The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To addres...The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To address this issue,this study proposes a transfer learning model based on a sequence-to-sequence twodimensional(2D)convolutional long short-term memory neural network(S2SCL2D).The model can use the existing data from other adjacent similar excavations to achieve wall deflection prediction once a limited amount of monitoring data from the target excavation has been recorded.In the absence of adjacent excavation data,numerical simulation data from the target project can be employed instead.A weight update strategy is proposed to improve the prediction accuracy by integrating the stochastic gradient masking with an early stopping mechanism.To illustrate the proposed methodology,an excavation project in Hangzhou,China is adopted.The proposed deep transfer learning model,which uses either adjacent excavation data or numerical simulation data as the source domain,shows a significant improvement in performance when compared to the non-transfer learning model.Using the simulation data from the target project even leads to better prediction performance than using the actual monitoring data from other adjacent excavations.The results demonstrate that the proposed model can reasonably predict the deformation with limited data from the target project.展开更多
Deep excavations in silt strata can lead to large deformation problems,posing risks to both the excavation and adjacent structures.This study combines field monitoring with numerical simulation to investigate the unde...Deep excavations in silt strata can lead to large deformation problems,posing risks to both the excavation and adjacent structures.This study combines field monitoring with numerical simulation to investigate the underlying mechanisms and key aspects associated with large deformation problems induced by deep excavation in silt strata in Shenzhen,China.The monitoring results reveal that,due to the weak property and creep effect of the silt strata,the maximum wall deflection in the first excavated section(Section 1)exceeds its controlled value at more than 93%of measurement points,reaching a peak value of 137.46 mm.Notably,the deformation exhibits prolonged development characteristics,with the diaphragm wall deflections contributing to 39%of the overall deformation magnitude during the construction of the base slab.Subsequently,numerical simulations are carried out to analyze and assess the primary factors influencing excavation-induced deformations,following the observation of large deformations.The simulations indicate that the low strength of the silt soil is a pivotal factor that results in significant deformations.Furthermore,the flexural stiffness of the diaphragm walls exerts a notable influence on the development of deformations.To address these concerns,an optimization study of potential treatment measures was performed during the subsequent excavation of Section 2.The combined treatment approach,which comprises the reinforcement of the silt layer within the excavation and the increase in the thickness of the diaphragm walls,has been demonstrated to offer an economically superior solution for the handling of thick silt strata.This approach has the effect of reducing the lateral wall displacement by 83.1%and the ground settlement by 70.8%,thereby ensuring the safe construction of the deep excavation.展开更多
The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging at...The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.展开更多
During the excavation and support process in deep soft rocks,complex conditions such as high stress and strong disturbance can be encountered.The complex conditions can cause failure of the support system.Aiming at st...During the excavation and support process in deep soft rocks,complex conditions such as high stress and strong disturbance can be encountered.The complex conditions can cause failure of the support system.Aiming at stability control in deep soft rocks,we proposed the excavation compensation theory.A new high strength and high toughness material was developed.The breaking load and elongation of the new material are 1.59 and 1.78 times that of common bolt materials.To overcome the problem that the CABLE element in FLAC^(3D) cannot simulate failure of support structures,the numerical model for the whole process of force-breaking-anchorage failure simulation(FBAS)for bolts(cables)was established.The numerical experiments on the excavation compensation control of deep soft rock were carried out.The excavation compensation control mechanism of high strength and high toughness material was clarified.Compared with the common support scheme,the highly prestressed support has a maximum increase of 90.24%in radial stress compensation rate and a maximum increase of 67.85%in deformation control rate.The results illustrate the rationality of the excavation compensation theory.The compensation design method of excavations in deep soft rocks was proposed and applied in a deep soft rock chamber.The monitoring indicated that the maximum surrounding rock deformation is 180 mm,reduced by 64%compared to the common support.The deformation of the chamber was controlled and the surrounding rock was stable.展开更多
Data-driven approaches such as neural networks are increasingly used for deep excavations due to the growing amount of available monitoring data in practical projects.However,most neural network models only use the da...Data-driven approaches such as neural networks are increasingly used for deep excavations due to the growing amount of available monitoring data in practical projects.However,most neural network models only use the data from a single monitoring point and neglect the spatial relationships between multiple monitoring points.Besides,most models lack flexibility in providing predictions for multiple days after monitoring activity.This study proposes a sequence-to-sequence(seq2seq)two-dimensional(2D)convolutional long short-term memory neural network(S2SCL2D)for predicting the spatiotemporal wall deflections induced by deep excavations.The model utilizes the data from all monitoring points on the entire wall and extracts spatiotemporal features from data by combining the 2D convolutional layers and long short-term memory(LSTM)layers.The S2SCL2D model achieves a long-term prediction of wall deflections through a recursive seq2seq structure.The excavation depth,which has a significant impact on wall deflections,is also considered using a feature fusion method.An excavation project in Hangzhou,China,is used to illustrate the proposed model.The results demonstrate that the S2SCL2D model has superior prediction accuracy and robustness than that of the LSTM and S2SCL1D(one-dimensional)models.The prediction model demonstrates a strong generalizability when applied to an adjacent excavation.Based on the long-term prediction results,practitioners can plan and allocate resources in advance to address the potential engineering issues.展开更多
A holistic and precise assessment of retaining wall deformations is critical for on-site risk management of large combined deep excavation projects,where the risk-related points are highly dispersed,evolving,and inter...A holistic and precise assessment of retaining wall deformations is critical for on-site risk management of large combined deep excavation projects,where the risk-related points are highly dispersed,evolving,and interacting.Despite extensive exploration of this topic in previous studies,the omission of intricate spatiotemporal characteristics of wall deformations has resulted in diminished prediction accuracy and stability.To mitigate this deficiency,a spatiotemporal characteristics matrix for all data points and time series was first generated for a deep excavation scenario and used as input for a new hybrid model that combines convolutional neural network(CNN)and long short-term memory(LSTM)with incorporated attention mechanism(CNN-LSTM-Att),which enables the cross-learning mechanism and improves interpretability.In addition,by leveraging the attention weight,a new risk assessment index for retaining wall deformations across various scenarios was formulated.Then the proposed method was applied in a large combined deep excavation in Shanghai,China.The results show that:(1)The incorporation of fedin characteristic data and the attention mechanism enables the proposed method to produce satisfactory prediction results for the holistic spatiotemporal distribution of a large combined excavation;(2)Compared with other published models,the proposed model shows much better prediction accuracy,interpretability,and stability,especially in medium-and long-term predictions;and(3)The new risk assessment index serves as a reliable decision-making tool for assessing the risk evolution of retaining wall deformations and provides valuable guidance for effective risk management in multi-scenario excavation projects.展开更多
Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed ...Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed satellites,ground users now can be covered by multiple visible satellites,but also face complex handover issues with such massive high-mobility satellites in multi-layer.The end-to-end routing is also affected by the handover behavior.In this paper,we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks.Firstly,an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model.Subsequently,an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network(D3QN)-based deep reinforcement learning algorithm for single-layer constellations.Moreover,an optimal crosslayer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead.Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation,showcasing reductions of up to 8.2%and 59.5%in end-to-end latency and jitter respectively,when compared to the existing handover strategies.展开更多
Real-time prediction of excavation-induced displacement of retaining pile during the deep excavation process is crucial for construction safety.This paper proposes a modified back analysis method with multi-objective ...Real-time prediction of excavation-induced displacement of retaining pile during the deep excavation process is crucial for construction safety.This paper proposes a modified back analysis method with multi-objective optimization procedure,which enables a real-time prediction of horizontal displacement of retaining pile during construction.As opposed to the traditional stage-by-stage back analysis,time series monitoring data till the current excavation stage are utilized to form a multi-objective function.Then,the multi-objective particle swarm optimization (MOPSO) algorithm is applied for parameter identification.The optimized model parameters are immediately adopted to predict the excavation-induced pile deformation in the continuous construction stages.To achieve efficient parameter optimization and real-time prediction of system behavior,the back propagation neural network (BPNN) is established to substitute the finite element model,which is further implemented together with MOPSO for automatic operation.The proposed approach is applied in the Taihu tunnel excavation project,where the effectiveness of the method is demonstrated via the comparisons with the site monitoring data.The method is reliable with a prediction accuracy of more than 90%.Moreover,different optimization algorithms,including non-dominated sorting genetic algorithm (NSGA-II),Pareto Envelope-based Selection Algorithm II (PESA-II) and MOPSO,are compared,and their influences on the prediction accuracy at different excavation stages are studied.The results show that MOPSO has the best performance for high dimensional optimization task.展开更多
The polyurethane foam(PU)compressible layer is a viable solution to the problem of damage to the secondary lining in squeezing tunnels.Nevertheless,the mechanical behaviour of the multi-layer yielding supports has not...The polyurethane foam(PU)compressible layer is a viable solution to the problem of damage to the secondary lining in squeezing tunnels.Nevertheless,the mechanical behaviour of the multi-layer yielding supports has not been thoroughly investigated.To fill this gap,large-scale model tests were conducted in this study.The synergistic load-bearing mechanics were analyzed using the convergenceconfinement method.Two types of multi-layer yielding supports with different thicknesses(2.5 cm,3.75 cm and 5 cm)of PU compressible layers were investigated respectively.Digital image correlation(DIC)analysis and acoustic emission(AE)techniques were used for detecting the deformation fields and damage evolution of the multi-layer yielding supports in real-time.Results indicated that the loaddisplacement relationship of the multi-layer yielding supports could be divided into the crack initiation,crack propagation,strain-hardening,and failure stages.Compared with those of the stiff support,the toughness,deformability and ultimate load of the yielding supports were increased by an average of 225%,61%and 32%,respectively.Additionally,the PU compressible layer is positioned between two primary linings to allow the yielding support to have greater mechanical properties.The analysis of the synergistic bearing effect suggested that the thickness of PU compressible layer and its location significantly affect the mechanical properties of the yielding supports.The use of yielding supports with a compressible layer positioned between the primary and secondary linings is recommended to mitigate the effects of high geo-stress in squeezing tunnels.展开更多
The unloading effect of the excavation of deep roadways has been considerably studied, but most research methods have been limited to numerical simulations and field measurements. Only a few have adopted experimental ...The unloading effect of the excavation of deep roadways has been considerably studied, but most research methods have been limited to numerical simulations and field measurements. Only a few have adopted experimental methods for similar simulations. On the basis of the theory of mechanics,the testing system is designed considering initial geostress and dynamic unloading. The system includes an impact unloading gear and in-situ stress loading equipment, and a designed three-link structure and the impact hammer can effectively realize the dynamic excavation of roadways.Meanwhile, a cyclic excavation similar simulation experiment on a deep roadway is conducted in a laboratory. The testing system and the relevant monitoring facilities are utilized, and the unloading effect inside the surrounding rock under the cyclic dynamic excavation is studied. Results show that the cyclic dynamic excavation causes significant unloading only in the nearby rock mass, and the unloading indicators show nonlinear changes.Moreover, when the lateral pressure coefficient is 1.2,the damage is concentrated on both roadsides due to the excavation unloading. Meanwhile, the damage gradually decays as the span increases.展开更多
In this paper, design, re-design, and performance of a long-standing very deep excavation, which was originally planned to depth of 38 m, are presented. Over-digging was not planned in the original design,thus the rea...In this paper, design, re-design, and performance of a long-standing very deep excavation, which was originally planned to depth of 38 m, are presented. Over-digging was not planned in the original design,thus the reassessment was performed. Two main topics were followed: deepening to increase the maximum depth of an existent excavation from 38 m to 42.5 m, and feasibility for upgrading a predesigned support system from temporary to permanent support system. The geological investigations in the project site illustrated a type of stiff and cemented coarse-grained alluvium. An observational approach with additional geotechnical investigations and in situ tests was applied. Back analyses of stability of an unsupported access ramp, as well as deformation monitoring of walls, were used in order to review geotechnical design parameters that represent the full-scale behavior of the ground. Additional nails and soldier piles together with building mat foundation were implemented as a complementary lateral support in the retaining system. From an engineering point of view, by assuming a corrosion rate of 0.065 mm/a for existent rebars, according to chemical and electrical resistivity tests, the long-term performance of the revised retaining system was verified by static and pseudo-dynamic ultimate limit state analyses. Performance monitoring during the construction shows that the measured deformation is in the lower limit of the prediction.展开更多
The dilation angle is the most commonly used parameter to study nonlinear post-peak dilatancy(PPD)behavior and simulate surrounding rock deformation;however,simplified or constant dilatancy models are often used in nu...The dilation angle is the most commonly used parameter to study nonlinear post-peak dilatancy(PPD)behavior and simulate surrounding rock deformation;however,simplified or constant dilatancy models are often used in numerical calculations owing to their simple mathematical forms.This study developed a PPD model for rocks(rock masses)based on the Alejanoe-Alonso(A-A)dilatancy model.The developed model comprehensively reflects the influences of confining pressure(σ_(3))and plastic shear strain(γ^(p)),with the advantages of a simple mathematical form,while requiring fewer parameters and demonstrating a clear physical significance.The overall fitting accuracy of the PPD model for 11 different rocks was found to be higher than that of the A-A model,particularly for Witwatersrand quartzite and jointed granite.The applicability and reliability of the PPD model to jointed granites and different scaled Moura coals were also investigated,and the model was found to be more suitable for the soft and large-scale rocks,e.g.deep rock mass.The PPD model was also successfully applied in studying the mechanical response of a circular tunnel excavated in strain-softening rock mass,and the developed semi-analytical solution was compared and verified with existing analytical solutions.The sensitivities of the rock dilatancy to γ^(p) and σ_(3) showed significant spatial variabilities along the radial direction of the surrounding rock,and the dilation angle did not exhibit a monotonical increasing or decreasing law from the elasticeplastic boundary to the tunnel wall,thereby presenting the σ3-or γ^(p)-dominated differential effects of rock dilatancy.Tunnel deformation parabolically or exponentially increased with increasing in situ stress(buried depth).The developed PPD model is promising to conduct refined numerical and analytical analyses for deep tunneling,which produces extensive plastic deformation and exhibits significant nonlinear post-peak behavior.展开更多
To predict rock burst in deep mining excavation in Linglong gold mine, systematical laboratory tests of mechanical properties of rock, in situ stress measurement and 3-D FEM analysis on energy distribution in rock mas...To predict rock burst in deep mining excavation in Linglong gold mine, systematical laboratory tests of mechanical properties of rock, in situ stress measurement and 3-D FEM analysis on energy distribution in rock mass surrounding deep mining rooms were carried out. According to various prediction criteria of rock burst, it is concluded that rock burst is liable to occur during deep mining excavation in the mine.展开更多
A complete case of a deep excavation was explored. According to the practical working conditions, a 3D non-linear finite element procedure is used to simulate a deep excavation supported by the composite soil nailed w...A complete case of a deep excavation was explored. According to the practical working conditions, a 3D non-linear finite element procedure is used to simulate a deep excavation supported by the composite soil nailed wall with bored piles in soft soil. The modified cam clay model is employed as the constitutive relationship of the soil in the numerical simulation. Results from the numerical analysis are fitted well with the field data, which indicate that the research approach used is reliable. Based on the field data and numerical results of the deep excavation supported by four different patterns of the composite soil nailed wall, the significant corner effect is founded in the 3D deep excavation. If bored piles or soil anchors are considered in the composite soil nailed wall, they are beneficial to decreasing deformations and internal forces of bored piles, cement mixing piles, soil anchors, soil nailings and soil around the deep excavation. Besides, the effects due to bored piles are more significant than those deduced from soil anchors. All mentioned above prove that the composite soil nailed wall with bored piles is feasible in the deep excavation.展开更多
This paper presents an analytical procedure for massive water-sealing barriers(MWSBs)that are made of partially overlapped jet-grouting columns used for deep excavations,in which two crucial factors of the permeabilit...This paper presents an analytical procedure for massive water-sealing barriers(MWSBs)that are made of partially overlapped jet-grouting columns used for deep excavations,in which two crucial factors of the permeability and strength of jet-grouted materials are considered.Subsequently,a calculation example is analyzed and discussed.Results show that“tension failure”mechanism is a major concern for the structural failure during a design of MWSBs.The maximum allowable seepage discharge is a crucial index for the design of MWSBs,which has a significant influence on determining the design parameters of MWSBs.Compared with the design procedure for MWSBs that is proposed in this paper,the design parameters of MWSBs determined by the stability equilibrium and seepage stability equilibrium approaches are conservative due to the fact that it fails to consider the permeability or strength of jet-grouted materials that makes a contribution to the structural safety.Based on the proposed design method,the ranges of both the thickness and depth of MWSBs for a case history of subway excavation in Fuzhou,China were determined.Finally,field pumping test results showed that the water-tightness performance of MWSBs performed at site was quite well.展开更多
The squeezing scenario in deep weak rock tunnels can hinder underground construction.However,due to the limitations of test technologies at hand,the real excavation stress path cannot be mimicked in the laboratory.Thu...The squeezing scenario in deep weak rock tunnels can hinder underground construction.However,due to the limitations of test technologies at hand,the real excavation stress path cannot be mimicked in the laboratory.Thus,the large deformation mechanism of deep weak rocks still remains unclear.For this,a true triaxial apparatus(TTA)to investigate the mechanical responses of deep weak rock under excavation stress paths in field and reveal the squeezing mechanism of deep tunnels is assembled and developed at Northeastern University,China.The apparatus can perform instantaneous unloading in s3 direction based on electromagnetism technology.In addition,uniform loading and deformation measurements can be carried out based on the proposed linked interlocking clamp and antifriction device,even if the sample has a strong dilatation deformation performance.Next,a bore trepanning is designed to capture noiseless acoustic emission(AE)signals for deep weak rock at a low threshold.Finally,two tests were are conducted using this instrument to preliminarily understand the failure and deformation features of deep weak rock based on fractured marble.The results show that the complete stressestrain curves of fractured marble have the characteristics of low strengths and large deformations,and the larger deformation and the more serious failure occur when the fractured marble enters the post-peak state after excavation.The results show that the developed apparatus is likely to be applicable for deep weak rock engineering.展开更多
Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To kn...Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.展开更多
The Fort d’Issy-Vanves-Clamart(FIVC)braced excavation in France is analyzed to provide insights into the geotechnical serviceability assessment of excavations at great depth within deterministic and probabilistic fra...The Fort d’Issy-Vanves-Clamart(FIVC)braced excavation in France is analyzed to provide insights into the geotechnical serviceability assessment of excavations at great depth within deterministic and probabilistic frameworks.The FIVC excavation is excavated at 32 m below the ground surface in Parisian sedimentary basin and a plane-strain finite element analysis is implemented to examine the wall deflections and ground surface settlements.A stochastic finite element method based on the polynomial chaos Kriging metamodel(MSFEM)is then proposed for the probabilistic analyses.Comparisons with field measurements and former studies are carried out.Several academic cases are then conducted to investigate the great-depth excavation stability regarding the maximum horizontal wall deflection and maximum ground surface settlement.The results indicate that the proposed MSFEM is effective for probabilistic analyses and can provide useful insights for the excavation design and construction.A sensitivity analysis for seven considered random parameters is then implemented.The soil friction angle at the excavation bottom layer is the most significant one for design.The soil-wall interaction effects on the excavation stability are also given.展开更多
The studies of prediction and control of rockburst are presented during deep excavation in a gold mine in China. Firstly,the stress-relief method is used to obtain a vast amount of data about initial geostress. Second...The studies of prediction and control of rockburst are presented during deep excavation in a gold mine in China. Firstly,the stress-relief method is used to obtain a vast amount of data about initial geostress. Secondly,3D FEM analyses of large scale are performed to find out the law of geostress distribution at various excavation levels of the mining area. At the same time,as an equally important measure,six typical kinds of rock blocks are sampled for the experimental study of rockburst tendency. According to the synthesized results of the theoretical and testing results,the methods of brittleness coefficient,brittle index and stress,and so on,are adopted. Finally,the evaluation on the possibility of rockbursts is given that may take place at the deep mining area and some effective measures are put forward to prevent and control the rockburst.展开更多
As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and...As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are crucial.The key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time series.To address this issue,we propose an anomaly detection method based on distributed deep learning.Our method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete features.We use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation set.Our method can not only detect abnormal attacks but also locate the sensors that cause anomalies.We conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public datasets.The experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2023YFC3009400)the National Natural Science Foundation of China(Grant Nos.42307218 and U2239251).
文摘The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To address this issue,this study proposes a transfer learning model based on a sequence-to-sequence twodimensional(2D)convolutional long short-term memory neural network(S2SCL2D).The model can use the existing data from other adjacent similar excavations to achieve wall deflection prediction once a limited amount of monitoring data from the target excavation has been recorded.In the absence of adjacent excavation data,numerical simulation data from the target project can be employed instead.A weight update strategy is proposed to improve the prediction accuracy by integrating the stochastic gradient masking with an early stopping mechanism.To illustrate the proposed methodology,an excavation project in Hangzhou,China is adopted.The proposed deep transfer learning model,which uses either adjacent excavation data or numerical simulation data as the source domain,shows a significant improvement in performance when compared to the non-transfer learning model.Using the simulation data from the target project even leads to better prediction performance than using the actual monitoring data from other adjacent excavations.The results demonstrate that the proposed model can reasonably predict the deformation with limited data from the target project.
基金supported by the National Natural Science Foundation of China (Grant Nos.52008039 and 52308425)the Natural Science Foundation of Hunan Province (Grant No.2021JJ40592).
文摘Deep excavations in silt strata can lead to large deformation problems,posing risks to both the excavation and adjacent structures.This study combines field monitoring with numerical simulation to investigate the underlying mechanisms and key aspects associated with large deformation problems induced by deep excavation in silt strata in Shenzhen,China.The monitoring results reveal that,due to the weak property and creep effect of the silt strata,the maximum wall deflection in the first excavated section(Section 1)exceeds its controlled value at more than 93%of measurement points,reaching a peak value of 137.46 mm.Notably,the deformation exhibits prolonged development characteristics,with the diaphragm wall deflections contributing to 39%of the overall deformation magnitude during the construction of the base slab.Subsequently,numerical simulations are carried out to analyze and assess the primary factors influencing excavation-induced deformations,following the observation of large deformations.The simulations indicate that the low strength of the silt soil is a pivotal factor that results in significant deformations.Furthermore,the flexural stiffness of the diaphragm walls exerts a notable influence on the development of deformations.To address these concerns,an optimization study of potential treatment measures was performed during the subsequent excavation of Section 2.The combined treatment approach,which comprises the reinforcement of the silt layer within the excavation and the increase in the thickness of the diaphragm walls,has been demonstrated to offer an economically superior solution for the handling of thick silt strata.This approach has the effect of reducing the lateral wall displacement by 83.1%and the ground settlement by 70.8%,thereby ensuring the safe construction of the deep excavation.
基金Nourah bint Abdulrahman University for funding this project through the Researchers Supporting Project(PNURSP2025R319)Riyadh,Saudi Arabia and Prince Sultan University for covering the article processing charges(APC)associated with this publication.Special acknowledgement to Automated Systems&Soft Computing Lab(ASSCL),Prince Sultan University,Riyadh,Saudi Arabia.
文摘The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFC3805700)the National Natural Science Foundation of China(Grant No.42277174)the Fundamental Research Funds for the Central Universities,China(Grant No.2024JCCXSB01).
文摘During the excavation and support process in deep soft rocks,complex conditions such as high stress and strong disturbance can be encountered.The complex conditions can cause failure of the support system.Aiming at stability control in deep soft rocks,we proposed the excavation compensation theory.A new high strength and high toughness material was developed.The breaking load and elongation of the new material are 1.59 and 1.78 times that of common bolt materials.To overcome the problem that the CABLE element in FLAC^(3D) cannot simulate failure of support structures,the numerical model for the whole process of force-breaking-anchorage failure simulation(FBAS)for bolts(cables)was established.The numerical experiments on the excavation compensation control of deep soft rock were carried out.The excavation compensation control mechanism of high strength and high toughness material was clarified.Compared with the common support scheme,the highly prestressed support has a maximum increase of 90.24%in radial stress compensation rate and a maximum increase of 67.85%in deformation control rate.The results illustrate the rationality of the excavation compensation theory.The compensation design method of excavations in deep soft rocks was proposed and applied in a deep soft rock chamber.The monitoring indicated that the maximum surrounding rock deformation is 180 mm,reduced by 64%compared to the common support.The deformation of the chamber was controlled and the surrounding rock was stable.
基金supported by the National Natural Science Foundation of China(Grant No.42307218)the Foundation of Key Laboratory of Soft Soils and Geoenvironmental Engineering(Zhejiang University),Ministry of Education(Grant No.2022P08)the Natural Science Foundation of Zhejiang Province(Grant No.LTZ21E080001).
文摘Data-driven approaches such as neural networks are increasingly used for deep excavations due to the growing amount of available monitoring data in practical projects.However,most neural network models only use the data from a single monitoring point and neglect the spatial relationships between multiple monitoring points.Besides,most models lack flexibility in providing predictions for multiple days after monitoring activity.This study proposes a sequence-to-sequence(seq2seq)two-dimensional(2D)convolutional long short-term memory neural network(S2SCL2D)for predicting the spatiotemporal wall deflections induced by deep excavations.The model utilizes the data from all monitoring points on the entire wall and extracts spatiotemporal features from data by combining the 2D convolutional layers and long short-term memory(LSTM)layers.The S2SCL2D model achieves a long-term prediction of wall deflections through a recursive seq2seq structure.The excavation depth,which has a significant impact on wall deflections,is also considered using a feature fusion method.An excavation project in Hangzhou,China,is used to illustrate the proposed model.The results demonstrate that the S2SCL2D model has superior prediction accuracy and robustness than that of the LSTM and S2SCL1D(one-dimensional)models.The prediction model demonstrates a strong generalizability when applied to an adjacent excavation.Based on the long-term prediction results,practitioners can plan and allocate resources in advance to address the potential engineering issues.
基金supported by the National Natural Science Foundation of China(Grant No.52090082).
文摘A holistic and precise assessment of retaining wall deformations is critical for on-site risk management of large combined deep excavation projects,where the risk-related points are highly dispersed,evolving,and interacting.Despite extensive exploration of this topic in previous studies,the omission of intricate spatiotemporal characteristics of wall deformations has resulted in diminished prediction accuracy and stability.To mitigate this deficiency,a spatiotemporal characteristics matrix for all data points and time series was first generated for a deep excavation scenario and used as input for a new hybrid model that combines convolutional neural network(CNN)and long short-term memory(LSTM)with incorporated attention mechanism(CNN-LSTM-Att),which enables the cross-learning mechanism and improves interpretability.In addition,by leveraging the attention weight,a new risk assessment index for retaining wall deformations across various scenarios was formulated.Then the proposed method was applied in a large combined deep excavation in Shanghai,China.The results show that:(1)The incorporation of fedin characteristic data and the attention mechanism enables the proposed method to produce satisfactory prediction results for the holistic spatiotemporal distribution of a large combined excavation;(2)Compared with other published models,the proposed model shows much better prediction accuracy,interpretability,and stability,especially in medium-and long-term predictions;and(3)The new risk assessment index serves as a reliable decision-making tool for assessing the risk evolution of retaining wall deformations and provides valuable guidance for effective risk management in multi-scenario excavation projects.
基金supported by the National Natural Science Foundation of China(No.62401597)Natural Science Foundation of Hunan Province,China(No.2024JJ6469)the Research Project of National University of Defense Technology,China(No.ZK22-02).
文摘Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed satellites,ground users now can be covered by multiple visible satellites,but also face complex handover issues with such massive high-mobility satellites in multi-layer.The end-to-end routing is also affected by the handover behavior.In this paper,we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks.Firstly,an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model.Subsequently,an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network(D3QN)-based deep reinforcement learning algorithm for single-layer constellations.Moreover,an optimal crosslayer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead.Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation,showcasing reductions of up to 8.2%and 59.5%in end-to-end latency and jitter respectively,when compared to the existing handover strategies.
基金supported by the National Natural Science Foundation of China(Grant Nos.52208380 and 51979270)the Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences(Grant No.SKLGME021022).
文摘Real-time prediction of excavation-induced displacement of retaining pile during the deep excavation process is crucial for construction safety.This paper proposes a modified back analysis method with multi-objective optimization procedure,which enables a real-time prediction of horizontal displacement of retaining pile during construction.As opposed to the traditional stage-by-stage back analysis,time series monitoring data till the current excavation stage are utilized to form a multi-objective function.Then,the multi-objective particle swarm optimization (MOPSO) algorithm is applied for parameter identification.The optimized model parameters are immediately adopted to predict the excavation-induced pile deformation in the continuous construction stages.To achieve efficient parameter optimization and real-time prediction of system behavior,the back propagation neural network (BPNN) is established to substitute the finite element model,which is further implemented together with MOPSO for automatic operation.The proposed approach is applied in the Taihu tunnel excavation project,where the effectiveness of the method is demonstrated via the comparisons with the site monitoring data.The method is reliable with a prediction accuracy of more than 90%.Moreover,different optimization algorithms,including non-dominated sorting genetic algorithm (NSGA-II),Pareto Envelope-based Selection Algorithm II (PESA-II) and MOPSO,are compared,and their influences on the prediction accuracy at different excavation stages are studied.The results show that MOPSO has the best performance for high dimensional optimization task.
基金supported by the National Key Research and Development Program of China (Grant No.2021YFB2600800)the National Key Research and Development 451 Program of China (Grant No.2021YFC3100803)the Guangdong Innovative and Entrepreneurial Research Team Program (Grant No.2016ZT06N340).
文摘The polyurethane foam(PU)compressible layer is a viable solution to the problem of damage to the secondary lining in squeezing tunnels.Nevertheless,the mechanical behaviour of the multi-layer yielding supports has not been thoroughly investigated.To fill this gap,large-scale model tests were conducted in this study.The synergistic load-bearing mechanics were analyzed using the convergenceconfinement method.Two types of multi-layer yielding supports with different thicknesses(2.5 cm,3.75 cm and 5 cm)of PU compressible layers were investigated respectively.Digital image correlation(DIC)analysis and acoustic emission(AE)techniques were used for detecting the deformation fields and damage evolution of the multi-layer yielding supports in real-time.Results indicated that the loaddisplacement relationship of the multi-layer yielding supports could be divided into the crack initiation,crack propagation,strain-hardening,and failure stages.Compared with those of the stiff support,the toughness,deformability and ultimate load of the yielding supports were increased by an average of 225%,61%and 32%,respectively.Additionally,the PU compressible layer is positioned between two primary linings to allow the yielding support to have greater mechanical properties.The analysis of the synergistic bearing effect suggested that the thickness of PU compressible layer and its location significantly affect the mechanical properties of the yielding supports.The use of yielding supports with a compressible layer positioned between the primary and secondary linings is recommended to mitigate the effects of high geo-stress in squeezing tunnels.
基金funded by the National Key R&D Program of China(Grant Nos.2017YFC0603000)the National Natural Foundation of China(Grant Nos.51404011,51674008,51774012,51474006,and 51574006)+2 种基金the Key Task Project in Scientific and Technological Research in AnhuiProvince(Grant No.1604a0802107)the Outstanding Top-notch Talent Cultivation Project in Anhui Province(No.gxbj ZD2016051)the Anhui provincial academic and technical leaders and reserve candidates for academic research activities(No.2015H036)
文摘The unloading effect of the excavation of deep roadways has been considerably studied, but most research methods have been limited to numerical simulations and field measurements. Only a few have adopted experimental methods for similar simulations. On the basis of the theory of mechanics,the testing system is designed considering initial geostress and dynamic unloading. The system includes an impact unloading gear and in-situ stress loading equipment, and a designed three-link structure and the impact hammer can effectively realize the dynamic excavation of roadways.Meanwhile, a cyclic excavation similar simulation experiment on a deep roadway is conducted in a laboratory. The testing system and the relevant monitoring facilities are utilized, and the unloading effect inside the surrounding rock under the cyclic dynamic excavation is studied. Results show that the cyclic dynamic excavation causes significant unloading only in the nearby rock mass, and the unloading indicators show nonlinear changes.Moreover, when the lateral pressure coefficient is 1.2,the damage is concentrated on both roadsides due to the excavation unloading. Meanwhile, the damage gradually decays as the span increases.
文摘In this paper, design, re-design, and performance of a long-standing very deep excavation, which was originally planned to depth of 38 m, are presented. Over-digging was not planned in the original design,thus the reassessment was performed. Two main topics were followed: deepening to increase the maximum depth of an existent excavation from 38 m to 42.5 m, and feasibility for upgrading a predesigned support system from temporary to permanent support system. The geological investigations in the project site illustrated a type of stiff and cemented coarse-grained alluvium. An observational approach with additional geotechnical investigations and in situ tests was applied. Back analyses of stability of an unsupported access ramp, as well as deformation monitoring of walls, were used in order to review geotechnical design parameters that represent the full-scale behavior of the ground. Additional nails and soldier piles together with building mat foundation were implemented as a complementary lateral support in the retaining system. From an engineering point of view, by assuming a corrosion rate of 0.065 mm/a for existent rebars, according to chemical and electrical resistivity tests, the long-term performance of the revised retaining system was verified by static and pseudo-dynamic ultimate limit state analyses. Performance monitoring during the construction shows that the measured deformation is in the lower limit of the prediction.
基金funded by a Special Fund for Basic Research on Scientific Instruments of the National Natural Science Foundation of China(Grant No.41827807)the Study on Intelligent Technology for Tunnels Construction of Sichuan-Tibet Railway(Grant No.19-21-1).
文摘The dilation angle is the most commonly used parameter to study nonlinear post-peak dilatancy(PPD)behavior and simulate surrounding rock deformation;however,simplified or constant dilatancy models are often used in numerical calculations owing to their simple mathematical forms.This study developed a PPD model for rocks(rock masses)based on the Alejanoe-Alonso(A-A)dilatancy model.The developed model comprehensively reflects the influences of confining pressure(σ_(3))and plastic shear strain(γ^(p)),with the advantages of a simple mathematical form,while requiring fewer parameters and demonstrating a clear physical significance.The overall fitting accuracy of the PPD model for 11 different rocks was found to be higher than that of the A-A model,particularly for Witwatersrand quartzite and jointed granite.The applicability and reliability of the PPD model to jointed granites and different scaled Moura coals were also investigated,and the model was found to be more suitable for the soft and large-scale rocks,e.g.deep rock mass.The PPD model was also successfully applied in studying the mechanical response of a circular tunnel excavated in strain-softening rock mass,and the developed semi-analytical solution was compared and verified with existing analytical solutions.The sensitivities of the rock dilatancy to γ^(p) and σ_(3) showed significant spatial variabilities along the radial direction of the surrounding rock,and the dilation angle did not exhibit a monotonical increasing or decreasing law from the elasticeplastic boundary to the tunnel wall,thereby presenting the σ3-or γ^(p)-dominated differential effects of rock dilatancy.Tunnel deformation parabolically or exponentially increased with increasing in situ stress(buried depth).The developed PPD model is promising to conduct refined numerical and analytical analyses for deep tunneling,which produces extensive plastic deformation and exhibits significant nonlinear post-peak behavior.
文摘To predict rock burst in deep mining excavation in Linglong gold mine, systematical laboratory tests of mechanical properties of rock, in situ stress measurement and 3-D FEM analysis on energy distribution in rock mass surrounding deep mining rooms were carried out. According to various prediction criteria of rock burst, it is concluded that rock burst is liable to occur during deep mining excavation in the mine.
基金Foundation item： Project（2009-K3-2） supported by the Ministry of Housing and Urban-Rural Development of China
文摘A complete case of a deep excavation was explored. According to the practical working conditions, a 3D non-linear finite element procedure is used to simulate a deep excavation supported by the composite soil nailed wall with bored piles in soft soil. The modified cam clay model is employed as the constitutive relationship of the soil in the numerical simulation. Results from the numerical analysis are fitted well with the field data, which indicate that the research approach used is reliable. Based on the field data and numerical results of the deep excavation supported by four different patterns of the composite soil nailed wall, the significant corner effect is founded in the 3D deep excavation. If bored piles or soil anchors are considered in the composite soil nailed wall, they are beneficial to decreasing deformations and internal forces of bored piles, cement mixing piles, soil anchors, soil nailings and soil around the deep excavation. Besides, the effects due to bored piles are more significant than those deduced from soil anchors. All mentioned above prove that the composite soil nailed wall with bored piles is feasible in the deep excavation.
基金Projects(52090084, 51938008) supported by the National Natural Science Foundation of ChinaProject(2021T140474)supported by the China Postdoctoral Science Foundation。
文摘This paper presents an analytical procedure for massive water-sealing barriers(MWSBs)that are made of partially overlapped jet-grouting columns used for deep excavations,in which two crucial factors of the permeability and strength of jet-grouted materials are considered.Subsequently,a calculation example is analyzed and discussed.Results show that“tension failure”mechanism is a major concern for the structural failure during a design of MWSBs.The maximum allowable seepage discharge is a crucial index for the design of MWSBs,which has a significant influence on determining the design parameters of MWSBs.Compared with the design procedure for MWSBs that is proposed in this paper,the design parameters of MWSBs determined by the stability equilibrium and seepage stability equilibrium approaches are conservative due to the fact that it fails to consider the permeability or strength of jet-grouted materials that makes a contribution to the structural safety.Based on the proposed design method,the ranges of both the thickness and depth of MWSBs for a case history of subway excavation in Fuzhou,China were determined.Finally,field pumping test results showed that the water-tightness performance of MWSBs performed at site was quite well.
基金the financial support from the 111 Project(Grant No.B17009)the Liao Ning Revitalization Talents Program(Grant No.XLYCYSZX1902).
文摘The squeezing scenario in deep weak rock tunnels can hinder underground construction.However,due to the limitations of test technologies at hand,the real excavation stress path cannot be mimicked in the laboratory.Thus,the large deformation mechanism of deep weak rocks still remains unclear.For this,a true triaxial apparatus(TTA)to investigate the mechanical responses of deep weak rock under excavation stress paths in field and reveal the squeezing mechanism of deep tunnels is assembled and developed at Northeastern University,China.The apparatus can perform instantaneous unloading in s3 direction based on electromagnetism technology.In addition,uniform loading and deformation measurements can be carried out based on the proposed linked interlocking clamp and antifriction device,even if the sample has a strong dilatation deformation performance.Next,a bore trepanning is designed to capture noiseless acoustic emission(AE)signals for deep weak rock at a low threshold.Finally,two tests were are conducted using this instrument to preliminarily understand the failure and deformation features of deep weak rock based on fractured marble.The results show that the complete stressestrain curves of fractured marble have the characteristics of low strengths and large deformations,and the larger deformation and the more serious failure occur when the fractured marble enters the post-peak state after excavation.The results show that the developed apparatus is likely to be applicable for deep weak rock engineering.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(NRF-2023R1A2C1005950)Jana Shafi is supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2024/R/1445).
文摘Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.
基金gratefully the China Scholarship Council for providing a PhD Scholarship(CSC No.201906690049).
文摘The Fort d’Issy-Vanves-Clamart(FIVC)braced excavation in France is analyzed to provide insights into the geotechnical serviceability assessment of excavations at great depth within deterministic and probabilistic frameworks.The FIVC excavation is excavated at 32 m below the ground surface in Parisian sedimentary basin and a plane-strain finite element analysis is implemented to examine the wall deflections and ground surface settlements.A stochastic finite element method based on the polynomial chaos Kriging metamodel(MSFEM)is then proposed for the probabilistic analyses.Comparisons with field measurements and former studies are carried out.Several academic cases are then conducted to investigate the great-depth excavation stability regarding the maximum horizontal wall deflection and maximum ground surface settlement.The results indicate that the proposed MSFEM is effective for probabilistic analyses and can provide useful insights for the excavation design and construction.A sensitivity analysis for seven considered random parameters is then implemented.The soil friction angle at the excavation bottom layer is the most significant one for design.The soil-wall interaction effects on the excavation stability are also given.
基金Natural Science Foundation of China under Grant No.59979026.
文摘The studies of prediction and control of rockburst are presented during deep excavation in a gold mine in China. Firstly,the stress-relief method is used to obtain a vast amount of data about initial geostress. Secondly,3D FEM analyses of large scale are performed to find out the law of geostress distribution at various excavation levels of the mining area. At the same time,as an equally important measure,six typical kinds of rock blocks are sampled for the experimental study of rockburst tendency. According to the synthesized results of the theoretical and testing results,the methods of brittleness coefficient,brittle index and stress,and so on,are adopted. Finally,the evaluation on the possibility of rockbursts is given that may take place at the deep mining area and some effective measures are put forward to prevent and control the rockburst.
基金supported in part by the Guangxi Science and Technology Major Program under grant AA22068067the Guangxi Natural Science Foundation under grant 2023GXNSFAA026236 and 2024GXNSFDA010064the National Natural Science Foundation of China under project 62172119.
文摘As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are crucial.The key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time series.To address this issue,we propose an anomaly detection method based on distributed deep learning.Our method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete features.We use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation set.Our method can not only detect abnormal attacks but also locate the sensors that cause anomalies.We conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public datasets.The experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.
