wave impedance, then a calculation method for the penetration depth of multi-layered media was obtained. The results show that the oblique penetration depth can be estimated by multiplying the normal penetration depth by the cosine of the angle of incidence, yielding a conservative result; the penetration depth in different media is inversely proportional to the medium's impedance. The derived formulas for oblique and layered media penetration depths are consistent in form with current specifications, elucidating the physical significance of the empirical formulas in the specifications. Compared with the oblique penetration test in reinforced concrete, the maximum relative error of the theoretical prediction is 21.6% in the range of incidence angle from 0° to 50°. Compared with the numerical simulation of penetration to layered media , the theoretical prediction of the number of penetration layers is consistent with the simulation, and the relative error of penetration depth is 3%, which verifies the applicability of the theoretical calculation method.
  • FANG Zhiyu, XIA Xiaochen, XU Kui, YE Zilyu, GENG Cunyi
    Journal of Army Engineering University of PLA. 2025, 4(3): 85-91. https://doi.org/10.12018/j.issn.2097-0730.20241103001
    To address the escalating demands for integrated low-altitude communications and sensing, this paper investigates the distributed integrated communication and sensing beamforming problem for low-altitude airspace based on a cell-free MIMO architecture. Assuming access point (AP) equipped with uniform planar array (UPA), the study optimizes distributed beamforming vectors to concurrently provide communication services to low-altitude and terrestrial users while actively sensing low-altitude targets. The distributed beamforming problem is modeled as a signal-to-noise ratio (SNR) maximization problem for sensing, subject to constraints on the communication signal-to-interference-plus-noise ratio (SINR).This problem is a non-convex optimization problem. Then, an approximate optimal solution method based on semidefinite relaxation (SDR) is proposed. The simulation results demonstrate that the proposed method can achieve a communication SINR comparable to that of the optimal beamforming design scheme. Moreover, the sensing SNR performance significantly outperforms that of prior comparative schemes designed solely for communication or sensing beams.
  • HAN Xiao, PAN Zhisong, XU Chengcheng, HUANG Yongjie
    Journal of Army Engineering University of PLA. 2025, 4(4): 61-70. https://doi.org/10.12018/j.issn.2097-0730.20250121001
    To address two major challenges in time series forecasting (TSF) research—namely, the failure to account for potential dynamic changes when modeling the intra-sequence and inter-sequence correlations, and the limited generalization ability caused by independently training multiple related forecasting tasks—this paper proposes a dynamic graph convolutional multi-task TSF (DGMTSF) model. The DGMTSF introduces multi-head attention (MHA) to learn the time-varying correlations among different time steps within the sequence in parallel and adaptively through dynamic attention weights. The graph convolutional network (GCN) is embedded in the long short term memory (LSTM), and information propagation and aggregation are carried out at the temporal state of each step, thereby effectively modeling the dynamic correlation across different sequences. The DGMTSF designs a feature weighted sharing mechanism under the multi-task learning framework. Each layer of the task subnet can perform weighted fusion of its own feature map with the features of the previous layer of all other subnets. This not only strengthens the learning of shared features among tasks but also enables flexible adjustment of the degree of feature sharing based on different task requirements, significantly enhancing the generalization ability of the model. The experimental verification on real public medical datasets shows that the DGMTSF exhibits outstanding predictive performance advantages over the baseline method.
  • FAN Pengxian, HE Lei, YANG Jianan, LI Jie, WANG Mingyang
    Journal of Army Engineering University of PLA. 2025, 4(4): 1-13. https://doi.org/10.12018/j.issn.2097-0730.20250304001
    The mechanical behavior of structural planes at various scales within a rock mass determines the stability of the surrounding rock in engineering projects. With the continuous increase in the burial depth of underground engineering projects, the threat posed by time-delay engineering disasters under disturbed environments is becoming more and more prominent. Based on a systematic review of research from field observations, laboratory experiments, and theoretical models, it is concluded that the remote induction of tectonically controlled time-delayed rockbursts and engineering seismic events is essentially the delayed instability of rock mass structural planes triggered by disturbances.A new concept termed the disturbance aftereffect of shear creep on structural planes is proposed. This concept refers to the phenomenon that, when a structural plane undergoing shear creep is subjected to a disturbance, the influence of the disturbance persists even after the disturbance has ceased. As a result, the subsequent creep process of the disturbed structural plane exhibits different states and nonlinear behaviors compared to its undisturbed condition. Based on the typical manifestations of the disturbance aftereffect, the critical transition behavior of shear creep in structural planes and its influencing factors are explored. In addition, this study highlights that the scientific characterization of disturbance intensity, the conditions and mechanisms of delayed instability critical transition, theoretical models of the critical transition process, and cross-scale research of rock-structural plane-rock mass systems are key issues that urgently need to be resolved. The solution of these problems will provide a solid foundation for understanding the mechanisms of remote triggering of earthquakes and the effective prevention of time-delay engineering disasters.
  • HUANG Shilong, ZHENG Xueqiang, CHENG Yunpeng, XU Yuhua, CHEN Juan, ZHOU Xin
    Journal of Army Engineering University of PLA. 2025, 4(4): 36-45. https://doi.org/10.12018/j.issn.2097-0730.20250109002
    With the continuous expansion of China's activities in polar scientific research, resource exploration and development, and polar shipping, the demand for polar communications continues to grow. The shortwave communication, capable of achieving long-distance transmission over thousands of kilometers through ionospheric reflection, features flexible deployment and strong survivability, making it one of the crucial communication means in polar regions. This paper first conducts a detailed analysis of the current research status of the unique ionospheric characteristics in polar regions and ionospheric models. It then reviews the research status of polar shortwave communications across four key areas: shortwave channel modeling, spectrum sensing prediction and channel detection and link establishment, communication waveform design, and ultra-long-distance link design. After that, this study summarizes four major challenges in polar shortwave communications: difficulties in channel modeling, frequency determination, waveform design, and technical verification. Finally, it proposes future research directions of polar shortwave communications from multiple perspectives, including enhanced ionospheric oblique sounding, data-driven frequency prediction, channel detection and communication waveform design, and AI-enabled shortwave communication technologies.
  • LI Haoming, SONG Fei, FENG Zhibin, XU Yifan, AO Liang, ZHONG Tianyao
    Journal of Army Engineering University of PLA. 2025, 4(5): 44-51. https://doi.org/10.12018/j.issn.2097-0730.20250223003
    Mobile jamming causes rapid and dynamic fluctuations in jamming signals, significantly complicating the task of efficient anti-jamming communication under channel resource constraints. Conventional single-slot "sensing-decision" approaches not only incur substantial time costs but also tend to concentrate multiple user pairs on the same jamming-free channels, thereby wasting the resources of the jammed channels. To overcome these limitations, a multi-slot channel trading mechanism driven by periodic bidding is designed. This mechanism operates by observing the jamming environment, collecting users' bids, and making a single allocation decision per cycle. Given the hierarchical and periodic nature of users' interactions, a system is modeled with the multi-stage Stackelberg game framework and a periodic bidding-driven anti-jamming channel selection algorithm is proposed. The simulation results demonstrate that the proposed trading mechanism achieves a significant utility improvement and exhibits strong robustness in multi-user scenarios, confirming its effectiveness in countering mobile jammers.
  • WANG Weiwen, ZOU Xia, ZHOU Haotian, LI Yihao
    Journal of Army Engineering University of PLA. 2025, 4(5): 60-67. https://doi.org/10.12018/j.issn.2097-0730.20250211001
    Multi-model combinations leverage the complementary nature of features extracted by different deep learning models to enhance the performance of automatic modulation recognition (AMR). However, a systematic analysis and comparison of these composite models is still lacking. This paper designs six serial composite models based on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Transformer. A comprehensive comparative analysis is conducted based on the RML2016.10a and RML2018 datasets, focusing on recognition accuracy, model complexity, noise robustness, and performance disparities across different modulation categories. The experimental results demonstrate that on the RML2016.10a dataset, the CNNTransformer (CT) dual-model combination achieves an average recognition accuracy of 60.7%, which is 0.8 percentage higher than the CNNLSTMTransformer (CTL) triple-model combination, while reducing the number of parameters by 54.7%. Moreover, for signal-to-noise ratios (SNR) greater than 10 dB, the accuracy variance is reduced by 35.3%. On the RML2018 dataset, the CNNLSTM (CL) dual-model combination achieves comparable performance to CTL with only 32.9% of the parameters. Further analysis reveals that the concurrent use of LSTM and Transformer introduces temporal redundancy; that positioning temporal modules at the forefront makes the model prone to extracting noise-corrupted features; and that local feature modeling is critical for accurately recognizing higher-order representations and phase-sensitive modulations. The findings of this study establish a quantitative foundation for optimizing the lightweight design and robustness of composite AMR models.
  • YANG Ning, ZHANG Bangning, GUO Daoxing
    Journal of Army Engineering University of PLA. 2025, 4(4): 46-52. https://doi.org/10.12018/j.issn.2097-0730.20250102001
    In open and dynamic environments, as new emitters continuously emerge, identification algorithms need to continually learn new features while maintaining the ability to recognize previously learned ones. However, in non-cooperative scenarios, the scarcity of newly added emitter samples poses a challenge to existing deep learning-based methods, especially in few-shot situations where insufficient generalization capability and catastrophic forgetting are commonly observed. This paper proposes a meta-learning-based few-shot class-incremental specific emitter identification (SEI)method. The proposed method replaces traditional offline training with a pseudo class-incremental training paradigm and incorporates a meta-learning strategy to optimize the parameter update mechanism, enabling the model to rapidly adapt and expand its identification capacity even with very limited new samples. Meanwhile, sample replay and knowledge distillation strategies are introduced to improve the loss function, effectively mitigating the problem of historical knowledge forgetting. The experimental results demonstrate that on the public ADS-B dataset, the proposed method maintains a identification accuracy of 94.25% after one round of incremental learning, verifying its effectiveness in few-shot incremental learning scenarios.
  • FENG Lei, GU Chuan, WANG Heng, GUO Daoxing
    Journal of Army Engineering University of PLA. 2025, 4(5): 52-59. https://doi.org/10.12018/j.issn.2097-0730.20250217001
    To resolve the conflict between path safety and strike timeliness arising from the decoupled design of threat assessment and trajectory planning in multi-target strike missions, this paper proposes a UAV trajectory planning algorithm based on dynamic threat assessment. A dynamic threat assessment model is first developed by integrating three-dimensional spatial distance and weighted target attributes, which quantifies battlefield threats to model the complex environment. Subsequently, a two-stage planning framework combining improved Affinity Propagation (AP) clustering and a Genetic Algorithm (GA) is introduced. This framework utilizes a threat-weighted similarity matrix and adaptive damping factors to dynamically generate clusters of strike points. An optimal flight trajectory that satisfies the threat constraints is then generated by the GA. This approach achieves the collaborative optimization of threat assessment, target clustering, and trajectory planning. The simulation results demonstrate that the proposed algorithm significantly reduces both the total threat exposure and the peak threat level in complex environments compared with conventional methods, while also maintaining lower computational complexity. This validates the effectiveness of the deep coupling between threat assessment and planning.
  • YE Zilyu, XU Kui, ZHANG Beihua, ZHOU Tao, ZENG Mingcong, FANG Zhiyu
    Journal of Army Engineering University of PLA. 2025, 4(4): 53-60. https://doi.org/10.12018/j.issn.2097-0730.20250120003
    With the surge of wireless devices and the escalation of malicious interference in the 6G era, the traditional reconfigurable intelligent surfaces (RIS) with fixed elements and half-space coverage can no longer guarantee reliable anti-jamming performance across indoor-outdoor scenarios. To address this limitation, a wireless anti-jamming transmission method assisted by a movable elements-based simultaneous transmitting and reflecting reconfigurable intelligent surface (ME-STAR-RIS) is proposed. By jointly optimizing the active beamforming and transmit power at the base station and the flexible passive beamforming (including element positions and phase shifts) at the ME-STAR-RIS, the scheme aims to maximize the anti-jamming communication rate. To tackle the high-dimensional non-convex joint optimization problem inherent in the ME-STAR-RIS-assisted anti-jamming transmission design, the optimization problem is formulated as a Markov decision process (MDP) and solved using a double deep Q network (DDQN) algorithm, yielding optimized transmit power and flexible passive beamforming. The simulation results demonstrate that the proposed anti-jamming transmission method assisted by ME-STAR-RIS significantly enhances the system's anti-jamming performance, compared with the traditional approaches.
  • YANG Xingyu, WANG Zhonghua, ZHAO Shiwei, LIU Yang, SHA Jin, MENG Xianlei
    Journal of Army Engineering University of PLA. 2025, 4(5): 68-72. https://doi.org/10.12018/j.issn.2097-0730.20250410002
    Airborne light detection and ranging (LiDAR) is widely used in the acquisition and analysis of transmission line point clouds, demonstrating high accuracy and efficiency in complex environments. However, conventional methods for extracting power line points still suffer from heavy reliance on manual intervention, limited automation, and low efficiency. To address these issues, this paper investigates the spatial features of power line point clouds through multi-dimensional analysis. An adaptive elevation threshold method is first introduced to segment ground and non-ground points. Subsequently, an automated extraction method is proposed by integrating the particle swarm optimization (PSO) algorithm with a density-based spatial clustering of applications with noise (DBSCAN). The experimental validation across multiple scenarios demonstrates that the proposed method significantly enhances both the automation level and efficiency of power line extraction. It meets the practical demands for efficient inspection and automated operation in power systems, thereby providing technical support and experimental evidence for advancing automated power line point cloud extraction.
  • LIU Qiang, ZHANG Yuwei, YANG Li, ZHANG Sheng, LUO Xiannan
    Journal of Army Engineering University of PLA. 2025, 4(4): 88-97. https://doi.org/10.12018/j.issn.2097-0730.20250109001
    Ground penetrating radar (GPR), as an efficient and rapid non-destructive detection technology, is often constrained by environmental noise interference and difficulties in image interpretation during shallow subsurface detection. Particularly in the detection of landmines and explosive remnants of war (ERW) , the false alarm rate remains high. To address these issues, this paper reviews advances in shallow-depth GPR technology focusing on the four critical dimensions: hardware architectures, soil environment, landmine/ERW detection, and signal processing techniques. The technical analyses and experimental investigations were conducted on prevailing shallow subsurface detection challenges, with a special emphasis on exploring the application of deep learning techniques in GPR signal clutter suppression and target recognition. This work provides valuable insights for improving GPR detection efficiency and effectively reducing the false alarm rate, and is expected to offer all-weather technical support for the rapid clearance of unexploded ordnance and military engineering surveys.
  • ZHAO Zhixin, CHEN Jie, XIN Bin, LI Li, DING Yulong, ZHENG Yifan
    Journal of Army Engineering University of PLA. 2025, 4(5): 1-10. https://doi.org/10.12018/j.issn.2097-0730.20250209001

    To address the issue of the quadratic growth in simulation time with increasing numbers of Unmanned Aerial Vehicles (UAVs) for multi-UAV combat mission simulations on Central Processing Units (CPUs), a lightweight, GPU-parallel accelerated simulation environment for multi-UAV combat is designed and developed. Unlike existing simulations frameworks that are often limited to single-UAV missions or only parallelize multi-UAV dynamics, the proposed framework, focusing on multi-UAV adversarial scenarios, further implements parallel computation for the relative poses and damage relationships of each UAV within the environment. In response to the limitation that traditional Basic Fighter Maneuvers (BFM) are only well-defined in level flight, an improved set of fully-attitude interpretable basic aerial combat maneuvers is designed. The simulation environment includes a variety of scalable multi-UAV confrontation tasks, which can provide heuristic baseline strategies for various tasks and support proximal policy optimization-based baseline strategies for one-on-one confrontation scenarios. Compared with the multi-threaded simulations running on an 8-core, the 16-thread CPU, the proposed platform improves sampling speed by two to three orders of magnitude. The reinforcement learning training results in both CPU and GPU parallel environments demonstrate that the GPU-accelerated simulation can reduce training time to 1/50, without significantly compromising sampling efficiency due to trajectory fragmentation. The proposed parallel-accelerated multi-UAV combat simulation environment significantly enhances sampling and training speeds, thereby facilitating accelerated research progress in this field.

  • ZHANG Xianfeng, LIU Chuang, GONG Ping, TAN Mengting
    Journal of Army Engineering University of PLA. 2025, 4(3): 10-16. https://doi.org/10.12018/j.issn.2097-0730.20250109004
    To investigate the distribution pattern of shock waves in relation to soil damage characteristics under the action of air shock waves generated by ground explosions of TNT charges, static ground explosion tests with 10 kg and 20 kg TNT charges were carried out, and the characteristics of air shock wave overpressure distribution after the explosion of charge were obtained. A simulation model for the pressure field of ground explosion shock waves was established, and its reliability was verified with experimental results.The research findings indicate that a frustum-shaped crater is formed on the soil surface after the charge explosion . The increase in the explosive charge from 10 kg to 20 kg results in an increase in the diameter of the crater from 1.5 m to 1.9 m, an increase in the air shockwave at 5 m from 0.186 MPa to 0.366 7 MPa, and an increase in the reflective pressure on the surface of the wall at 5 m from 0.604 MPa to 1.485 MPa. The shape of the explosive charge has a significant impact on the near-field pressure distribution. The ground reflection effect substantially enhances the pressure values in the near-ground pressure field, with the pressure in the near zone exhibiting a convex profile that gradually degenerates into an ellipsoidal shape at a distance of 5 m from the explosion center. The wall reflection pressure of the shock wave decreases with height, following a quadratic function attenuation pattern.
  • LAI Jizhou, SHI Yuxing, LI Jiong, CHEN Yuxuan, LYU Pin
    Journal of Army Engineering University of PLA. 2025, 4(5): 11-20. https://doi.org/10.12018/j.issn.2097-0730.20250427001
    This paper addresses the challenges in collaborative Simultaneous Localization and Mapping (SLAM) for heterogeneous robot teams in global-navigation-satellite-system-denied environments, including pose alignment errors, communication constraints, and accumulated estimation drift. A multi-stage cooperative SLAM architecture is proposed to overcome these issues. The solution features a two-stage relocalization mechanism that employs Scan Context for coarse alignment and the Generalized Iterative Closest Point (GICP) algorithm for fine registration, enhancing initial pose estimation accuracy across heterogeneous platforms. A lightweight communication framework leveraging high-speed network-address-translation traversal technology is developed, which utilizes point cloud voxel filtering and keypoint extraction for data compression, ensuring real-time transmission in low-bandwidth conditions. Furthermore,the Random Sample Consensus (RANSAC) algorithm is incorporated to optimize multi-robot pose transformations by eliminating outliers, thereby improving global consistency. Multi-source point cloud fusion is subsequently achieved through coordinate transformation.The extensive experiments in underground parking garages and utility tunnels demonstrate the system's robustness and real-time performance, successfully generating complete point cloud maps with relocalization times under 1.58 s, RANSAC optimization under 3.2 ms, and map merging under 1.8 ms. The quantitative results confirm that the collaborative mapping accuracy is improved by over 45% compared with single-robot SLAM, and the integration of RANSAC further boosts relocalization accuracy by more than 15%, significantly enhancing the overall mapping precision. The proposed method offers a valuable reference for multi-robot collaborative perception and navigation.
  • RONG Chuanzhen, WANG Huali, FAN Hongbin, NI Xue, YU Jing
    Journal of Army Engineering University of PLA. 2025, 4(5): 21-27. https://doi.org/10.12018/j.issn.2097-0730.20250324003
    Change detection by multimodal remote sensing images faces the problem of significant geometric and radiometric differences caused by their distinct imaging mechanisms. This makes direct comparison of images from different sources infeasible for change detection tasks. This paper proposes a novel change detection framework that incorporates scene structure priors by integrating graph learning with superpixel segmentation. To reduce computational complexity, the image is first over-segmented into superpixels using a Gaussian mixture model to characterize the structural information. Graph representations of the bi-temporal images are then constructed based on graph signal smoothing. By further integrating the prior knowledge of structural consistency, a unified multimodal change detection framework is developed. The extensive experiments on multimodal remote sensing datasets validate the efficacy of the proposed method in enhancing both detection efficiency and accuracy.
  • YAO Kui, CHEN Xiaohang, HE Ming, XUE Chenlong, LIU Kefeng, WANG Yangjun
    Journal of Army Engineering University of PLA. 2025, 4(5): 73-79. https://doi.org/10.12018/j.issn.2097-0730.20250410001
    The bionic statistical network algorithm inspired by the layered structure of starling flocks offers theoretical underpinnings for the dynamic simulation and intelligent control of large-scale unmanned aerial vehicle (UAV) swarms. Beginning with a precise quantification of biological characteristics, an indicator system for individual starlings in a flock is established, encompassing five primary categories: the motor system function index, the cardiopulmonary metabolic composite index, the environmental perception composite index, the neural perception composite index, and the cognitive adaptability index. A bionic organizational model for flock formations is then constructed based on a Bayesian network, which facilitates hierarchical construction, formation adjustment, communication, and command control, thereby accurately characterizing the organizational and bionic control architecture of both starling flocks and corresponding UAV swarms. The performance of the proposed model is verified through simulations, data analysis, and visual demonstrations. The results indicate that this approach ensures effective collective formation control, providing a novel strategy for the bionic design and control of large-scale unmanned swarms.
  • GAO Lei,SONG Yuhang,Xie Xingkun,BAI Linyue
    Journal of Army Engineering University of PLA. 2025, 4(5): 97-104. https://doi.org/10.12018/j.issn.2097-0730.20250419001
    This study investigates the global stability of 7075-T6 aluminum alloy I-beams. The flexural-torsional buckling tests were first conducted on three simply-supported beams with doubly symmetric I-sections. A finite element (FE) model was then developed and validated against the experimental results. With the validated model, the influence of the normalized slenderness ratio on the global stability was examined. The results indicate that for every 10% increase in the normalized slenderness ratio, the stability coefficient decreases by an average of 11.3%. Furthermore, a comparison was made between the test/FE results and the stability coefficients calculated from various design codes, including the Chinese (GB 50429—2007), European, and American standards. The analysis reveals that the Chinese and European codes provide overly conservative designs for members with a normalized slenderness ratio below 1.5. In contrast, the American code exhibits significant discrepancies for members with a slenderness ratio under 0.9, but its calculations become considerably more accurate for ratios above this value. These findings provide a valuable reference for future revisions of design codes concerning the stability calculation of high-strength aluminum alloy beams.
  • CHENG Yangfan, LIANG Haojian, SUN Tianyu, GUO Qingqing, SUN Kuankuan, LI Xiang
    Journal of Army Engineering University of PLA. 2025, 4(3): 17-24. https://doi.org/10.12018/j.issn.2097-0730.20250102003
    To solve the problem of low reaction rate and incomplete reaction of metal fuels, a core-shell structured composite energetic material, AP@TiH2, consisting of ammonium perchlorate (AP) particles coated with titanium hydride (TiH2) powder, was prepared by the solvent-nonsolvent method. The microstructure distribution and ignition combustion performance of this composite material were studied by characterization technique and laser ignition experiments. The results show that Ti is uniformly attached to the surface of massive ammonium perchlorate particles, and the AP@TiH2 composite material has brighter flame, larger reaction area, shorter combustion time and faster reaction rate. The addition of AP@TiH2 to solid propellants can effectively improve the ignition characteristics of the propellants, and improve the combustion performance. Compared with the traditional mechanical mixing methods, the temperature rise rate of AP@TiH2/HTPB propellant prepared by the solvent-nonsolvent method is faster, the average temperature of stable combustion stage is higher, the ignition delay time is reduced by about 10%, and the linear combustion rate is increased by about 11%. The coating structure reduced the mass and heat transfer distance between the fuel and oxidizer, and effectively improved the reaction rate and reaction completeness of the fuel. This fine control method is expected to provide a new way for the effective utilization of solid propellant energy.
  • MAO Wensheng , XIE Xianqi , JI Chong , XIE Xingbo
    Journal of Army Engineering University of PLA. 2025, 4(3): 1-9. https://doi.org/10.12018/j.issn.2097-0730.20241224005
    To investigate the influence mechanism of liquid filling ratio on the blast resistance of liquid-filled cylindrical shells, this study conducts a systematic examination of the dynamic response of partially filled cylindrical shell structures under near-field underwater explosions through integrated experimental and numerical simulation approaches. The results reveal that the structural response demonstrates three distinct phases: shock wave action stage, stable stage, and bubble pulsation action stage, with the primary deformation occurring during the bubble pulsation action stage. A significant positive correlation exists between liquid filling ratio and blast resistance, evidenced by a 52.7% reduction in central deflection when the filling ratio increases from 0% to 95%. While the internal peak pressure during the shockwave phase increases progressively with elevated filling ratios, the maximum internal pressure during bubble pulsation occurs at the 90% filling ratio. The plasticity of cylindrical shells under explosive loading decreases proportionally with increasing filling ratio, exhibiting reduction trends consistent with central deflection. These findings provide theoretical foundations and engineering insights for blast-resistant design and damage assessment of underwater protective structures.
  • DUAN Zhaoxia, XU Zhen, SHAO Zhen
    Journal of Army Engineering University of PLA. 2025, 4(3): 98-104. https://doi.org/10.12018/j.issn.2097-0730.20250112001
    At present, most of the manned and unmanned maritime forces rely on subjective decision-making for organization and deployment. In response to the issue of limited and relatively weak systematic modeling and optimization applications for force allocation, this study, starting from the operational and tactical scales and based on the PREA (planning, readiness, execution, and assessment) loop operational theory, comprehensively considers the characteristics of both manned and unmanned maritime forces, and proposes a methodology for constructing a force allocation model for collaborative command of manned and unmanned forces, primarily from three aspects: operational resource modeling, operational requirement analysis, and operational capability employment. By analyzing typical maritime combat simulation cases and employing the multi-objective quantum particle swarm optimization algorithm for rapid model optimization and iteration, this study provides operational resource allocation schemes that align with practical application scenarios. The results demonstrate that the proposed model exhibits good practicality and can serve as a reference for combat force deployment (CFD).
  • YANG Sen,SU Lijun,LIANG Zipeng,GU Zeyang,LI Kehua,ZUO Xiaotong
    Journal of Army Engineering University of PLA. 2025, 4(3): 92-97. https://doi.org/10.12018/j.issn.2097-0730.20250125002
    The traditional methods for evaluating the effectiveness of manned-unmanned collaborative combat systems suffer from issues such as over-reliance on single indicators, insufficient objectivity and accuracy, non-intuitive evaluation results, and low credibility. These limitations make it challenging to comprehensively reflect the complex interconnected relationships among various elements within the manned-unmanned combat system and address the uncertainties inherent in the combat process. In order to solve these problems, this paper puts forward a method for evaluating the effectiveness of manned-unmanned collaborative combat based on the Bayesian network. Based on an analysis of effectiveness evaluation criteria, an evaluation index system for manned-unmanned collaborative combat effectiveness is constructed, targeting six types of typical collaborative combat missions. Furthermore, a Bayesian network model is established to assess the effectiveness of manned-unmanned collaborative combat. The simulation results show that this model has certain unique advantages for the effectiveness evaluation of manned-unmanned collaborative combat in uncertain battlefield environment.
  • ZHANG Jinhang, GAO Min, FANG Dan, WANG Yi, LI Chaowang, ZHOU Yulong
    Journal of Army Engineering University of PLA. 2025, 4(5): 28-36. https://doi.org/10.12018/j.issn.2097-0730.20250219001
    To address the issue of the YOLOv5s object detection algorithm with an excessive number of parameters and computational cost, which makes it difficult to deploy on small UAV platforms, a lightweight algorithm named Ghost-SimAM-YOLOv5 (GS-YOLOv5) is proposed. The proposed model replaces the standard convolutional and C3 modules with Ghost and C3Ghost modules, significantly reducing the model's number of parameters and computational complexity. Furthermore, it incorporates the SimAM attention mechanism and the MPDIoU loss function. The SimAM mechanism enhances the representation of relevant information, enabling the model to capture the critical features of targets more accurately. The MPDIoU loss facilitates efficient bounding box regression, is computationally fast, and is less susceptible to extreme cases. The experimental results on a self-built ground target dataset show that the proposed GS-YOLOv5 algorithm achieves an average precision of 94.4%, a computational cost of 8.1 GFLOP, and a model size of 3.7 MB. Compared with YOLOv5s, the proposed algorithm reduces the number of parameters and computational cost by 51.2% and 48.6%, respectively, while maintaining high detection accuracy. The algorithm proves effective for ground target detection, achieving an optimal balance between lightweight design and detection performance.
  • ZHOU Yongkang, LIU Yi, LUO Lailong, ZHU Cheng
    Journal of Army Engineering University of PLA. 2025, 4(5): 37-43. https://doi.org/10.12018/j.issn.2097-0730.20250207001
    The performance of artificial intelligence models is influenced by both the training methodology and the training data, making distributed training a current research hotspot. To address the issue of varying data quality across different nodes in distributed training, where low-quality data degrades global model performance, a method for assessing the data contribution value of each node is proposed. This method aims to screen for high-quality data, mitigate the negative impact of low-quality data, improve distributed training efficiency, and enhance model performance. The marginal utility of a client's data is defined as the difference in performance metrics of the model trained on the remaining data when that client exits the set. Based on the marginal utility of client data, an initial screening of clients is performed, and the Shapley value is calculated. By integrating a multi-index evaluation approach, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is used to aggregate marginal utility and Shapley value, thereby deriving the data contribution value of clients, which serves as the basis for a second screening. The experimental results on the MNIST dataset demonstrate that, compared with the equal-weight average aggregation method, the proposed assessment method for data contribution value improves both the accuracy and convergence speed of the global model.
  • HAO Xulong, LI Feng, LI Da
    Journal of Army Engineering University of PLA. 2025, 4(5): 88-96. https://doi.org/10.12018/j.issn.2097-0730.20250220001
    This study investigates the flexural behavior of adhesive-bonded bridge decks made from large span-to-depth ratio pultruded glass fiber reinforced polymer (GFRP) square tubes, aiming to support the development of modular, lightweight emergency bridges. The three-point bending tests were conducted and numerically simulated with two finite element (FE) models: a solid model accounting for damage evolution and a planar frame model of the directly loaded area. The results reveal that the initial damage occurs at the top flange-web junction near the edge of the directly loaded area. The ultimate load capacity is governed by local buckling of the top flange, with the final failure mode being local buckling of the web. The initiation of structural damage is controlled by localized cross-sectional deformation in the directly loaded area. Specifically, the damage in the edge tubes is caused by combined shear deformation and local transverse bending, while the damage in the central tubes is primarily due to local transverse bending of the top flange. The overall failure mechanism is dominated by mid-span bending moments, leading to successive local buckling of the top flange and web. This process is accelerated by the weakened constraints resulting from the crack initiation at the top flange-web junctions. The designed bridge deck provides a reference for emergency scenarios.
  • LIU Huali, ZHAO Yuetang, ZHU Yantao, LI Ming
    Journal of Army Engineering University of PLA. 2025, 4(6): 1-7. https://doi.org/10.12018/j .issn.2097-0730.20250605001
    Based on the mechanical principle of limit equilibrium, a method for calculating the safety factor for a symmetric three-dimensional slope without assuming the inter-column forces is proposed, and the safety factor expression considering the direction and intensity of blast-induced load is derived. The three classic examples are analyzed to quantify the influence of the direction and intensity of blast-induced load on the safety factor of the slopes. The results show that under the action of blast-induced load, the most dangerous action direction of the three-dimensional symmetric slope is consistent with the resultant force direction of the sliding force; as the pseudo-static coefficient of the blast-induced load gradually increases from 0 to 0.3, the slope safety factor gradually decreases, and the safety factors of the three examples in the most unfavorable direction decrease by up to 38.89%, 29.29%, and 42.83% respectively. The proposed method and analysis results provide a reference for the stability analysis and safety risk assessment of homogeneous slopes under weapon strike conditions.
  • MA Lei, YANG Guili, ZHONG Mingshou, MA Yiming, MAO Wensheng, MA Huayuan
    Journal of Army Engineering University of PLA. 2025, 4(3): 34-40. https://doi.org/10.12018/j.issn.2097-0730.20241108002
    As the crucial protective structure for coastal engineering works such as ports and wharves, breakwaters are typically constructed by piling up rock blocks. In response to the issues of damage, fragmentation, and cracking that occur under the high-speed impact of blasting loads, multi-condition blasting tests have been conducted to analyze the destructive effects of grouped charges with various explosive quantities at different positions on the breakwater structure. Based on the blasting test data of breakwaters, the accuracy of the material parameters in the numerical model was validated, a full-section breakwater numerical model was constructed, and the damage data of the breakwater under different charge positions, quantities, and burial depths were obtained. The research indicates both the charge quantity and detonation position significantly influence the destruction effects on the breakwater; internal charges, due to the efficient release of energy, exert a prominent destructive effect on the vertical structure of the breakwater, with a damage range being significantly larger than that in the cases of charges placed on the slope and the top. Through multi-condition numerical simulations, the revised calculation formulas for crater depth and destruction diameter were proposed. The research results provide theoretical support for the precision blasting design and damage assessment of large-scale block pile structures.
  • ZHOU Hang,GAO Xingyong,ZHEN Jianwei,YU Weibo,LIU Yangshuo,LUO Hao
    Journal of Army Engineering University of PLA. 2025, 4(3): 25-33. https://doi.org/10.12018/j.issn.2097-0730.20241208001
    Products of thermite reaction are widely used in the fields of metal demolition and ammunition destruction due to their ability to rapidly heat target plates and perform external work. To study the ablation perforation performance of aluminum/polytetrafluoroethylene/iron oxide/copper oxide (Al/PTFE/Fe2O3/CuO) fluorothermite on steel plates, the reaction performances of different combinations of thermite mixtures were comparatively analyzed. The ablation perforation test was carried out and the relationship between the combustion performance and perforation performance of the thermite mixture was discussed. The results indicate that the actual combustion temperatures of the five formulations of thermite range from 2 200 K to 2 400 K; thermite mixtures with lower Al/PTFE content exhibit stronger flames, higher combustion temperatures, and pressures; meanwhile, the presence of Al/Fe2O3 helps to lower the reaction initiation temperature; thermite formulations possessing the aforementioned characteristics demonstrate better penetration effects on steel plates; the pressure rise rate and combustion temperature of the thermite are likely the primary factors influencing the perforation performance; a lower residual carbon content can prevent the formation of high-melting-point carbides in the stagnation zone of the steel plate, which is conducive to the melting of the steel plate.
  • YANG Jianan, FAN Pengxian, LI Jie, WANG Mingyang, WU Xuezhen
    Journal of Army Engineering University of PLA. 2025, 4(6): 8-16. https://doi.org/10.12018/j .issn.2097-0730.20250514002
    Structural planes, as weak links in rock masses, often determine the overall stability of the rock mass. To reveal the triggering mechanism of structural plane instability, the mechanical and energy characteristics during the formation of sliding surfaces are analyzed. The energy relationship for the progressive instability of structural planes is established, and an energy criterion for structural plane instability under single disturbance is proposed based on a dimensionless energy factor. The main influencing factors of block stability were quantitatively analyzed with a three-block model and a simplified bilinear shear constitutive relation. The analysis revealed that when adopting the bilinear constitutive relation, the threshold of the instability energy factor can be expressed as a function of the initial state of the structural plane and the constitutive characteristics of the material deformation. Its magnitude is proportional to the square of the nominal ultimate strain and the square of the relative magnitude of the initial shear force to the ultimate resistance, while being independent of disturbance parameters. Under single disturbance, higher disturbance frequencies require greater disturbing forces to induce instability. The main conclusions derived from the energy-based theoretical analysis are consistent with the results obtained from limit equilibrium methods and the general patterns revealed by relevant experimental results. The proposed dimensionless energy factor criterion provides a new perspective for preventing engineering disasters induced by structural plane instability.
  • CHEN Ping, ZHAO Xudong, WEI Hong, ZHAO Ziwei, WU Fang, CHEN Zhilong
    Journal of Army Engineering University of PLA. 2025, 4(6): 50-55. https://doi.org/10.12018/j .issn.2097-0730.20250423001
    Urban civil air defense engineering serves as a critical means for safeguarding the lives and property of urban residents and preserving war potential during wartime. This study aims to conduct a scientific assessment of the protective effectiveness of the urban civil air defense engineering. Based on the constituent factors of the urban civil air defense engineering's overall efficacy, an evaluation index system was established around the five key dimensions: fundamental protection, peacetime-to-wartime conversion, system connectivity, spatial layout, and supporting facilities. The fuzzy comprehensive evaluation method was utilized to perform the assessment. By evaluating the protective effectiveness of three typical urban civil air defense engineering, the operability of the proposed index system and method were successfully validated. The findings of this research can serve as a valuable reference for the future planning and construction of urban civil air defense engineering.
  • ZHONG Jiahe, SONG Chunming, ZHANG Haotian, JIANG Longyu, LIU Feng
    Journal of Army Engineering University of PLA. 2025, 4(6): 25-33. https://doi.org/10.12018/j .issn.2097-0730.20250427002
    The failure modes of reinforced concrete slabs exhibit dynamic transformation characteristics influenced by factors, such as structural configuration and impact conditions,and the internal damage and failure evolution after impact are difficult to measure directly. Based on the low-velocity impact tests, the numerical simulations of impact failure under multiple working conditions were conducted with the LS-DYNA software. The dynamic responses of reinforced concrete slabs under impact, including impact force, displacement, and failure characteristics, were investigated, and the transformation of failure modes as well as the influence of key factors were analyzed. The results show that simply supported reinforced concrete slabs under low-velocity impact loads exhibit four failure modes: local impact damage, global impact failure, coupled local global failure, and punching shear failure. As the impact velocity increases, the failure mode evolves sequentially from local impact damage to global impact failure, coupled local global failure, and punching shear failure. Increasing the bottom longitudinal reinforcement ratio and slab thickness significantly enhances the global bending resistance compared to the punching shear resistance. Improving concrete strength is more beneficial for enhancing punching shear resistance, thereby inducing a transformation in the failure mode of the slab. The findings can provide references for analyzing the structural safety of reinforced concrete slabs.