Tunnels deeply buried have high crustal stress and are prone to large deformation disasters when encountering soft rock.The large deformation phenomenon during the construction process of the Maoxian Tunnel on the Che...Tunnels deeply buried have high crustal stress and are prone to large deformation disasters when encountering soft rock.The large deformation phenomenon during the construction process of the Maoxian Tunnel on the Chengdu-Lanzhou Railway is particularly evident.This article focuses on the large deformation problem of the No.1 inclined shaft of the Maoxian Tunnel,and uses on-site monitoring methods to explore the reasons for tunnel structure failure,and analyzes the mechanical behavior of the tunnel structure.By using numerical simulation methods,the effectiveness of the second-layer support in resisting creep loads in tunnels was studied,and the influence of the construction time of the secondlayer support on the mechanical properties of the tunnel was discussed.The results indicate that the first-layer support in the tunnel is a structural failure caused by asymmetric deformation caused by creep,while the second-layer support has a good effect on resisting creep loads.The research results can provide a technical reference for deformation control of squeezing tunnels.展开更多
Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and ...Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.展开更多
Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metavers...Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.展开更多
Artificial intelligence is currently achieving impressive success in all fields.However,autonomous navigation remains a major challenge for AI.Reinforcement learning is used for target navigation to simulate the inter...Artificial intelligence is currently achieving impressive success in all fields.However,autonomous navigation remains a major challenge for AI.Reinforcement learning is used for target navigation to simulate the interaction between the brain and the environment at the behavioral level,but the Artificial Neural Network trained by reinforcement learning cannot match the autonomous mobility of humans and animals.The hippocampus–striatum circuits are considered as key circuits for target navigation planning and decision-making.This paper aims to construct a bionic navigation model of reinforcement learning corresponding to the nervous system to improve the autonomous navigation performance of the robot.The ventral striatum is considered to be the behavioral evaluation region,and the hippocampal–striatum circuit constitutes the position–reward association.In this paper,a set of episode cognition and reinforcement learning system simulating the mechanism of hippocampus and ventral striatum is constructed,which is used to provide target guidance for the robot to perform autonomous tasks.Compared with traditional methods,this system reflects the high efficiency of learning and better Environmental Adaptability.Our research is an exploration of the intersection and fusion of artificial intelligence and neuroscience,which is conducive to the development of artificial intelligence and the understanding of the nervous system.展开更多
A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve humanrobot cooperation performance for a curtain wall installation task.This method breaks the traditional idea that ...A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve humanrobot cooperation performance for a curtain wall installation task.This method breaks the traditional idea that the robot is regarded as the follower or only adjusts the leader and the follower in cooperation.In this paper,a self-learning method is proposed which can dynamically adapt and continuously adjust the initiative weight of the robot according to the change of the task.Firstly,the physical human-robot cooperation model,including the role factor is built.Then,a reinforcement learningmodel that can adjust the role factor in real time is established,and a reward and actionmodel is designed.The role factor can be adjusted continuously according to the comprehensive performance of the human-robot interaction force and the robot’s Jerk during the repeated installation.Finally,the roles adjustment rule established above continuously improves the comprehensive performance.Experiments of the dynamic roles allocation and the effect of the performance weighting coefficient on the result have been verified.The results show that the proposed method can realize the role adaptation and achieve the dual optimization goal of reducing the sum of the cooperator force and the robot’s Jerk.展开更多
This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control ...This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.展开更多
Image description task is the intersection of computer vision and natural language processing,and it has important prospects,including helping computers understand images and obtaining information for the visually imp...Image description task is the intersection of computer vision and natural language processing,and it has important prospects,including helping computers understand images and obtaining information for the visually impaired.This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images.Our method focuses on refining the reward function in deep reinforcement learning,facilitating the generation of precise descriptions by aligning visual and textual features more closely.Our approach comprises three key architectures.Firstly,it utilizes Residual Network 101(ResNet-101)and Faster Region-based Convolutional Neural Network(Faster R-CNN)to extract average and local image features,respectively,followed by the implementation of a dual attention mechanism for intricate feature fusion.Secondly,the Transformer model is engaged to derive contextual semantic features from textual data.Finally,the generation of descriptive text is executed through a two-layer long short-term memory network(LSTM),directed by the value and reward functions.Compared with the image description method that relies on deep learning,the score of Bilingual Evaluation Understudy(BLEU-1)is 0.762,which is 1.6%higher,and the score of BLEU-4 is 0.299.Consensus-based Image Description Evaluation(CIDEr)scored 0.998,Recall-Oriented Understudy for Gisting Evaluation(ROUGE)scored 0.552,the latter improved by 0.36%.These results not only attest to the viability of our approach but also highlight its superiority in the realm of image description.Future research can explore the integration of our method with other artificial intelligence(AI)domains,such as emotional AI,to create more nuanced and context-aware systems.展开更多
The stability of the ancient flood control levees is mainly influenced by water level fluctuations, groundwater concentration and rainfalls. This paper takes the Lanxi ancient levee as a research object to study the e...The stability of the ancient flood control levees is mainly influenced by water level fluctuations, groundwater concentration and rainfalls. This paper takes the Lanxi ancient levee as a research object to study the evolution laws of its seepage, displacement and stability before and after reinforcement with the upside-down hanging wells and grouting curtain through numerical simulation methods combined with experiments and observations. The study results indicate that the filled soil is less affected by water level fluctuations and groundwater concentration after reinforcement. A high groundwater level is detrimental to the levee's long-term stability, and the drainage issues need to be fully considered. The deformation of the reinforced levee is effectively controlled since the fill deformation is mainly borne by the upside-down hanging wells. The safety factors of the levee before reinforcement vary significantly with the water level. The minimum value of the safety factors is 0.886 during the water level decreasing period, indicating a very high risk of the instability. While it reached 1.478 after reinforcement, the stability of the ancient levee is improved by a large margin.展开更多
This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a larg...This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and satellites.All these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become unavailable.Motivated by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical dynamics.The approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control approach.Different from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control signal.The developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output tracking.Eventually,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.展开更多
The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC n...The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/6G.However, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.展开更多
Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy....Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy.Due to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC server.In this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks.Furthermore,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted cost.The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution.Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.展开更多
The control of large deformation problems in layered soft rock tunnels needs to solve urgently.The roof problem is particularly severe among the deformation issues in tunnels.This study first analyzes the asymmetric d...The control of large deformation problems in layered soft rock tunnels needs to solve urgently.The roof problem is particularly severe among the deformation issues in tunnels.This study first analyzes the asymmetric deformation modes in layered soft rock tunnels with large deformations.Subsequently,we construct a mechanical model under ideal conditions for controlling the roof of layered soft rock tunnels through high preload with the support of NPR anchor cables.The prominent roles of long and short NPR anchor cables in the support system are also analyzed.The results indicate the significance of high preload in controlling the roof of layered soft rock tunnels.The short NPR anchor cables effectively improve the integrity of the stratified soft rock layers,while the long NPR anchor cables effectively mobilize the self-bearing capacity of deep-stable rock layers.Finally,the high-preload support method with NPR anchor cables is validated to have a good effect on controlling large deformations in layered soft rock tunnels through field monitoring data.展开更多
To solve the problem of the low interference success rate of air defense missile radio fuzes due to the unified interference form of the traditional fuze interference system,an interference decision method based Q-lea...To solve the problem of the low interference success rate of air defense missile radio fuzes due to the unified interference form of the traditional fuze interference system,an interference decision method based Q-learning algorithm is proposed.First,dividing the distance between the missile and the target into multiple states to increase the quantity of state spaces.Second,a multidimensional motion space is utilized,and the search range of which changes with the distance of the projectile,to select parameters and minimize the amount of ineffective interference parameters.The interference effect is determined by detecting whether the fuze signal disappears.Finally,a weighted reward function is used to determine the reward value based on the range state,output power,and parameter quantity information of the interference form.The effectiveness of the proposed method in selecting the range of motion space parameters and designing the discrimination degree of the reward function has been verified through offline experiments involving full-range missile rendezvous.The optimal interference form for each distance state has been obtained.Compared with the single-interference decision method,the proposed decision method can effectively improve the success rate of interference.展开更多
To enhance flow stability and reduce hydrodynamic noise caused by fluctuating pressure,a quasiperiodic elastic support skin composed of flexible walls and elastic support elements is proposed for fluid noise reduction...To enhance flow stability and reduce hydrodynamic noise caused by fluctuating pressure,a quasiperiodic elastic support skin composed of flexible walls and elastic support elements is proposed for fluid noise reduction.The arrangement of the elastic support element is determined by the equivalent periodic distance and quasi-periodic coefficient.In this paper,a dynamic model of skin in a fluid environment is established.The influence of equivalent periodic distance and quasi-periodic coefficient on flow stability is investigated.The results suggest that arranging the elastic support elements in accordance with the quasi-periodic law can effectively enhance flow stability.Meanwhile,the hydrodynamic noise calculation results demonstrate that the skin exhibits excellent noise reduction performance,with reductions of 10 dB in the streamwise direction,11 dB in the spanwise direction,and 10 dB in the normal direction.The results also demonstrate that the stability analysis method can serve as a diagnostic tool for flow fields and guide the design of noise reduction structures.展开更多
In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classif...In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.展开更多
Enhancing the stability of supported noble metal catalysts emerges is a major challenge in both science and industry.Herein,a heterogeneous Pd catalyst(Pd/NCF)was prepared by supporting Pd ultrafine metal nanoparticle...Enhancing the stability of supported noble metal catalysts emerges is a major challenge in both science and industry.Herein,a heterogeneous Pd catalyst(Pd/NCF)was prepared by supporting Pd ultrafine metal nanoparticles(NPs)on nitrogen-doped carbon;synthesized by using F127 as a stabilizer,as well as chitosan as a carbon and nitrogen source.The Pd/NCF catalyst was efficient and recyclable for oxidative carbonylation of phenol to diphenyl carbonate,exhibiting higher stability than Pd/NC prepared without F127 addition.The hydrogen bond between chitosan(CTS)and F127 was enhanced by F127,which anchored the N in the free amino group,increasing the N content of the carbon material and ensuring that the support could provide sufficient N sites for the deposition of Pd NPs.This process helped to improve metal dispersion.The increased metal-support interaction,which limits the leaching and coarsening of Pd NPs,improves the stability of the Pd/NCF catalyst.Furthermore,density functional theory calculations indicated that pyridine N stabilized the Pd^(2+)species,significantly inhibiting the loss of Pd^(2+)in Pd/NCF during the reaction process.This work provides a promising avenue towards enhancing the stability of nitrogen-doped carbon-supported metal catalysts.展开更多
The weak interface interaction and solid-solid phase transition have long been a conundrum for 1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane(HMX)-based polymer-bonded explosives(PBX).A two-step strategy that involves...The weak interface interaction and solid-solid phase transition have long been a conundrum for 1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane(HMX)-based polymer-bonded explosives(PBX).A two-step strategy that involves the pretreatment of HMX to endow—OH groups on the surface via polyalcohol bonding agent modification and in situ coating with nitrate ester-containing polymer,was proposed to address the problem.Two types of energetic polyether—glycidyl azide polymer(GAP)and nitrate modified GAP(GNP)were grafted onto HMX crystal based on isocyanate addition reaction bridged through neutral polymeric bonding agent(NPBA)layer.The morphology and structure of the HMX-based composites were characterized in detail and the core-shell structure was validated.The grafted polymers obviously enhanced the adhesion force between HMX crystals and fluoropolymer(F2314)binder.Due to the interfacial reinforcement among the components,the two HMX-based composites exhibited a remarkable increment of phase transition peak temperature by 10.2°C and 19.6°C with no more than 1.5%shell content,respectively.Furthermore,the impact and friction sensitivity of the composites decreased significantly as a result of the barrier produced by the grafted polymers.These findings will enhance the future prospects for the interface design of energetic composites aiming to solve the weak interface and safety concerns.展开更多
This work proposes a recorded recurrent twin delayed deep deterministic(RRTD3)policy gradient algorithm to solve the challenge of constructing guidance laws for intercepting endoatmospheric maneuvering missiles with u...This work proposes a recorded recurrent twin delayed deep deterministic(RRTD3)policy gradient algorithm to solve the challenge of constructing guidance laws for intercepting endoatmospheric maneuvering missiles with uncertainties and observation noise.The attack-defense engagement scenario is modeled as a partially observable Markov decision process(POMDP).Given the benefits of recurrent neural networks(RNNs)in processing sequence information,an RNN layer is incorporated into the agent’s policy network to alleviate the bottleneck of traditional deep reinforcement learning methods while dealing with POMDPs.The measurements from the interceptor’s seeker during each guidance cycle are combined into one sequence as the input to the policy network since the detection frequency of an interceptor is usually higher than its guidance frequency.During training,the hidden states of the RNN layer in the policy network are recorded to overcome the partially observable problem that this RNN layer causes inside the agent.The training curves show that the proposed RRTD3 successfully enhances data efficiency,training speed,and training stability.The test results confirm the advantages of the RRTD3-based guidance laws over some conventional guidance laws.展开更多
With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most...With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.展开更多
Tunnel construction is susceptible to accidents such as loosening, deformation, collapse, and water inrush, especiallyunder complex geological conditions like dense fault areas. These accidents can cause instability a...Tunnel construction is susceptible to accidents such as loosening, deformation, collapse, and water inrush, especiallyunder complex geological conditions like dense fault areas. These accidents can cause instability and damageto the tunnel. As a result, it is essential to conduct research on tunnel construction and grouting reinforcementtechnology in fault fracture zones to address these issues and ensure the safety of tunnel excavation projects. Thisstudy utilized the Xianglushan cross-fault tunnel to conduct a comprehensive analysis on the construction, support,and reinforcement of a tunnel crossing a fault fracture zone using the three-dimensional finite element numericalmethod. The study yielded the following research conclusions: The excavation conditions of the cross-fault tunnelarray were analyzed to determine the optimal construction method for excavation while controlling deformationand stress in the surrounding rock. The middle partition method (CD method) was found to be the most suitable.Additionally, the effects of advanced reinforcement grouting on the cross-fault fracture zone tunnel were studied,and the optimal combination of grouting reinforcement range (140°) and grouting thickness (1m) was determined.The stress and deformation data obtained fromon-site monitoring of the surrounding rock was slightly lower thanthe numerical simulation results. However, the change trend of both sets of data was found to be consistent. Theseresearch findings provide technical analysis and data support for the construction and design of cross-fault tunnels.展开更多
基金the National Natural Science Foundation of China(Grant No.51978041)。
文摘Tunnels deeply buried have high crustal stress and are prone to large deformation disasters when encountering soft rock.The large deformation phenomenon during the construction process of the Maoxian Tunnel on the Chengdu-Lanzhou Railway is particularly evident.This article focuses on the large deformation problem of the No.1 inclined shaft of the Maoxian Tunnel,and uses on-site monitoring methods to explore the reasons for tunnel structure failure,and analyzes the mechanical behavior of the tunnel structure.By using numerical simulation methods,the effectiveness of the second-layer support in resisting creep loads in tunnels was studied,and the influence of the construction time of the secondlayer support on the mechanical properties of the tunnel was discussed.The results indicate that the first-layer support in the tunnel is a structural failure caused by asymmetric deformation caused by creep,while the second-layer support has a good effect on resisting creep loads.The research results can provide a technical reference for deformation control of squeezing tunnels.
基金supported in part by the National Natural Science Foundation of China(62222301, 62073085, 62073158, 61890930-5, 62021003)the National Key Research and Development Program of China (2021ZD0112302, 2021ZD0112301, 2018YFC1900800-5)Beijing Natural Science Foundation (JQ19013)。
文摘Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.
基金supported in part by NSFC (62102099, U22A2054, 62101594)in part by the Pearl River Talent Recruitment Program (2021QN02S643)+9 种基金Guangzhou Basic Research Program (2023A04J1699)in part by the National Research Foundation, SingaporeInfocomm Media Development Authority under its Future Communications Research Development ProgrammeDSO National Laboratories under the AI Singapore Programme under AISG Award No AISG2-RP-2020-019Energy Research Test-Bed and Industry Partnership Funding Initiative, Energy Grid (EG) 2.0 programmeDesCartes and the Campus for Research Excellence and Technological Enterprise (CREATE) programmeMOE Tier 1 under Grant RG87/22in part by the Singapore University of Technology and Design (SUTD) (SRG-ISTD-2021- 165)in part by the SUTD-ZJU IDEA Grant SUTD-ZJU (VP) 202102in part by the Ministry of Education, Singapore, through its SUTD Kickstarter Initiative (SKI 20210204)。
文摘Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.
基金funded by National Key R&D Program of China to Fusheng Zha with Grant numbers 2020YFB13134Natural Science Foundation of China to Fusheng Zha with Grant numbers U2013602,52075115,51521003,61911530250.
文摘Artificial intelligence is currently achieving impressive success in all fields.However,autonomous navigation remains a major challenge for AI.Reinforcement learning is used for target navigation to simulate the interaction between the brain and the environment at the behavioral level,but the Artificial Neural Network trained by reinforcement learning cannot match the autonomous mobility of humans and animals.The hippocampus–striatum circuits are considered as key circuits for target navigation planning and decision-making.This paper aims to construct a bionic navigation model of reinforcement learning corresponding to the nervous system to improve the autonomous navigation performance of the robot.The ventral striatum is considered to be the behavioral evaluation region,and the hippocampal–striatum circuit constitutes the position–reward association.In this paper,a set of episode cognition and reinforcement learning system simulating the mechanism of hippocampus and ventral striatum is constructed,which is used to provide target guidance for the robot to perform autonomous tasks.Compared with traditional methods,this system reflects the high efficiency of learning and better Environmental Adaptability.Our research is an exploration of the intersection and fusion of artificial intelligence and neuroscience,which is conducive to the development of artificial intelligence and the understanding of the nervous system.
基金The research has been generously supported by Tianjin Education Commission Scientific Research Program(2020KJ056),ChinaTianjin Science and Technology Planning Project(22YDTPJC00970),China.The authors would like to express their sincere appreciation for all support provided.
文摘A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve humanrobot cooperation performance for a curtain wall installation task.This method breaks the traditional idea that the robot is regarded as the follower or only adjusts the leader and the follower in cooperation.In this paper,a self-learning method is proposed which can dynamically adapt and continuously adjust the initiative weight of the robot according to the change of the task.Firstly,the physical human-robot cooperation model,including the role factor is built.Then,a reinforcement learningmodel that can adjust the role factor in real time is established,and a reward and actionmodel is designed.The role factor can be adjusted continuously according to the comprehensive performance of the human-robot interaction force and the robot’s Jerk during the repeated installation.Finally,the roles adjustment rule established above continuously improves the comprehensive performance.Experiments of the dynamic roles allocation and the effect of the performance weighting coefficient on the result have been verified.The results show that the proposed method can realize the role adaptation and achieve the dual optimization goal of reducing the sum of the cooperator force and the robot’s Jerk.
基金supported in part by the Natural Sciences Engineering Research Council of Canada (NSERC)。
文摘This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.
基金This research was funded by the Natural Science Foundation of Gansu Province with Approval Numbers 20JR10RA334 and 21JR7RA570Funding is provided for the 2021 Longyuan Youth Innovation and Entrepreneurship Talent Project with Approval Number 2021LQGR20+1 种基金the University Level Innovation Project with Approval NumbersGZF2020XZD18jbzxyb2018-01 of Gansu University of Political Science and Law.
文摘Image description task is the intersection of computer vision and natural language processing,and it has important prospects,including helping computers understand images and obtaining information for the visually impaired.This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images.Our method focuses on refining the reward function in deep reinforcement learning,facilitating the generation of precise descriptions by aligning visual and textual features more closely.Our approach comprises three key architectures.Firstly,it utilizes Residual Network 101(ResNet-101)and Faster Region-based Convolutional Neural Network(Faster R-CNN)to extract average and local image features,respectively,followed by the implementation of a dual attention mechanism for intricate feature fusion.Secondly,the Transformer model is engaged to derive contextual semantic features from textual data.Finally,the generation of descriptive text is executed through a two-layer long short-term memory network(LSTM),directed by the value and reward functions.Compared with the image description method that relies on deep learning,the score of Bilingual Evaluation Understudy(BLEU-1)is 0.762,which is 1.6%higher,and the score of BLEU-4 is 0.299.Consensus-based Image Description Evaluation(CIDEr)scored 0.998,Recall-Oriented Understudy for Gisting Evaluation(ROUGE)scored 0.552,the latter improved by 0.36%.These results not only attest to the viability of our approach but also highlight its superiority in the realm of image description.Future research can explore the integration of our method with other artificial intelligence(AI)domains,such as emotional AI,to create more nuanced and context-aware systems.
基金the scientific research foundation of Zhejiang Provincial Natural Science Foundation of China (LTGG24E090002)Zhejiang University of Water Resources and Electric Power (xky2022013)+1 种基金Major Science and Technology Plan Project of Zhejiang Provincial Department of Water Resources (RA1904)the water conservancy management department, Zhejiang Design Institute of Water Conservancy and Hydro Electric Power Co., Ltd. and the construction company for their support。
文摘The stability of the ancient flood control levees is mainly influenced by water level fluctuations, groundwater concentration and rainfalls. This paper takes the Lanxi ancient levee as a research object to study the evolution laws of its seepage, displacement and stability before and after reinforcement with the upside-down hanging wells and grouting curtain through numerical simulation methods combined with experiments and observations. The study results indicate that the filled soil is less affected by water level fluctuations and groundwater concentration after reinforcement. A high groundwater level is detrimental to the levee's long-term stability, and the drainage issues need to be fully considered. The deformation of the reinforced levee is effectively controlled since the fill deformation is mainly borne by the upside-down hanging wells. The safety factors of the levee before reinforcement vary significantly with the water level. The minimum value of the safety factors is 0.886 during the water level decreasing period, indicating a very high risk of the instability. While it reached 1.478 after reinforcement, the stability of the ancient levee is improved by a large margin.
基金supported in part by the National Key R&D Program of China under Grant 2021YFB2011300the National Natural Science Foundation of China under Grant 52075262。
文摘This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and satellites.All these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become unavailable.Motivated by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical dynamics.The approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control approach.Different from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control signal.The developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output tracking.Eventually,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.
基金supported in part by the National Natural Science Foundation of China under Grants 62201105,62331017,and 62075024in part by the Natural Science Foundation of Chongqing under Grant cstc2021jcyj-msxmX0404+1 种基金in part by the Chongqing Municipal Education Commission under Grant KJQN202100643in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515110056.
文摘The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/6G.However, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.
基金supported by Jilin Provincial Science and Technology Department Natural Science Foundation of China(20210101415JC)Jilin Provincial Science and Technology Department Free exploration research project of China(YDZJ202201ZYTS642).
文摘Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy.Due to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC server.In this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks.Furthermore,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted cost.The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution.Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.
基金financial support from the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(No.2019QZKK0708)the National Natural Science Foundation of China(No.41941018)the Special Fund of Yueqi Scholars(No.800015Z1207).
文摘The control of large deformation problems in layered soft rock tunnels needs to solve urgently.The roof problem is particularly severe among the deformation issues in tunnels.This study first analyzes the asymmetric deformation modes in layered soft rock tunnels with large deformations.Subsequently,we construct a mechanical model under ideal conditions for controlling the roof of layered soft rock tunnels through high preload with the support of NPR anchor cables.The prominent roles of long and short NPR anchor cables in the support system are also analyzed.The results indicate the significance of high preload in controlling the roof of layered soft rock tunnels.The short NPR anchor cables effectively improve the integrity of the stratified soft rock layers,while the long NPR anchor cables effectively mobilize the self-bearing capacity of deep-stable rock layers.Finally,the high-preload support method with NPR anchor cables is validated to have a good effect on controlling large deformations in layered soft rock tunnels through field monitoring data.
基金National Natural Science Foundation of China(61973037)National 173 Program Project(2019-JCJQ-ZD-324).
文摘To solve the problem of the low interference success rate of air defense missile radio fuzes due to the unified interference form of the traditional fuze interference system,an interference decision method based Q-learning algorithm is proposed.First,dividing the distance between the missile and the target into multiple states to increase the quantity of state spaces.Second,a multidimensional motion space is utilized,and the search range of which changes with the distance of the projectile,to select parameters and minimize the amount of ineffective interference parameters.The interference effect is determined by detecting whether the fuze signal disappears.Finally,a weighted reward function is used to determine the reward value based on the range state,output power,and parameter quantity information of the interference form.The effectiveness of the proposed method in selecting the range of motion space parameters and designing the discrimination degree of the reward function has been verified through offline experiments involving full-range missile rendezvous.The optimal interference form for each distance state has been obtained.Compared with the single-interference decision method,the proposed decision method can effectively improve the success rate of interference.
基金National Natural Science Foundation of China(Grant Nos.52075111,51775123)Fundamental Research Funds for the Central Universities(Grant No.3072022JC0701)。
文摘To enhance flow stability and reduce hydrodynamic noise caused by fluctuating pressure,a quasiperiodic elastic support skin composed of flexible walls and elastic support elements is proposed for fluid noise reduction.The arrangement of the elastic support element is determined by the equivalent periodic distance and quasi-periodic coefficient.In this paper,a dynamic model of skin in a fluid environment is established.The influence of equivalent periodic distance and quasi-periodic coefficient on flow stability is investigated.The results suggest that arranging the elastic support elements in accordance with the quasi-periodic law can effectively enhance flow stability.Meanwhile,the hydrodynamic noise calculation results demonstrate that the skin exhibits excellent noise reduction performance,with reductions of 10 dB in the streamwise direction,11 dB in the spanwise direction,and 10 dB in the normal direction.The results also demonstrate that the stability analysis method can serve as a diagnostic tool for flow fields and guide the design of noise reduction structures.
基金supported by National Natural Science Foundation of China(62371098)Natural Science Foundation of Sichuan Province(2023NSFSC1422)+1 种基金National Key Research and Development Program of China(2021YFB2900404)Central Universities of South west Minzu University(ZYN2022032).
文摘In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.
基金support by the National Natural Science Foundation of China(U21A20306,U20A20152)Natural Science Foundation of Hebei Province(B2022202077).
文摘Enhancing the stability of supported noble metal catalysts emerges is a major challenge in both science and industry.Herein,a heterogeneous Pd catalyst(Pd/NCF)was prepared by supporting Pd ultrafine metal nanoparticles(NPs)on nitrogen-doped carbon;synthesized by using F127 as a stabilizer,as well as chitosan as a carbon and nitrogen source.The Pd/NCF catalyst was efficient and recyclable for oxidative carbonylation of phenol to diphenyl carbonate,exhibiting higher stability than Pd/NC prepared without F127 addition.The hydrogen bond between chitosan(CTS)and F127 was enhanced by F127,which anchored the N in the free amino group,increasing the N content of the carbon material and ensuring that the support could provide sufficient N sites for the deposition of Pd NPs.This process helped to improve metal dispersion.The increased metal-support interaction,which limits the leaching and coarsening of Pd NPs,improves the stability of the Pd/NCF catalyst.Furthermore,density functional theory calculations indicated that pyridine N stabilized the Pd^(2+)species,significantly inhibiting the loss of Pd^(2+)in Pd/NCF during the reaction process.This work provides a promising avenue towards enhancing the stability of nitrogen-doped carbon-supported metal catalysts.
基金the support for this work by National Natural Science Foundation of China(Grant Nos.22175139 and 22105156)。
文摘The weak interface interaction and solid-solid phase transition have long been a conundrum for 1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane(HMX)-based polymer-bonded explosives(PBX).A two-step strategy that involves the pretreatment of HMX to endow—OH groups on the surface via polyalcohol bonding agent modification and in situ coating with nitrate ester-containing polymer,was proposed to address the problem.Two types of energetic polyether—glycidyl azide polymer(GAP)and nitrate modified GAP(GNP)were grafted onto HMX crystal based on isocyanate addition reaction bridged through neutral polymeric bonding agent(NPBA)layer.The morphology and structure of the HMX-based composites were characterized in detail and the core-shell structure was validated.The grafted polymers obviously enhanced the adhesion force between HMX crystals and fluoropolymer(F2314)binder.Due to the interfacial reinforcement among the components,the two HMX-based composites exhibited a remarkable increment of phase transition peak temperature by 10.2°C and 19.6°C with no more than 1.5%shell content,respectively.Furthermore,the impact and friction sensitivity of the composites decreased significantly as a result of the barrier produced by the grafted polymers.These findings will enhance the future prospects for the interface design of energetic composites aiming to solve the weak interface and safety concerns.
基金supported by the National Natural Science Foundation of China(Grant No.12072090)。
文摘This work proposes a recorded recurrent twin delayed deep deterministic(RRTD3)policy gradient algorithm to solve the challenge of constructing guidance laws for intercepting endoatmospheric maneuvering missiles with uncertainties and observation noise.The attack-defense engagement scenario is modeled as a partially observable Markov decision process(POMDP).Given the benefits of recurrent neural networks(RNNs)in processing sequence information,an RNN layer is incorporated into the agent’s policy network to alleviate the bottleneck of traditional deep reinforcement learning methods while dealing with POMDPs.The measurements from the interceptor’s seeker during each guidance cycle are combined into one sequence as the input to the policy network since the detection frequency of an interceptor is usually higher than its guidance frequency.During training,the hidden states of the RNN layer in the policy network are recorded to overcome the partially observable problem that this RNN layer causes inside the agent.The training curves show that the proposed RRTD3 successfully enhances data efficiency,training speed,and training stability.The test results confirm the advantages of the RRTD3-based guidance laws over some conventional guidance laws.
基金supported in part by National Natural Science Foundation of China(Nos.62102311,62202377,62272385)in part by Natural Science Basic Research Program of Shaanxi(Nos.2022JQ-600,2022JM-353,2023-JC-QN-0327)+2 种基金in part by Shaanxi Distinguished Youth Project(No.2022JC-47)in part by Scientific Research Program Funded by Shaanxi Provincial Education Department(No.22JK0560)in part by Distinguished Youth Talents of Shaanxi Universities,and in part by Youth Innovation Team of Shaanxi Universities.
文摘With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.
基金the Postgraduate Research and Practice Innovation Program of Jiangsu Province(Grant No.KYCX22_0621)the National Natural Science Foundation of China(Grant No.52209130)Jiangsu Funding Program for Excellent Postdoctoral Talent.
文摘Tunnel construction is susceptible to accidents such as loosening, deformation, collapse, and water inrush, especiallyunder complex geological conditions like dense fault areas. These accidents can cause instability and damageto the tunnel. As a result, it is essential to conduct research on tunnel construction and grouting reinforcementtechnology in fault fracture zones to address these issues and ensure the safety of tunnel excavation projects. Thisstudy utilized the Xianglushan cross-fault tunnel to conduct a comprehensive analysis on the construction, support,and reinforcement of a tunnel crossing a fault fracture zone using the three-dimensional finite element numericalmethod. The study yielded the following research conclusions: The excavation conditions of the cross-fault tunnelarray were analyzed to determine the optimal construction method for excavation while controlling deformationand stress in the surrounding rock. The middle partition method (CD method) was found to be the most suitable.Additionally, the effects of advanced reinforcement grouting on the cross-fault fracture zone tunnel were studied,and the optimal combination of grouting reinforcement range (140°) and grouting thickness (1m) was determined.The stress and deformation data obtained fromon-site monitoring of the surrounding rock was slightly lower thanthe numerical simulation results. However, the change trend of both sets of data was found to be consistent. Theseresearch findings provide technical analysis and data support for the construction and design of cross-fault tunnels.
