The location and geometry of large-scale asperity present at the foundation of concrete gravity dams and buttress dams affect the shear resistance of the concrete-rock interface.However,the parameters describing the f...The location and geometry of large-scale asperity present at the foundation of concrete gravity dams and buttress dams affect the shear resistance of the concrete-rock interface.However,the parameters describing the frictional resistance of the interface usually do not account for these asperities.This could result in an underestimate of the peak shear stre ngth,which leads to significantly conservative design for new dams or unnecessary stability enhancing measures for existing ones.The aim of this work was to investigate the effect of the location of first-order asperity on the peak shear strength of a concrete-rock interface under eccentric load and the model discrepancy associated with the commonly used rigid body methods for calculating the factor of safety(FS)against sliding.For this,a series of direct and eccentric shear tests under constant normal load(CNL)was carried out on concrete-rock samples.The peak shear strengths measured in the tests were compared in terms of asperity location and with the predicted values from analytical rigid body methods.The results showed that the large-scale asperity under eccentric load significantly affected the peak shear strength.Furthermore,unlike the conventional assumption of sliding or shear failure of an asperity in direct shear,under the effect of eccentric shear load,a tensile failure in the rock or in the concrete could occur,resulting in a lower shear strength compared with that of direct shear tests.These results could have important implications for assessment of the FS against sliding failure in the concrete-rock interface.展开更多
Hierarchical Federated Learning(HFL)extends traditional Federated Learning(FL)by introducing multi-level aggregation in which model updates pass through clients,edge servers,and a global server.While this hierarchical...Hierarchical Federated Learning(HFL)extends traditional Federated Learning(FL)by introducing multi-level aggregation in which model updates pass through clients,edge servers,and a global server.While this hierarchical structure enhances scalability,it also increases vulnerability to adversarial attacks—such as data poisoning and model poisoning—that disrupt learning by introducing discrepancies at the edge server level.These discrepancies propagate through aggregation,affecting model consistency and overall integrity.Existing studies on adversarial behaviour in FL primarily rely on single-metric approaches—such as cosine similarity or Euclidean distance—to assess model discrepancies and filter out anomalous updates.However,these methods fail to capture the diverse ways adversarial attacks influence model updates,particularly in highly heterogeneous data environments and hierarchical structures.Attackers can exploit the limitations of single-metric defences by crafting updates that seem benign under one metric while remaining anomalous under another.Moreover,prior studies have not systematically analysed how model discrepancies evolve over time,vary across regions,or affect clustering structures in HFL architectures.To address these limitations,we propose the Model Discrepancy Score(MDS),a multi-metric framework that integrates Dissimilarity,Distance,Uncorrelation,and Divergence to provide a comprehensive analysis of how adversarial activity affects model discrepancies.Through temporal,spatial,and clustering analyses,we examine how attacks affect model discrepancies at the edge server level in 3LHFL and 4LHFL architectures and evaluate MDS’s ability to distinguish between benign and malicious servers.Our results show that while 4LHFL effectively mitigates discrepancies in regional attack scenarios,it struggles with distributed attacks due to additional aggregation layers that obscure distinguishable discrepancy patterns over time,across regions,and within clustering structures.Factors influencing detection include data heterogeneity,attack sophistication,and hierarchical aggregation depth.These findings highlight the limitations of single-metric approaches and emphasize the need for multi-metric strategies such as MDS to enhance HFL security.展开更多
基金funded by the Research Council of Norway(Grant No.244029)。
文摘The location and geometry of large-scale asperity present at the foundation of concrete gravity dams and buttress dams affect the shear resistance of the concrete-rock interface.However,the parameters describing the frictional resistance of the interface usually do not account for these asperities.This could result in an underestimate of the peak shear stre ngth,which leads to significantly conservative design for new dams or unnecessary stability enhancing measures for existing ones.The aim of this work was to investigate the effect of the location of first-order asperity on the peak shear strength of a concrete-rock interface under eccentric load and the model discrepancy associated with the commonly used rigid body methods for calculating the factor of safety(FS)against sliding.For this,a series of direct and eccentric shear tests under constant normal load(CNL)was carried out on concrete-rock samples.The peak shear strengths measured in the tests were compared in terms of asperity location and with the predicted values from analytical rigid body methods.The results showed that the large-scale asperity under eccentric load significantly affected the peak shear strength.Furthermore,unlike the conventional assumption of sliding or shear failure of an asperity in direct shear,under the effect of eccentric shear load,a tensile failure in the rock or in the concrete could occur,resulting in a lower shear strength compared with that of direct shear tests.These results could have important implications for assessment of the FS against sliding failure in the concrete-rock interface.
基金supported by the Technical and Vocational Training Corporation(TVTC)through the Saudi Arabian Culture Bureau(SACB)in the United Kingdom and the EPSRC-funded project National Edge AI Hub for Real Data:Edge Intelligence for Cyber-disturbances and Data Quality(EP/Y028813/1).
文摘Hierarchical Federated Learning(HFL)extends traditional Federated Learning(FL)by introducing multi-level aggregation in which model updates pass through clients,edge servers,and a global server.While this hierarchical structure enhances scalability,it also increases vulnerability to adversarial attacks—such as data poisoning and model poisoning—that disrupt learning by introducing discrepancies at the edge server level.These discrepancies propagate through aggregation,affecting model consistency and overall integrity.Existing studies on adversarial behaviour in FL primarily rely on single-metric approaches—such as cosine similarity or Euclidean distance—to assess model discrepancies and filter out anomalous updates.However,these methods fail to capture the diverse ways adversarial attacks influence model updates,particularly in highly heterogeneous data environments and hierarchical structures.Attackers can exploit the limitations of single-metric defences by crafting updates that seem benign under one metric while remaining anomalous under another.Moreover,prior studies have not systematically analysed how model discrepancies evolve over time,vary across regions,or affect clustering structures in HFL architectures.To address these limitations,we propose the Model Discrepancy Score(MDS),a multi-metric framework that integrates Dissimilarity,Distance,Uncorrelation,and Divergence to provide a comprehensive analysis of how adversarial activity affects model discrepancies.Through temporal,spatial,and clustering analyses,we examine how attacks affect model discrepancies at the edge server level in 3LHFL and 4LHFL architectures and evaluate MDS’s ability to distinguish between benign and malicious servers.Our results show that while 4LHFL effectively mitigates discrepancies in regional attack scenarios,it struggles with distributed attacks due to additional aggregation layers that obscure distinguishable discrepancy patterns over time,across regions,and within clustering structures.Factors influencing detection include data heterogeneity,attack sophistication,and hierarchical aggregation depth.These findings highlight the limitations of single-metric approaches and emphasize the need for multi-metric strategies such as MDS to enhance HFL security.
