The discrete element method (DEM) was used to simulate the flow characteristic and strength characteristic of the conditioned sands in the earth pressure balance (EPB) tunneling. In the laboratory the conditioned sand...The discrete element method (DEM) was used to simulate the flow characteristic and strength characteristic of the conditioned sands in the earth pressure balance (EPB) tunneling. In the laboratory the conditioned sands were reproduced and the slump test and the direct shear test of the conditioned sands were implemented. A DEM equivalent model that can simulate the macro mechanical characteristic of the conditioned sands was proposed,and the corresponding numerical models of the slump test and the shear test were established. By selecting proper DEM model parameters,the errors of the slump values between the simulation results and the test results are in the range of 10.3%-14.3%,and the error of the curves between the shear displacement and the shear stress calculated with the DEM simulation is 4.68%-16.5% compared with that of the laboratory direct shear test. This illustrates that the proposed DEM equivalent model can approximately simulate the mechanical characteristics of the conditioned sands,which provides the basis for further simulation of the interaction between the conditioned soil and the chamber pressure system of the EPB machine.展开更多
Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Ext...Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.展开更多
Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear failure.Existing design methods often exhibit significant bias and varia...Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear failure.Existing design methods often exhibit significant bias and variability.Accurate estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and management.This study introduces a novel computation method,the jellyfish-least square support vector machine(JS-LSSVR)hybrid model,to predict punching shear strength.By combining machine learning(LSSVR)with jellyfish swarm(JS)intelligence,this hybrid model ensures precise and reliable predictions.The model’s development utilizes a real-world experimental data set.Comparison with seven established optimizers,including artificial bee colony(ABC),differential evolution(DE),genetic algorithm(GA),and others,as well as existing machine learning(ML)-based models and design codes,validates the superiority of the JS-LSSVR hybrid model.This innovative approach significantly enhances prediction accuracy,providing valuable support for civil engineers in estimating RC flat slab punching shear strength.展开更多
Shear logs,also known as shear velocity logs,are used for various types of seismic analysis,such as determining the relationship between amplitude variation with offset(AVO)and interpreting multiple types of seismic d...Shear logs,also known as shear velocity logs,are used for various types of seismic analysis,such as determining the relationship between amplitude variation with offset(AVO)and interpreting multiple types of seismic data.This log is an important tool for analyzing the properties of rocks and interpreting seismic data to identify potential areas of oil and gas reserves.However,these logs are often not collected due to cost constraints or poor borehole conditions possibly leading to poor data quality,though there are various approaches in practice for estimating shear wave velocity.In this study,a detailed review of the recent advances in the various techniques used to measure shear wave(S-wave)velocity is carried out.These techniques include direct and indirect measurement,determination of empirical relationships between S-wave velocity and other parameters,machine learning,and rock physics models.Therefore,this study creates a collection of employed techniques,enhancing the existing knowledge of this significant topic and offering a progressive approach for practical implementation in the field.展开更多
Joints shear strength is a critical parameter during the design and construction of geotechnical engineering structures.The prevailing models mostly adopt the form of empirical functions,employing mathematical regress...Joints shear strength is a critical parameter during the design and construction of geotechnical engineering structures.The prevailing models mostly adopt the form of empirical functions,employing mathematical regression techniques to represent experimental data.As an alternative approach,this paper proposes a new integrated intelligent computing paradigm that aims to predict joints shear strength.Five metaheuristic optimization algorithms,including the chameleon swarm algorithm(CSA),slime mold algorithm,transient search optimization algorithm,equilibrium optimizer and social network search algorithm,were employed to enhance the performance of the multilayered perception(MLP)model.Efficiency comparisons were conducted between the proposed CSA-MLP model and twelve classical models,employing statistical indicators such as root mean square error(RMSE),correlation coefficient(R2),mean absolute error(MAE),and variance accounted for(VAF)to evaluate the performance of each model.The sensitivity analysis of parameters that impact joints shear strength was conducted.Finally,the feasibility and limitations of this study were discussed.The results revealed that,in comparison to other models,the CSA-MLP model exhibited the most appropriate performance in terms of R2(0.88),RMSE(0.19),MAE(0.15),and VAF(90.32%)values.The result of sensitivity analysis showed that the normal stress and the joint roughness coefficient were the most critical factors influencing joints shear strength.This paper presented an efficacious attempt toward swift prediction of joints shear strength,thus avoiding the need for costly in-site and laboratory tests.展开更多
BACKGROUND Hepatitis C virus(HCV)infection progresses through various phases,starting with inflammation and ending with hepatocellular carcinoma.There are several invasive and non-invasive methods to diagnose chronic ...BACKGROUND Hepatitis C virus(HCV)infection progresses through various phases,starting with inflammation and ending with hepatocellular carcinoma.There are several invasive and non-invasive methods to diagnose chronic HCV infection.The invasive methods have their benefits but are linked to morbidity and complications.Thus,it is important to analyze the potential of non-invasive methods as an alternative.Shear wave elastography(SWE)is a non-invasive imaging tool widely validated in clinical and research studies as a surrogate marker of liver fibrosis.Liver fibrosis determination by invasive liver biopsy and non-invasive SWE agree closely in clinical studies and therefore both are gold standards.AIM To analyzed the diagnostic efficacy of non-invasive indices[serum fibronectin,aspartate aminotransferase to platelet ratio index(APRI),alanine aminotransferase ratio(AAR),and fibrosis-4(FIB-4)]in relation to SWE.We have used an Artificial Intelligence method to predict the severity of liver fibrosis and uncover the complex relationship between non-invasive indices and fibrosis severity.METHODS We have conducted a hospital-based study considering 100 untreated patients detected as HCV positive using a quantitative Real-Time Polymerase Chain Reaction assay.We performed statistical and probabilistic analyses to determine the relationship between non-invasive indices and the severity of fibrosis.We also used standard diagnostic methods to measure the diagnostic accuracy for all the subjects.RESULTS The results of our study showed that fibronectin is a highly accurate diagnostic tool for predicting fibrosis stages(mild,moderate,and severe).This was based on its sensitivity(100%,92.2%,96.2%),specificity(96%,100%,98.6%),Youden’s index(0.960,0.922,0.948),area under receiver operating characteristic curve(0.999,0.993,0.922),and Likelihood test(LR+>10 and LR-<0.1).Additionally,our Bayesian Network analysis revealed that fibronectin(>200),AAR(>1),APRI(>3),and FIB-4(>4)were all strongly associated with patients who had severe fibr展开更多
Shear deformation mechanisms of diamond-like carbon(DLC)are commonly unclear since its thickness of several micrometers limits the detailed analysis of its microstructural evolution and mechanical performance,which fu...Shear deformation mechanisms of diamond-like carbon(DLC)are commonly unclear since its thickness of several micrometers limits the detailed analysis of its microstructural evolution and mechanical performance,which further influences the improvement of the friction and wear performance of DLC.This study aims to investigate this issue utilizing molecular dynamics simulation and machine learning(ML)techniques.It is indicated that the changes in the mechanical properties of DLC are mainly due to the expansion and reduction of sp3 networks,causing the stick-slip patterns in shear force.In addition,cluster analysis showed that the sp2-sp3 transitions arise in the stick stage,while the sp3-sp2 transitions occur in the slip stage.In order to analyze the mechanisms governing the bond breaking/re-formation in these transitions,the Random Forest(RF)model in ML identifies that the kinetic energies of sp3 atoms and their velocities along the loading direction have the highest influence.This is because high kinetic energies of atoms can exacerbate the instability of the bonding state and increase the probability of bond breaking/re-formation.Finally,the RF model finds that the shear force of DLC is highly correlated to its potential energy,with less correlation to its content of sp3 atoms.Since the changes in potential energy are caused by the variances in the content of sp3 atoms and localized strains,potential energy is an ideal parameter to evaluate the shear deformation of DLC.The results can enhance the understanding of the shear deformation of DLC and support the improvement of its frictional and wear performance.展开更多
Based on four reanalysis datasets including CMA-RA,ERA5,ERA-Interim,and FNL,this paper proposes an improved intelligent method for shear line identification by introducing a second-order zonal-wind shear.Climatic char...Based on four reanalysis datasets including CMA-RA,ERA5,ERA-Interim,and FNL,this paper proposes an improved intelligent method for shear line identification by introducing a second-order zonal-wind shear.Climatic characteristics of shear lines and related rainstorms over the Southern Yangtze River Valley(SYRV)during the summers(June-August)from 2008 to 2018 are then analyzed by using two types of unsupervised machine learning algorithm,namely the t-distributed stochastic neighbor embedding method(t-SNE)and the k-means clustering method.The results are as follows:(1)The reproducibility of the 850 hPa wind fields over the SYRV using China’s reanalysis product CMARA is superior to that of European and American products including ERA5,ERA-Interim,and FNL.(2)Theory and observations indicate that the introduction of a second-order zonal-wind shear criterion can effectively eliminate the continuous cyclonic curvature of the wind field and identify shear lines with significant discontinuities.(3)The occurrence frequency of shear lines appearing in the daytime and nighttime is almost equal,but the intensity and the accompanying rainstorm have a clear diurnal variation:they are significantly stronger during daytime than those at nighttime.(4)Half(47%)of the shear lines can cause short-duration rainstorms(≥20 mm(3h)^(-1)),and shear line rainstorms account for one-sixth(16%)of the total summer short-duration rainstorms.Rainstorms caused by shear lines are significantly stronger than that caused by other synoptic forcing.(5)Under the influence of stronger water vapor transport and barotropic instability,shear lines and related rainstorms in the north and middle of the SYRV are stronger than those in the south.展开更多
The purpose of this study is the accurate prediction of undrained shear strength using Standard Penetration Test results and soil consistency indices,such as water content and Atterberg limits.With this study,along wi...The purpose of this study is the accurate prediction of undrained shear strength using Standard Penetration Test results and soil consistency indices,such as water content and Atterberg limits.With this study,along with the conventional methods of simple and multiple linear regression models,three machine learning algorithms,random forest,gradient boosting and stacked models,are developed for prediction of undrained shear strength.These models are employed on a relatively large data set from different projects around Turkey covering 230 observations.As an improvement over the available studies in literature,this study utilizes correct statistical analyses techniques on a relatively large database,such as using a train/test split on the data set to avoid overfitting of the developed models.Furthermore,the validity and consistency of the prediction results are ensured with the correct use of statistical measures like p-value and cross-validation which were missing in previous studies.To compare the performances of the models developed in this study with the prior ones existing in literature,all models were applied on the test data set and their performances are evaluated in terms of the resulting root mean squared error(RMSE)values and coefficient of determination(R^2).Accordingly,the models developed in this study demonstrate superior prediction capabilities compared to all of the prior studies.Moreover,to facilitate the use of machine learning algorithms for prediction purposes,entire source code prepared for this study and the collected data set are provided as supplements of this study.展开更多
基金Project (2007CB714006) supported by the National Basic Research Program of China
文摘The discrete element method (DEM) was used to simulate the flow characteristic and strength characteristic of the conditioned sands in the earth pressure balance (EPB) tunneling. In the laboratory the conditioned sands were reproduced and the slump test and the direct shear test of the conditioned sands were implemented. A DEM equivalent model that can simulate the macro mechanical characteristic of the conditioned sands was proposed,and the corresponding numerical models of the slump test and the shear test were established. By selecting proper DEM model parameters,the errors of the slump values between the simulation results and the test results are in the range of 10.3%-14.3%,and the error of the curves between the shear displacement and the shear stress calculated with the DEM simulation is 4.68%-16.5% compared with that of the laboratory direct shear test. This illustrates that the proposed DEM equivalent model can approximately simulate the mechanical characteristics of the conditioned sands,which provides the basis for further simulation of the interaction between the conditioned soil and the chamber pressure system of the EPB machine.
基金the University of Transport Technology under grant number DTTD2022-12.
文摘Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.
基金Acknowledgements This research was supported by the Research Program funded by Seoul National University of Science and Technology(SeoulTech).
文摘Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear failure.Existing design methods often exhibit significant bias and variability.Accurate estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and management.This study introduces a novel computation method,the jellyfish-least square support vector machine(JS-LSSVR)hybrid model,to predict punching shear strength.By combining machine learning(LSSVR)with jellyfish swarm(JS)intelligence,this hybrid model ensures precise and reliable predictions.The model’s development utilizes a real-world experimental data set.Comparison with seven established optimizers,including artificial bee colony(ABC),differential evolution(DE),genetic algorithm(GA),and others,as well as existing machine learning(ML)-based models and design codes,validates the superiority of the JS-LSSVR hybrid model.This innovative approach significantly enhances prediction accuracy,providing valuable support for civil engineers in estimating RC flat slab punching shear strength.
文摘Shear logs,also known as shear velocity logs,are used for various types of seismic analysis,such as determining the relationship between amplitude variation with offset(AVO)and interpreting multiple types of seismic data.This log is an important tool for analyzing the properties of rocks and interpreting seismic data to identify potential areas of oil and gas reserves.However,these logs are often not collected due to cost constraints or poor borehole conditions possibly leading to poor data quality,though there are various approaches in practice for estimating shear wave velocity.In this study,a detailed review of the recent advances in the various techniques used to measure shear wave(S-wave)velocity is carried out.These techniques include direct and indirect measurement,determination of empirical relationships between S-wave velocity and other parameters,machine learning,and rock physics models.Therefore,this study creates a collection of employed techniques,enhancing the existing knowledge of this significant topic and offering a progressive approach for practical implementation in the field.
基金This paper gets its funding from Projects(42277175)supported by National Natural Science Foundation of China,Project(2023JJ30657)+2 种基金supported by Hunan Provincial Natural Science Foundation of China and the National Key Research,Hunan Provincial Department of natural resources geological exploration project(BSDZSB43202403)The First National Natural Disaster Comprehensive Risk Survey in Hunan Province(2022-70the National Key Research and Development Program of China-2023 Key Special Project(No.2023YFC2907400).
文摘Joints shear strength is a critical parameter during the design and construction of geotechnical engineering structures.The prevailing models mostly adopt the form of empirical functions,employing mathematical regression techniques to represent experimental data.As an alternative approach,this paper proposes a new integrated intelligent computing paradigm that aims to predict joints shear strength.Five metaheuristic optimization algorithms,including the chameleon swarm algorithm(CSA),slime mold algorithm,transient search optimization algorithm,equilibrium optimizer and social network search algorithm,were employed to enhance the performance of the multilayered perception(MLP)model.Efficiency comparisons were conducted between the proposed CSA-MLP model and twelve classical models,employing statistical indicators such as root mean square error(RMSE),correlation coefficient(R2),mean absolute error(MAE),and variance accounted for(VAF)to evaluate the performance of each model.The sensitivity analysis of parameters that impact joints shear strength was conducted.Finally,the feasibility and limitations of this study were discussed.The results revealed that,in comparison to other models,the CSA-MLP model exhibited the most appropriate performance in terms of R2(0.88),RMSE(0.19),MAE(0.15),and VAF(90.32%)values.The result of sensitivity analysis showed that the normal stress and the joint roughness coefficient were the most critical factors influencing joints shear strength.This paper presented an efficacious attempt toward swift prediction of joints shear strength,thus avoiding the need for costly in-site and laboratory tests.
文摘BACKGROUND Hepatitis C virus(HCV)infection progresses through various phases,starting with inflammation and ending with hepatocellular carcinoma.There are several invasive and non-invasive methods to diagnose chronic HCV infection.The invasive methods have their benefits but are linked to morbidity and complications.Thus,it is important to analyze the potential of non-invasive methods as an alternative.Shear wave elastography(SWE)is a non-invasive imaging tool widely validated in clinical and research studies as a surrogate marker of liver fibrosis.Liver fibrosis determination by invasive liver biopsy and non-invasive SWE agree closely in clinical studies and therefore both are gold standards.AIM To analyzed the diagnostic efficacy of non-invasive indices[serum fibronectin,aspartate aminotransferase to platelet ratio index(APRI),alanine aminotransferase ratio(AAR),and fibrosis-4(FIB-4)]in relation to SWE.We have used an Artificial Intelligence method to predict the severity of liver fibrosis and uncover the complex relationship between non-invasive indices and fibrosis severity.METHODS We have conducted a hospital-based study considering 100 untreated patients detected as HCV positive using a quantitative Real-Time Polymerase Chain Reaction assay.We performed statistical and probabilistic analyses to determine the relationship between non-invasive indices and the severity of fibrosis.We also used standard diagnostic methods to measure the diagnostic accuracy for all the subjects.RESULTS The results of our study showed that fibronectin is a highly accurate diagnostic tool for predicting fibrosis stages(mild,moderate,and severe).This was based on its sensitivity(100%,92.2%,96.2%),specificity(96%,100%,98.6%),Youden’s index(0.960,0.922,0.948),area under receiver operating characteristic curve(0.999,0.993,0.922),and Likelihood test(LR+>10 and LR-<0.1).Additionally,our Bayesian Network analysis revealed that fibronectin(>200),AAR(>1),APRI(>3),and FIB-4(>4)were all strongly associated with patients who had severe fibr
基金The simulations in this work are supported by the High-Performance Computing Center of Central South University.
文摘Shear deformation mechanisms of diamond-like carbon(DLC)are commonly unclear since its thickness of several micrometers limits the detailed analysis of its microstructural evolution and mechanical performance,which further influences the improvement of the friction and wear performance of DLC.This study aims to investigate this issue utilizing molecular dynamics simulation and machine learning(ML)techniques.It is indicated that the changes in the mechanical properties of DLC are mainly due to the expansion and reduction of sp3 networks,causing the stick-slip patterns in shear force.In addition,cluster analysis showed that the sp2-sp3 transitions arise in the stick stage,while the sp3-sp2 transitions occur in the slip stage.In order to analyze the mechanisms governing the bond breaking/re-formation in these transitions,the Random Forest(RF)model in ML identifies that the kinetic energies of sp3 atoms and their velocities along the loading direction have the highest influence.This is because high kinetic energies of atoms can exacerbate the instability of the bonding state and increase the probability of bond breaking/re-formation.Finally,the RF model finds that the shear force of DLC is highly correlated to its potential energy,with less correlation to its content of sp3 atoms.Since the changes in potential energy are caused by the variances in the content of sp3 atoms and localized strains,potential energy is an ideal parameter to evaluate the shear deformation of DLC.The results can enhance the understanding of the shear deformation of DLC and support the improvement of its frictional and wear performance.
基金Open Project Fund of Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction,CMA(J202009)Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province(SZKT202005)+1 种基金Innovation and Development Project of China Meteorological Administration(CXFZ2021J020)Key Projects of Hunan Meteorological Service(XQKJ21A003,XQKJ21A004,XQKJ22A004)。
文摘Based on four reanalysis datasets including CMA-RA,ERA5,ERA-Interim,and FNL,this paper proposes an improved intelligent method for shear line identification by introducing a second-order zonal-wind shear.Climatic characteristics of shear lines and related rainstorms over the Southern Yangtze River Valley(SYRV)during the summers(June-August)from 2008 to 2018 are then analyzed by using two types of unsupervised machine learning algorithm,namely the t-distributed stochastic neighbor embedding method(t-SNE)and the k-means clustering method.The results are as follows:(1)The reproducibility of the 850 hPa wind fields over the SYRV using China’s reanalysis product CMARA is superior to that of European and American products including ERA5,ERA-Interim,and FNL.(2)Theory and observations indicate that the introduction of a second-order zonal-wind shear criterion can effectively eliminate the continuous cyclonic curvature of the wind field and identify shear lines with significant discontinuities.(3)The occurrence frequency of shear lines appearing in the daytime and nighttime is almost equal,but the intensity and the accompanying rainstorm have a clear diurnal variation:they are significantly stronger during daytime than those at nighttime.(4)Half(47%)of the shear lines can cause short-duration rainstorms(≥20 mm(3h)^(-1)),and shear line rainstorms account for one-sixth(16%)of the total summer short-duration rainstorms.Rainstorms caused by shear lines are significantly stronger than that caused by other synoptic forcing.(5)Under the influence of stronger water vapor transport and barotropic instability,shear lines and related rainstorms in the north and middle of the SYRV are stronger than those in the south.
文摘The purpose of this study is the accurate prediction of undrained shear strength using Standard Penetration Test results and soil consistency indices,such as water content and Atterberg limits.With this study,along with the conventional methods of simple and multiple linear regression models,three machine learning algorithms,random forest,gradient boosting and stacked models,are developed for prediction of undrained shear strength.These models are employed on a relatively large data set from different projects around Turkey covering 230 observations.As an improvement over the available studies in literature,this study utilizes correct statistical analyses techniques on a relatively large database,such as using a train/test split on the data set to avoid overfitting of the developed models.Furthermore,the validity and consistency of the prediction results are ensured with the correct use of statistical measures like p-value and cross-validation which were missing in previous studies.To compare the performances of the models developed in this study with the prior ones existing in literature,all models were applied on the test data set and their performances are evaluated in terms of the resulting root mean squared error(RMSE)values and coefficient of determination(R^2).Accordingly,the models developed in this study demonstrate superior prediction capabilities compared to all of the prior studies.Moreover,to facilitate the use of machine learning algorithms for prediction purposes,entire source code prepared for this study and the collected data set are provided as supplements of this study.