Penetration testing plays a critical role in ensuring security in an increasingly interconnected world. Despite advancements in technology leading to smaller, more portable devices, penetration testing remains reliant...Penetration testing plays a critical role in ensuring security in an increasingly interconnected world. Despite advancements in technology leading to smaller, more portable devices, penetration testing remains reliant on traditional laptops and computers, which, while portable, lack true ultra-portability. This paper explores the potential impact of developing a dedicated, ultra-portable, low-cost device for on-the-go penetration testing. Such a device could replicate the core functionalities of advanced penetration testing tools, including those found in Kali Linux, within a compact form factor that fits easily into a pocket. By offering the convenience and portability akin to a smartphone, this innovative device could redefine the way penetration testers operate, enabling them to carry essential tools wherever they go and ensuring they are always prepared to conduct security assessments efficiently. This approach aims to revolutionize penetration testing by merging high functionality with unparalleled portability.展开更多
Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attack...Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target network.Existing RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the convergence.Moreover,most methods still rely on experts’knowledge.To address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic memory.First,the penetration testing problem is formally described in terms of reinforcement learning.To speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first time.Furthermore,the method offers an exploration strategy based on episodic memory to guide the agents in learning.The design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning efficiency.Ultimately,comparison experiments are carried out with the existing RL-based methods.The results reveal that the proposed method has better convergence performance.The running time is reduced by more than 20%.展开更多
In today’s era, where mobile devices have become an integral part of our daily lives, ensuring the security of mobile applications has become increasingly crucial. Mobile penetration testing, a specialized subfield w...In today’s era, where mobile devices have become an integral part of our daily lives, ensuring the security of mobile applications has become increasingly crucial. Mobile penetration testing, a specialized subfield within the realm of cybersecurity, plays a vital role in safeguarding mobile ecosystems against the ever-evolving landscape of threats. The ubiquity of mobile devices has made them a prime target for cybercriminals, and the data and functionality accessed through mobile applications make them valuable assets to protect. Mobile penetration testing is designed to identify vulnerabilities, weaknesses, and potential exploits within mobile applications and the devices themselves. Unlike traditional penetration testing, which often focuses on network and server security, mobile penetration testing zeroes in on the unique challenges posed by mobile platforms. Mobile penetration testing, a specialized field within cybersecurity, is an essential tool in the Cybersecurity specialists’ toolkit to protect mobile ecosystems from emerging threats. This article introduces mobile penetration testing, emphasizing its significance, including comprehensive learning labs for Android and iOS platforms, and highlighting how it distinctly differs from traditional penetration testing methodologies.展开更多
Geotechnical parameters derived from an intrusive cone penetration test(CPT)are used to asses mechanical properties to inform the design phase of infrastructure projects.However,local,in situ 1D measurements can fail ...Geotechnical parameters derived from an intrusive cone penetration test(CPT)are used to asses mechanical properties to inform the design phase of infrastructure projects.However,local,in situ 1D measurements can fail to capture 3D subsurface variations,which could mean less than optimal design decisions for foundation engineering.By coupling the localised measurements from CPTs with more global 3D measurements derived from geophysical methods,a higher fidelity 3D overview of the subsurface can be obtained.Machine Learning(ML)may offer an effective means to capture all types of geophysical information associated with CPT data at a site scale to build a 2D or 3D ground model.In this paper,we present an ML approach to build a 3D ground model of cone resistance and sleeve friction by combining several CPT measurements with Multichannel Analysis of Surface Waves(MASW)and Electrical Resistivity Tomography(ERT)data on a land site characterisation project in the United Arab Emirates(UAE).To avoid a potential overfitting problem inherent to the use of machine learning and a lack of data at certain locations,we explore the possibility of using a prior Geo-Statistical(GS)approach that attempts to constrain the overfitting process by“artificially”increasing the amount of input data.A sensitivity study is also performed on input features used to train the ML algorithm to better define the optimal combination of input features for the prediction.Our results showed that ERT data were not useful in capturing 3D variations of geotechnical properties compared to Vs due to the geographical location of the site(200 m east from the Oman Gulf)and the possible effect of saline water intrusion.Additionally,we demonstrate that the use of a prior GS phase could be a promising and interesting means to make the prediction of ground properties more robust,especially for this specific case study described in this paper.Looking ahead,better representation of the subsurface can lead to a number of benefits for stakeholders involved in developing assets.Better ground/geotechnical models mean better site calibration of design methods and fewer design assumptions for reliability-based design,creating an opportunity for value engineering in the form of lighter construction without compromising safety,shorter construction timelines,and reduced resource requirements.展开更多
The Fifth Generation of Mobile Communications for Railways(5G-R)brings significant opportunities for the rail industry.However,alongside the potential and benefits of the railway 5G network are complex security challe...The Fifth Generation of Mobile Communications for Railways(5G-R)brings significant opportunities for the rail industry.However,alongside the potential and benefits of the railway 5G network are complex security challenges.Ensuring the security and reliability of railway 5G networks is therefore essential.This paper presents a detailed examination of security assessment techniques for railway 5G networks,focusing on addressing the unique security challenges in this field.In this paper,various security requirements in railway 5G networks are analyzed,and specific processes and methods for conducting comprehensive security risk assessments are presented.This study provides a framework for securing railway 5G network development and ensuring its long-term sustainability.展开更多
Cone penetration testing(CPT)and its variant with pore pressure measurements(CPTu)are versatile tools that have been traditionally used for in situ geotechnical site investigations.These investigations are among the m...Cone penetration testing(CPT)and its variant with pore pressure measurements(CPTu)are versatile tools that have been traditionally used for in situ geotechnical site investigations.These investigations are among the most challenging yet indispensable tasks,providing a crucial reference for infrastructure planning,design and construction.However,data obtained through the CPT/CPTu testing often exhibit significant variability,even at closely spaced test points.This variability is primarily attributed to the complex mineral compositions and sedimentary process of the Quaternary sediments.Problems induced by the scattering data include the difficulties in estimating the shear strength of the sediments and determining the appropriate bearing stratum for pile foundations.In this paper,the conventional interpretation methods of the CPT/CPTu data are enhanced with sedimentary facies knowledge.The geotechnical investigation mainly involves 42 CPTu tests(39 essential data sets available)and 4 boring samples.Sediment types are interpreted from the CPTu data and calibrated by the nearby boring samples.Sedimentary facies are derived from the interpreted sequence stratigraphy,for which the interpretation skills are summarized in the form of characteristic curves of the CPTu data.Scattering distribution of the sediment types and their mechanical parameters are well explained by the sedimentary facies.The sediments are then categorized into a few groups by their sedimentary facies,resulting in reduced uncertainties and scattering in terms of shear strength.Bearing stratum of pile foundations is also suggested based on the sedimentary regulations.展开更多
Conventional empirical equations for estimating undrained shear strength(s_(u))from piezocone penetration test(CPTu)data,without incorporating soil physical properties,often lack the accuracy and robustness required f...Conventional empirical equations for estimating undrained shear strength(s_(u))from piezocone penetration test(CPTu)data,without incorporating soil physical properties,often lack the accuracy and robustness required for geotechnical site investigations.This study introduces a hybrid virus colony search(VCS)algorithm that integrates the standard VCS algorithm with a mutation-based search mechanism to develop high-performance XGBoost learning models to address this limitation.A dataset of 372 seismic CPTu and corresponding soil physical properties data from 26 geotechnical projects in Jiangs_(u)Province,China,was collected for model development.Comparative evaluations demonstrate that the proposed hybrid VCS-XGBoost model exhibits s_(u)perior performance compared to standard meta-heuristic algorithm-based XGBoost models.The res_(u)lts highlight that the consideration of soil physical properties significantly improves the predictive accuracy of s_(u),emphasizing the importance of considering additional soil information beyond CPTu data for accurate s_(u)estimation.展开更多
MICP(Microbially induced calcite precipitation),an environmentally friendly soil improvement technique,has great potential in ocean engineering due to its ability to promote the precipitation of calcium carbonate thro...MICP(Microbially induced calcite precipitation),an environmentally friendly soil improvement technique,has great potential in ocean engineering due to its ability to promote the precipitation of calcium carbonate through microbial activity to enhance the engineering properties of geomaterials.In this study,piezocone penetration test(CPTU)is used to evaluate the effectiveness of MICP treatment in calcareous sand.The change of physical properties(relative density D and total unit weight)of MICP treated calcareous sand is investigated by conducting CPTU on the geomaterials prepared in a series of mini calibration chambers(25 cm×50 cm).Results indicate that CPTU(tip stress,sleeve friction,and porewater pressure)measurements can be used to interpret the physical characteristics of calcareous sand treated with MICP under seawater conditions.Additionally,a relationship between CPTU measurements,physical parameters(relative density D,and total unit weight y)of MICP treated calcareous sand is proposed and calibrated.The findings of the research extend the implementation of in-situ testing techniques such as CPTU towards physical property evaluation of bio-treated geomaterials in ocean environment,and demonstrate the potential of scaling up MICP techniques for broader engineeringapplication.展开更多
With the increasing construction of port facilities,cross-sea bridges,and offshore engineering projects,uplift piles embedded in marine sedimentary soft soil are becoming increasingly necessary.The load-displacement c...With the increasing construction of port facilities,cross-sea bridges,and offshore engineering projects,uplift piles embedded in marine sedimentary soft soil are becoming increasingly necessary.The load-displacement curve of uplift piles is crucial for evaluating their uplift bearing characteristics,which facilitates the risk evaluation,design,and construction of large infrastructural supports.In this study,a load-displacement curve model based on piezocone penetration test(CPTU)data is proposed via the load transfer method.Experimental tests are conducted to analyze the uplift bearing characteristics and establish a correlation between the proposed model and CPTU data.The results of the proposed load-displacement curve are compared with the results from numerical simulations and those calculated by previous methods.The results show that the proposed curves appropriately evaluated the uplift bearing characteristics and improved the accuracy in comparison with previous methods.展开更多
The water drop penetration time(WDPT)test consists of placing water drops on a material's surface in order to evaluate how long it takes to penetrate the pores.It is used to evaluate the hydrophobicity of material...The water drop penetration time(WDPT)test consists of placing water drops on a material's surface in order to evaluate how long it takes to penetrate the pores.It is used to evaluate the hydrophobicity of materials.This study aims at investigating in more detail the soil-water interaction during the test,exposing its mechanism.For that,a model soil named Hamburg Sand was coated with a hydrophobic fluoropolymer and then a WDPT test was performed while computed tomography(CT)images were taken.Tomography experiments were performed at the P07 high-energy materials science(HEMS)beamline,operated by Helmholtz–Zentrum Hereon,at the storage ring PETRA III at the Deutsches Elektronen-Synchrotron(DESY)in Hamburg.Using synchrotron radiation,a tomogram can be obtained in about 10 min,way less time than regular laboratory X-ray sources usually owned by universities.The faster imaging enables the observation of the drop penetration during time and thus provides insight into the dynamics of the process.After that,digital discrete image correlation is performed to track the displacement of the grains throughout time.From the results one can observe that,as the drop is absorbed at the material's surface,the grains directly around the droplet base are dragged to the liquid-air interface around the drop,revealing grain kinematics during capillary interactions of the penetrating liquid and sand grains.展开更多
Ground improvement has been used on many construction sites to densify granular materials, in other word, to improve soil properties and reduce potential settlement. This work presents a case study of ground improveme...Ground improvement has been used on many construction sites to densify granular materials, in other word, to improve soil properties and reduce potential settlement. This work presents a case study of ground improvement using rapid impact compaction （RIC）. The research site comprises the construction of workshop and depots as part of railway development project at Batu Gajah-Ipoh, Malaysia. In-situ testing results show that the subsurface soil comprises mainly of sand and silty sand through the investigated depth extended to 10 m. Groundwater is approximately 0.5 m below the ground surface. Evaluation of improvement was based on the results of pre- and post-improvement cone penetration test （CPT）. Interpretation software has been used to infer soil properties. Load test was conducted to estimate soil settlement. It is found that the technique succeeds in improving soil properties namely the relative density increases from 45% to 70%, the friction angle of soil is increased by an average of 3°, and the soil settlement is reduced by 50%： The technique succeeds in improving soil properties to approximately 5.0 m in depth depending on soil uniformity with depth.展开更多
Soil liquefaction is one of the complex research topics in geotechnical engineering and engineering geology. Especially after the 1964 Niigata earthquake (Japan) induced many soil liquefaction incidents, a variety of ...Soil liquefaction is one of the complex research topics in geotechnical engineering and engineering geology. Especially after the 1964 Niigata earthquake (Japan) induced many soil liquefaction incidents, a variety of soil liquefaction studies were conducted and reported, including the liquefaction potential assessment methods utilizing the shear wave velocity (V<sub>s</sub>) or SPT-N profiles (SPT: standard penetration test). This study used the V<sub>s</sub> and SPT methods recommended by the National Center for Earthquake Engineering Research (NCEER) to examine which is more conservative according to the assessment results on 41 liquefiable soil layers at sites in two major cities in Taiwan. Statistical hypothesis testing was used to make the analysis more quantitative and objective. Based on three sets of hypothesis tests, it shows that the hypothesis—the SPT method is more conservative than the V<sub>s</sub> method—was not rejected on a 5% level of significance.展开更多
To explore the penetration resistance of calcareous sand media,penetration tests have been conducted in the velocity range of 200-1000 m/s using conical-nosed projectiles with a diameter of 14.5 mm.Further,a pseudo fl...To explore the penetration resistance of calcareous sand media,penetration tests have been conducted in the velocity range of 200-1000 m/s using conical-nosed projectiles with a diameter of 14.5 mm.Further,a pseudo fluid penetration model applicable to the penetration of rigid projectiles in sand media is established according to the approximate flow of compacted sand in the adjacent zone of penetration.The correlation between the impedance function of projectile-target interaction and the internal friction features of pseudo fluid is clarified,and the effects of sand density,cone angle of nose-shaped projectile,and dynamic hardness on the penetration depth are investigated.The results verify the feasibility,wide applicability,and much lower error(with respect to the experimental data)of the proposed model as compared to the Slepyan hydrodynamic model.展开更多
The interpretation of the cone penetration test(CPT)still relies largely on empirical correlations that have been predominantly developed in resource-intensive and time-consuming calibration chambers.This paper presen...The interpretation of the cone penetration test(CPT)still relies largely on empirical correlations that have been predominantly developed in resource-intensive and time-consuming calibration chambers.This paper presents a CPT virtual calibration chamber using deep learning(DL)approaches,which allow for the consideration of depth-dependent cone resistance profiles through the implementation of two proposed strategies:(1)depth-resistance mapping using a multilayer perceptron(MLP)and(2)sequence-to-sequence training using a long short-term memory(LSTM)neural network.Two DL models are developed to predict cone resistance profiles(qc)under various soil states and testing conditions,where Bayesian optimization(BO)is adopted to identify the optimal hyperparameters.Subsequently,the BO-MLP and BO-LSTM networks are trained using the available data from published datasets.The results show that the models with BO can effectively improve the prediction accuracy and efficiency of neural networks compared to those without BO.The two training strategies yielded comparable results in the testing set,and both can be used to reproduce the whole cone resistance profile.An extended comparison and validation of the prediction results are carried out against numerical results obtained from a coupled Eulerian-Lagrangian(CEL)model,demonstrating a high degree of agreement between the DL and CEL models.Ultimately,to demonstrate the usability of this new virtual calibration chamber,the predicted qc is used to enhance the preceding correlations with the relative density(Dr)of the sand.The improved correlation with superior generalization has an R^(2) of 82%when considering all data,and 89.6%when examining the pure experimental data.展开更多
Coal bursting liability refers to the mechanical property of the degree and possibility of coal burst.The bursting liability is important to evaluate coal burst in mining.In this paper,the needle penetration test was ...Coal bursting liability refers to the mechanical property of the degree and possibility of coal burst.The bursting liability is important to evaluate coal burst in mining.In this paper,the needle penetration test was carried out to determinate the coal bursting liability,and the empirical criterion of coal bursting liability was proposed.Moreover,the machine learning method was applied to coal bursting liability determination.Through analyzing the elastic strain energy release and failure time,the residual elastic strain energy release rate index K_(RE)was proposed to evaluate the coal bursting liability.According to the relationship between needle penetration index(NPI),K_(RE)and the critical value of K_(RE),the Needle Penetration Test-based Empirical Classification Criterion(NPT-ECC)was obtained.In addition,four machine learning classification models were constructed.After training and testing of the models,Needle Penetration Test-based Machine Learning Classification Model(NPT-MLCM)was proposed.The research results show that the accuracy of NPT-ECC is 6.66%higher than that of China National Standard Comprehensive Evaluation(CNSCE)according to verification of the coal fragment ejection ratio F.Gridsearch cross validation-extreme gradient boosting(GSCV-XGBoost)has the best prediction performance among all the models,and accuracy,Macro-Precision,Macro-Recall and Macro-F1-score of which were 86.67%,88.97%,87.50%and 87.37%.Based on this,the Needle Penetration Test-based GSCV-XGBoost(NPT-GSCV-XGBoost)was proposed.After comparative analysis and discussion,NPT-GSCV-XGBoost is superior to NPT-ECC and CNSCE in the comprehensive prediction ability.展开更多
Ensuring the reliability of pipe pile designs under earthquake loading necessitates an accurate determination of lateral displacement and bending moment,typically achieved through complex numerical modeling to address...Ensuring the reliability of pipe pile designs under earthquake loading necessitates an accurate determination of lateral displacement and bending moment,typically achieved through complex numerical modeling to address the intricacies of soil-pile interaction.Despite recent advancements in machine learning techniques,there is a persistent need to establish data-driven models that can predict these parameters without using numerical simulations due to the difficulties in conducting correct numerical simulations and the need for constitutive modelling parameters that are not readily available.This research presents novel lateral displacement and bending moment predictive models for closed and open-ended pipe piles,employing a Genetic Programming(GP)approach.Utilizing a soil dataset extracted from existing literature,comprising 392 data points for both pile types embedded in cohesionless soil and subjected to earthquake loading,the study intentionally limited input parameters to three features to enhance model simplicity:Standard Penetration Test(SPT)corrected blow count(N60),Peak Ground Acceleration(PGA),and pile slenderness ratio(L/D).Model performance was assessed via coefficient of determination(R^(2)),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE),with R^(2) values ranging from 0.95 to 0.99 for the training set,and from 0.92 to 0.98 for the testing set,which indicate of high accuracy of prediction.Finally,the study concludes with a sensitivity analysis,evaluating the influence of each input parameter across different pile types.展开更多
The number and creativity of side channel attacks have increased dramatically in recent years. Of particular interest are attacks leveraging power line communication to 1) gather information on power consumption from ...The number and creativity of side channel attacks have increased dramatically in recent years. Of particular interest are attacks leveraging power line communication to 1) gather information on power consumption from the victim and 2) exfiltrate data from compromised machines. Attack strategies of this nature on the greater power grid and building infrastructure levels have been shown to be a serious threat. This project further explores this concept of a novel attack vector by creating a new type of penetration testing tool: an USB power adapter capable of remote monitoring of device power consumption and communicating through powerline communications.展开更多
Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases reliability.Nonetheless,given the rapidly expanding scale of modern network infrastructure,the...Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases reliability.Nonetheless,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical application.In this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive scale.CLAP employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large networks.It also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between them.Experimental results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 hosts.Additionally,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.展开更多
The methodology of predicting pile shaft skin ultimate friction has been studied in a systematic way. In the light of that, the analysis of the pile shaft resistance for bored and cast in situ piles in cohesive soil...The methodology of predicting pile shaft skin ultimate friction has been studied in a systematic way. In the light of that, the analysis of the pile shaft resistance for bored and cast in situ piles in cohesive soils was carried out thoroughly in the basis of field performance data of 10 fully instrumented large diameter bored piles (LDBPs) used as the bridge foundation. The undrained strength index μ in term of cohesive soils was brought forward in allusion to the cohesive soils in the consistence plastic state, and can effectively combine the friction angle and the cohesion of cohesive soils in undrained condition. And that the classical ' α method' was modified much in effect to predict the pile shaft skin friction of LDBPs in cohesive soils. Furthermore, the approach of standard penetration test (SPT) N value used to estimate the pile shaft skin ultimate friction was analyzed, and the calculating formulae were established for LDBPs in clay and silt clay respectively.展开更多
文摘Penetration testing plays a critical role in ensuring security in an increasingly interconnected world. Despite advancements in technology leading to smaller, more portable devices, penetration testing remains reliant on traditional laptops and computers, which, while portable, lack true ultra-portability. This paper explores the potential impact of developing a dedicated, ultra-portable, low-cost device for on-the-go penetration testing. Such a device could replicate the core functionalities of advanced penetration testing tools, including those found in Kali Linux, within a compact form factor that fits easily into a pocket. By offering the convenience and portability akin to a smartphone, this innovative device could redefine the way penetration testers operate, enabling them to carry essential tools wherever they go and ensuring they are always prepared to conduct security assessments efficiently. This approach aims to revolutionize penetration testing by merging high functionality with unparalleled portability.
文摘Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target network.Existing RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the convergence.Moreover,most methods still rely on experts’knowledge.To address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic memory.First,the penetration testing problem is formally described in terms of reinforcement learning.To speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first time.Furthermore,the method offers an exploration strategy based on episodic memory to guide the agents in learning.The design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning efficiency.Ultimately,comparison experiments are carried out with the existing RL-based methods.The results reveal that the proposed method has better convergence performance.The running time is reduced by more than 20%.
文摘In today’s era, where mobile devices have become an integral part of our daily lives, ensuring the security of mobile applications has become increasingly crucial. Mobile penetration testing, a specialized subfield within the realm of cybersecurity, plays a vital role in safeguarding mobile ecosystems against the ever-evolving landscape of threats. The ubiquity of mobile devices has made them a prime target for cybercriminals, and the data and functionality accessed through mobile applications make them valuable assets to protect. Mobile penetration testing is designed to identify vulnerabilities, weaknesses, and potential exploits within mobile applications and the devices themselves. Unlike traditional penetration testing, which often focuses on network and server security, mobile penetration testing zeroes in on the unique challenges posed by mobile platforms. Mobile penetration testing, a specialized field within cybersecurity, is an essential tool in the Cybersecurity specialists’ toolkit to protect mobile ecosystems from emerging threats. This article introduces mobile penetration testing, emphasizing its significance, including comprehensive learning labs for Android and iOS platforms, and highlighting how it distinctly differs from traditional penetration testing methodologies.
文摘Geotechnical parameters derived from an intrusive cone penetration test(CPT)are used to asses mechanical properties to inform the design phase of infrastructure projects.However,local,in situ 1D measurements can fail to capture 3D subsurface variations,which could mean less than optimal design decisions for foundation engineering.By coupling the localised measurements from CPTs with more global 3D measurements derived from geophysical methods,a higher fidelity 3D overview of the subsurface can be obtained.Machine Learning(ML)may offer an effective means to capture all types of geophysical information associated with CPT data at a site scale to build a 2D or 3D ground model.In this paper,we present an ML approach to build a 3D ground model of cone resistance and sleeve friction by combining several CPT measurements with Multichannel Analysis of Surface Waves(MASW)and Electrical Resistivity Tomography(ERT)data on a land site characterisation project in the United Arab Emirates(UAE).To avoid a potential overfitting problem inherent to the use of machine learning and a lack of data at certain locations,we explore the possibility of using a prior Geo-Statistical(GS)approach that attempts to constrain the overfitting process by“artificially”increasing the amount of input data.A sensitivity study is also performed on input features used to train the ML algorithm to better define the optimal combination of input features for the prediction.Our results showed that ERT data were not useful in capturing 3D variations of geotechnical properties compared to Vs due to the geographical location of the site(200 m east from the Oman Gulf)and the possible effect of saline water intrusion.Additionally,we demonstrate that the use of a prior GS phase could be a promising and interesting means to make the prediction of ground properties more robust,especially for this specific case study described in this paper.Looking ahead,better representation of the subsurface can lead to a number of benefits for stakeholders involved in developing assets.Better ground/geotechnical models mean better site calibration of design methods and fewer design assumptions for reliability-based design,creating an opportunity for value engineering in the form of lighter construction without compromising safety,shorter construction timelines,and reduced resource requirements.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant No.2025JBXT010in part by NSFC under Grant No.62171021,in part by the Project of China State Railway Group under Grant No.N2024B004in part by ZTE IndustryUniversityInstitute Cooperation Funds under Grant No.l23L00010.
文摘The Fifth Generation of Mobile Communications for Railways(5G-R)brings significant opportunities for the rail industry.However,alongside the potential and benefits of the railway 5G network are complex security challenges.Ensuring the security and reliability of railway 5G networks is therefore essential.This paper presents a detailed examination of security assessment techniques for railway 5G networks,focusing on addressing the unique security challenges in this field.In this paper,various security requirements in railway 5G networks are analyzed,and specific processes and methods for conducting comprehensive security risk assessments are presented.This study provides a framework for securing railway 5G network development and ensuring its long-term sustainability.
基金supported by the National Natural Science Foundation of China(Grant Nos.42272328 and 52108356).
文摘Cone penetration testing(CPT)and its variant with pore pressure measurements(CPTu)are versatile tools that have been traditionally used for in situ geotechnical site investigations.These investigations are among the most challenging yet indispensable tasks,providing a crucial reference for infrastructure planning,design and construction.However,data obtained through the CPT/CPTu testing often exhibit significant variability,even at closely spaced test points.This variability is primarily attributed to the complex mineral compositions and sedimentary process of the Quaternary sediments.Problems induced by the scattering data include the difficulties in estimating the shear strength of the sediments and determining the appropriate bearing stratum for pile foundations.In this paper,the conventional interpretation methods of the CPT/CPTu data are enhanced with sedimentary facies knowledge.The geotechnical investigation mainly involves 42 CPTu tests(39 essential data sets available)and 4 boring samples.Sediment types are interpreted from the CPTu data and calibrated by the nearby boring samples.Sedimentary facies are derived from the interpreted sequence stratigraphy,for which the interpretation skills are summarized in the form of characteristic curves of the CPTu data.Scattering distribution of the sediment types and their mechanical parameters are well explained by the sedimentary facies.The sediments are then categorized into a few groups by their sedimentary facies,resulting in reduced uncertainties and scattering in terms of shear strength.Bearing stratum of pile foundations is also suggested based on the sedimentary regulations.
基金funded by the National Science Fund for Distinguished Young Scholars(Grant No.42225206)the National Key R&D Program of China(Grant No.2020YFC1807200)the National Natural Science Foundation of China(Grant No.42072299).
文摘Conventional empirical equations for estimating undrained shear strength(s_(u))from piezocone penetration test(CPTu)data,without incorporating soil physical properties,often lack the accuracy and robustness required for geotechnical site investigations.This study introduces a hybrid virus colony search(VCS)algorithm that integrates the standard VCS algorithm with a mutation-based search mechanism to develop high-performance XGBoost learning models to address this limitation.A dataset of 372 seismic CPTu and corresponding soil physical properties data from 26 geotechnical projects in Jiangs_(u)Province,China,was collected for model development.Comparative evaluations demonstrate that the proposed hybrid VCS-XGBoost model exhibits s_(u)perior performance compared to standard meta-heuristic algorithm-based XGBoost models.The res_(u)lts highlight that the consideration of soil physical properties significantly improves the predictive accuracy of s_(u),emphasizing the importance of considering additional soil information beyond CPTu data for accurate s_(u)estimation.
基金funded by the Tsinghua Shenzhen International Graduate School Research Startup Funds[item number:01030100009].
文摘MICP(Microbially induced calcite precipitation),an environmentally friendly soil improvement technique,has great potential in ocean engineering due to its ability to promote the precipitation of calcium carbonate through microbial activity to enhance the engineering properties of geomaterials.In this study,piezocone penetration test(CPTU)is used to evaluate the effectiveness of MICP treatment in calcareous sand.The change of physical properties(relative density D and total unit weight)of MICP treated calcareous sand is investigated by conducting CPTU on the geomaterials prepared in a series of mini calibration chambers(25 cm×50 cm).Results indicate that CPTU(tip stress,sleeve friction,and porewater pressure)measurements can be used to interpret the physical characteristics of calcareous sand treated with MICP under seawater conditions.Additionally,a relationship between CPTU measurements,physical parameters(relative density D,and total unit weight y)of MICP treated calcareous sand is proposed and calibrated.The findings of the research extend the implementation of in-situ testing techniques such as CPTU towards physical property evaluation of bio-treated geomaterials in ocean environment,and demonstrate the potential of scaling up MICP techniques for broader engineeringapplication.
基金supported by the China Postdoctoral Science Foundation(Grant No.2024M760734)National Science Fund for Distinguished Young Scholars(Grant No.42225206)the National Natural Science Foundation of China(Grant Nos.41877231 and 42072299).
文摘With the increasing construction of port facilities,cross-sea bridges,and offshore engineering projects,uplift piles embedded in marine sedimentary soft soil are becoming increasingly necessary.The load-displacement curve of uplift piles is crucial for evaluating their uplift bearing characteristics,which facilitates the risk evaluation,design,and construction of large infrastructural supports.In this study,a load-displacement curve model based on piezocone penetration test(CPTU)data is proposed via the load transfer method.Experimental tests are conducted to analyze the uplift bearing characteristics and establish a correlation between the proposed model and CPTU data.The results of the proposed load-displacement curve are compared with the results from numerical simulations and those calculated by previous methods.The results show that the proposed curves appropriately evaluated the uplift bearing characteristics and improved the accuracy in comparison with previous methods.
基金funding of this research by the German Research Foundation(Deutsche Forschungsgemeinschaft,DFG)in the framework of Research Training Group GRK 2462:Processes in natural and technical Particle-Fluid-Systems at Hamburg University of Technology(TUHH).
文摘The water drop penetration time(WDPT)test consists of placing water drops on a material's surface in order to evaluate how long it takes to penetrate the pores.It is used to evaluate the hydrophobicity of materials.This study aims at investigating in more detail the soil-water interaction during the test,exposing its mechanism.For that,a model soil named Hamburg Sand was coated with a hydrophobic fluoropolymer and then a WDPT test was performed while computed tomography(CT)images were taken.Tomography experiments were performed at the P07 high-energy materials science(HEMS)beamline,operated by Helmholtz–Zentrum Hereon,at the storage ring PETRA III at the Deutsches Elektronen-Synchrotron(DESY)in Hamburg.Using synchrotron radiation,a tomogram can be obtained in about 10 min,way less time than regular laboratory X-ray sources usually owned by universities.The faster imaging enables the observation of the drop penetration during time and thus provides insight into the dynamics of the process.After that,digital discrete image correlation is performed to track the displacement of the grains throughout time.From the results one can observe that,as the drop is absorbed at the material's surface,the grains directly around the droplet base are dragged to the liquid-air interface around the drop,revealing grain kinematics during capillary interactions of the penetrating liquid and sand grains.
基金Projects(RG148/12AET,RG086/10AET) supported by the UMRG,MalaysiaProject(PS05812010B) supported by the Post Graduate Research Fund,Malaysia
文摘Ground improvement has been used on many construction sites to densify granular materials, in other word, to improve soil properties and reduce potential settlement. This work presents a case study of ground improvement using rapid impact compaction （RIC）. The research site comprises the construction of workshop and depots as part of railway development project at Batu Gajah-Ipoh, Malaysia. In-situ testing results show that the subsurface soil comprises mainly of sand and silty sand through the investigated depth extended to 10 m. Groundwater is approximately 0.5 m below the ground surface. Evaluation of improvement was based on the results of pre- and post-improvement cone penetration test （CPT）. Interpretation software has been used to infer soil properties. Load test was conducted to estimate soil settlement. It is found that the technique succeeds in improving soil properties namely the relative density increases from 45% to 70%, the friction angle of soil is increased by an average of 3°, and the soil settlement is reduced by 50%： The technique succeeds in improving soil properties to approximately 5.0 m in depth depending on soil uniformity with depth.
文摘Soil liquefaction is one of the complex research topics in geotechnical engineering and engineering geology. Especially after the 1964 Niigata earthquake (Japan) induced many soil liquefaction incidents, a variety of soil liquefaction studies were conducted and reported, including the liquefaction potential assessment methods utilizing the shear wave velocity (V<sub>s</sub>) or SPT-N profiles (SPT: standard penetration test). This study used the V<sub>s</sub> and SPT methods recommended by the National Center for Earthquake Engineering Research (NCEER) to examine which is more conservative according to the assessment results on 41 liquefiable soil layers at sites in two major cities in Taiwan. Statistical hypothesis testing was used to make the analysis more quantitative and objective. Based on three sets of hypothesis tests, it shows that the hypothesis—the SPT method is more conservative than the V<sub>s</sub> method—was not rejected on a 5% level of significance.
基金funded by the National Natural Science Foundation of China(Grant No.12072371)Jiangsu Natural Science Foundation(Grant No.BK20221528)。
文摘To explore the penetration resistance of calcareous sand media,penetration tests have been conducted in the velocity range of 200-1000 m/s using conical-nosed projectiles with a diameter of 14.5 mm.Further,a pseudo fluid penetration model applicable to the penetration of rigid projectiles in sand media is established according to the approximate flow of compacted sand in the adjacent zone of penetration.The correlation between the impedance function of projectile-target interaction and the internal friction features of pseudo fluid is clarified,and the effects of sand density,cone angle of nose-shaped projectile,and dynamic hardness on the penetration depth are investigated.The results verify the feasibility,wide applicability,and much lower error(with respect to the experimental data)of the proposed model as compared to the Slepyan hydrodynamic model.
基金support from the National Natural Science Foundation of China(Grant No.52408356)the China Scholarship Council(CSC).
文摘The interpretation of the cone penetration test(CPT)still relies largely on empirical correlations that have been predominantly developed in resource-intensive and time-consuming calibration chambers.This paper presents a CPT virtual calibration chamber using deep learning(DL)approaches,which allow for the consideration of depth-dependent cone resistance profiles through the implementation of two proposed strategies:(1)depth-resistance mapping using a multilayer perceptron(MLP)and(2)sequence-to-sequence training using a long short-term memory(LSTM)neural network.Two DL models are developed to predict cone resistance profiles(qc)under various soil states and testing conditions,where Bayesian optimization(BO)is adopted to identify the optimal hyperparameters.Subsequently,the BO-MLP and BO-LSTM networks are trained using the available data from published datasets.The results show that the models with BO can effectively improve the prediction accuracy and efficiency of neural networks compared to those without BO.The two training strategies yielded comparable results in the testing set,and both can be used to reproduce the whole cone resistance profile.An extended comparison and validation of the prediction results are carried out against numerical results obtained from a coupled Eulerian-Lagrangian(CEL)model,demonstrating a high degree of agreement between the DL and CEL models.Ultimately,to demonstrate the usability of this new virtual calibration chamber,the predicted qc is used to enhance the preceding correlations with the relative density(Dr)of the sand.The improved correlation with superior generalization has an R^(2) of 82%when considering all data,and 89.6%when examining the pure experimental data.
基金supported by the National Natural Science Foundation of China(52225402 and U1910206).
文摘Coal bursting liability refers to the mechanical property of the degree and possibility of coal burst.The bursting liability is important to evaluate coal burst in mining.In this paper,the needle penetration test was carried out to determinate the coal bursting liability,and the empirical criterion of coal bursting liability was proposed.Moreover,the machine learning method was applied to coal bursting liability determination.Through analyzing the elastic strain energy release and failure time,the residual elastic strain energy release rate index K_(RE)was proposed to evaluate the coal bursting liability.According to the relationship between needle penetration index(NPI),K_(RE)and the critical value of K_(RE),the Needle Penetration Test-based Empirical Classification Criterion(NPT-ECC)was obtained.In addition,four machine learning classification models were constructed.After training and testing of the models,Needle Penetration Test-based Machine Learning Classification Model(NPT-MLCM)was proposed.The research results show that the accuracy of NPT-ECC is 6.66%higher than that of China National Standard Comprehensive Evaluation(CNSCE)according to verification of the coal fragment ejection ratio F.Gridsearch cross validation-extreme gradient boosting(GSCV-XGBoost)has the best prediction performance among all the models,and accuracy,Macro-Precision,Macro-Recall and Macro-F1-score of which were 86.67%,88.97%,87.50%and 87.37%.Based on this,the Needle Penetration Test-based GSCV-XGBoost(NPT-GSCV-XGBoost)was proposed.After comparative analysis and discussion,NPT-GSCV-XGBoost is superior to NPT-ECC and CNSCE in the comprehensive prediction ability.
文摘Ensuring the reliability of pipe pile designs under earthquake loading necessitates an accurate determination of lateral displacement and bending moment,typically achieved through complex numerical modeling to address the intricacies of soil-pile interaction.Despite recent advancements in machine learning techniques,there is a persistent need to establish data-driven models that can predict these parameters without using numerical simulations due to the difficulties in conducting correct numerical simulations and the need for constitutive modelling parameters that are not readily available.This research presents novel lateral displacement and bending moment predictive models for closed and open-ended pipe piles,employing a Genetic Programming(GP)approach.Utilizing a soil dataset extracted from existing literature,comprising 392 data points for both pile types embedded in cohesionless soil and subjected to earthquake loading,the study intentionally limited input parameters to three features to enhance model simplicity:Standard Penetration Test(SPT)corrected blow count(N60),Peak Ground Acceleration(PGA),and pile slenderness ratio(L/D).Model performance was assessed via coefficient of determination(R^(2)),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE),with R^(2) values ranging from 0.95 to 0.99 for the training set,and from 0.92 to 0.98 for the testing set,which indicate of high accuracy of prediction.Finally,the study concludes with a sensitivity analysis,evaluating the influence of each input parameter across different pile types.
文摘The number and creativity of side channel attacks have increased dramatically in recent years. Of particular interest are attacks leveraging power line communication to 1) gather information on power consumption from the victim and 2) exfiltrate data from compromised machines. Attack strategies of this nature on the greater power grid and building infrastructure levels have been shown to be a serious threat. This project further explores this concept of a novel attack vector by creating a new type of penetration testing tool: an USB power adapter capable of remote monitoring of device power consumption and communicating through powerline communications.
基金supported by te Key Research Project of Zhejiang Lab(No.2021PB0AV02)。
文摘Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases reliability.Nonetheless,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical application.In this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive scale.CLAP employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large networks.It also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between them.Experimental results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 hosts.Additionally,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.
文摘The methodology of predicting pile shaft skin ultimate friction has been studied in a systematic way. In the light of that, the analysis of the pile shaft resistance for bored and cast in situ piles in cohesive soils was carried out thoroughly in the basis of field performance data of 10 fully instrumented large diameter bored piles (LDBPs) used as the bridge foundation. The undrained strength index μ in term of cohesive soils was brought forward in allusion to the cohesive soils in the consistence plastic state, and can effectively combine the friction angle and the cohesion of cohesive soils in undrained condition. And that the classical ' α method' was modified much in effect to predict the pile shaft skin friction of LDBPs in cohesive soils. Furthermore, the approach of standard penetration test (SPT) N value used to estimate the pile shaft skin ultimate friction was analyzed, and the calculating formulae were established for LDBPs in clay and silt clay respectively.
