In order to study the work-ability and establish the optimum hot formation processing parameters for industrial 1060 pure aluminum, the compressive deformation behavior of pure aluminum was investigated at temperature...In order to study the work-ability and establish the optimum hot formation processing parameters for industrial 1060 pure aluminum, the compressive deformation behavior of pure aluminum was investigated at temperatures of 523?823 K and strain rates of 0.005?10 s?1 on a Gleeble?1500 thermo-simulation machine. The influence rule of processing parameters (strain, strain rate and temperature) on flow stress of pure aluminum was investigated. Nine analysis factors consisting of material parameters and according weights were optimized. Then, the constitutive equations of multilevel series rules, multilevel parallel rules and multilevel series ¶llel rules were established. The correlation coefficients (R) are 0.992, 0.988 and 0.990, respectively, and the average absolute relative errors (AAREs) are 6.77%, 8.70% and 7.63%, respectively, which proves that the constitutive equations of multilevel series rules can predict the flow stress of pure aluminum with good correlation and precision.展开更多
Total organic carbon(TOC)content is a crucial evaluation parameter in the process of shale gas exploration and development.Marine-continental transitional shale is characterized by strong heterogeneity and thin single...Total organic carbon(TOC)content is a crucial evaluation parameter in the process of shale gas exploration and development.Marine-continental transitional shale is characterized by strong heterogeneity and thin single-layer thickness.The discrete TOC data measured by experimental methods are unable to accurately reflect the reservoir characteristics of marine-continental transitional shale.In this paper,a multivariate nonlinear regression prediction model(R-MNR)was established,and the model was applied to predict the TOC content of shale for the first time.TheΔlgR model,multiple linear regression model(MLR),BP neural network model(BP model),and R-MNR model were built to predict the TOC of shale in Benxi Formation.The coefficient of determination(R2),mean-absolute-percentage-error(MAPE),root-mean-square-error(RMSE),and the number of input layer parameters(NILP)were employed to assess the efficacy of the model through the analytic hierarchy process(AHP)method.The total weight of R-MNR is 0.361,and that of BP model is 0.336.The weights of the two traditional models are 0.104 and 0.199,respectively.The results indicate that the R-MNR is comparable to the BP model in terms of prediction accuracy,and both models are significantly more accurate than the traditional prediction model.The R-MNR is capable of obtaining a clear TOC prediction formula,which is convenient for verification and promotion.During the training process of the R-MNR,the influence of each parameter and coupling relationship on the prediction results is elucidated,which enables researchers to gain a deeper understanding of the geophysical significance and geological process of the model.The result of this study suggests that the R-MNR can be employed to predict the TOC content of marine-continental transitional shale effectively in the future.展开更多
Design for six sigma （DFSS） is a powerful approach of designing products, processes, and services with the objective of meeting the needs of customers in a cost-effective maimer. DFSS activities are classified into ...Design for six sigma （DFSS） is a powerful approach of designing products, processes, and services with the objective of meeting the needs of customers in a cost-effective maimer. DFSS activities are classified into four major phases viz. identify, design, optimize, and validate （IDOV）. And an adaptive design for six sigma （ADFSS） incorporating the traits of artifidai intelligence and statistical techniques is presented. In the identify phase of the ADFSS, fuzzy relation measures between customer attributes （CAs） and engineering characteristics （ECs） as well as fuzzy correlation measures among ECs are determined with the aid of two fuzzy logic controllers （FLCs）. These two measures are then used to establish the cumulative impact factor for ECs. In the next phase （ i. e. design phase）, a transfer function is developed with the aid of robust multiple nonlinear regression analysis. Furthermore, 1this transfer function is optimized with the simulated annealing （ SA ） algorithm in the optimize phase. In the validate phase, t-test is conducted for the validation of the design resulted in earlier phase. Finally, a case study of a hypothetical writing instrument is simulated to test the efficacy of the proposed ADFSS.展开更多
Understanding the strength characteristics and deformation behaviour of the tunnel surrounding rock in a fault zone is significant for tunnel stability evaluation.In this study,a series of unconfined compression tests...Understanding the strength characteristics and deformation behaviour of the tunnel surrounding rock in a fault zone is significant for tunnel stability evaluation.In this study,a series of unconfined compression tests were conducted to investigate the mechanical characteristics and failure behaviour of completely weathered granite(CWG)from a fault zone,considering with height-diameter(h/d)ratio,dry densities(ρd)and moisture contents(ω).Based on the experimental results,a regression mathematical model of unconfined compressive strength(UCS)for CWG was developed using the Multiple Nonlinear Regression method(MNLR).The research results indicated that the UCS of the specimen with a h/d ratio of 0.6 decreased with the increase ofω.When the h/d ratio increased to 1.0,the UCS increasedωwith up to 10.5%and then decreased.Increasingρd is conducive to the improvement of the UCS at anyω.The deformation and rupture process as well as final failure modes of the specimen are controlled by h/d ratio,ρd andω,and the h/d ratio is the dominant factor affecting the final failure mode,followed byωandρd.The specimens with different h/d ratio exhibited completely different fracture mode,i.e.,typical splitting failure(h/d=0.6)and shear failure(h/d=1.0).By comparing the experimental results,this regression model for predicting UCS is accurate and reliable,and the h/d ratio is the dominant factor affecting the UCS of CWG,followed byρd and thenω.These findings provide important references for maintenance of the tunnel crossing other fault fractured zones,especially at low confining pressure or unconfined condition.展开更多
Estimation of support pressure is extremely important to the support system design and the construction safety of tunnels.At present,there are many methods for the estimation of support pressure based on different roc...Estimation of support pressure is extremely important to the support system design and the construction safety of tunnels.At present,there are many methods for the estimation of support pressure based on different rock mass classification systems,such as Q system,GSI system and RMR system.However,various rock mass classification systems are based on different tunnel geologic conditions in various regions.Therefore,each rock mass classification system has a certain regionality.In China,the BQ-Inex(BQ system)has been widely used in the field of rock engineering ever since its development.Unfortunately,there is still no estimation method of support pressure with BQ-index as parameters.Based on the field test data from 54 tunnels in China,a new empirical method considering BQ-Inex,tunnel span and rock weight is proposed to estimate the support pressure using multiple nonlinear regression analysis methods.And then the significance and necessity of support pressure estimation method for the safety of tunnel construction in China is explained through the comparison and analysis with the existing internationally widely used support pressure estimation methods of RMR system,Q system and GSI system.Finally,the empirical method of estimating the support pressure based on BQ-index was applied to designing the support system in the China’s high-speed railway tunnel—Zhengwan high-speed railway and the rationality of this method has been verified through the data of field test.展开更多
Soil surface roughness, denoted by the root mean square height(RMSH), and soil moisture(SM) are critical factors that affect the accuracy of quantitative remote sensing research due to their combined influence on spec...Soil surface roughness, denoted by the root mean square height(RMSH), and soil moisture(SM) are critical factors that affect the accuracy of quantitative remote sensing research due to their combined influence on spectral reflectance(SR). In regards to this issue, three SM levels and four RMSH levels were artificially designed in this study; a total of 12 plots was used, each plot had a size of 3 m × 3 m. Eight spectral observations were conducted from 14 to 30 October 2017 to investigate the correlation between RMSH, SM, and SR. On this basis, 6 commonly used bands of optical satellite sensors were selected in this study, which are red(675 nm), green(555 nm), blue(485 nm), near infrared(845 nm), shortwave infrared 1(1600 nm), and shortwave infrared 2(2200 nm). A negative correlation was found between SR and RMSH, and between SR and SM. The bands with higher coefficient of determination R^2 values were selected for stepwise multiple nonlinear regression analysis. Four characterized bands(i.e., blue, green, near infrared, and shortwave infrared 2) were chosen as the independent variables to estimate SM with R^2 and root mean square error(RMSE) values equal to 0.62 and 2.6%, respectively. Similarly, the four bands(green, red, near infrared, and shortwave infrared 1) were used to estimate RMSH with R^2 and RMSE values equal to 0.48 and 0.69 cm, respectively. These results indicate that the method used is not only suitable for estimating SM but can also be extended to the prediction of RMSH. Finally, the evaluation approach presented in this paper highly restores the real situation of the natural farmland surface on the one hand, and obtains high precision values of SM and RMSH on the other. The method can be further applied to the prediction of farmland SM and RMSH based on satellite and unmanned aerial vehicle(UAV) optical imagery.展开更多
The construction of urban metro systems in Southwest China is invariably challenged by adverse geological conditions characteristic of water-rich karst formations.This study investigates the damage evolution mechanism...The construction of urban metro systems in Southwest China is invariably challenged by adverse geological conditions characteristic of water-rich karst formations.This study investigates the damage evolution mechanisms in the cavern-containing stratum and the extent of stratum damage through a comprehensive analysis of the shield tunnel construction in the Shenzhen-Huizhou double-line project.Firstly,the ground surface settlement curve was calculated by numerical modeling and theoretical calculation,and the results were verified by on-site monitoring data.The analysis confirmed that the tunnel construction simulation employed a synchronized excavation method for both parallel tunnels.Subsequently,the stratum dissolution rate(δ)and fissure surface roughness(f)were introduced,and PFC^(2D)was utilized to study the damage evolution mechanisms in the cavern-containing stratum.The data were then compared with the actual monitoring data to ensure the accuracy of the numerical simulation.It was found that the cave-containing stratum under the tunnel was most prone to collapse.Finally,to study the extent of damage to the stratum under such conditions,four factors,namely,fissure width e,fissure surface roughness f,dynamic viscosity of water u,and karst water pressure P_(w),were introduced to study and predict the extension of single-cave fissures.Additionally,the degree of connectivity(L^(*))between multiple caves was introduced to obtain the law between the L^(*)and karst water pressure and caved location.The results of the study provided some guidance for the prevention and control of water inrush disasters in karst areas.展开更多
In recent years,there have been many studies on the recognition of strawberry maturity,but there are still problems such as low recognition accuracy and expensive experimental instruments.These factors make their meth...In recent years,there have been many studies on the recognition of strawberry maturity,but there are still problems such as low recognition accuracy and expensive experimental instruments.These factors make their methods difficult for farmers to use.To solve these problems,we developed a fast,non-destructive,accurate and convenient method for strawberry maturity identification using smartphones.In this paper,strawberry maturity is divided into three levels:mature,nearly-mature and immature.Considering the actual strawberry harvest process and postharvest handling,we focus on the differentiation between the mature and the nearly-mature ones to help farmers reduce possible damage in transit and improve profitability.We obtained the images of strawberries with different maturities at 535 nm and 670 nm wavelengths through a smartphone and got absorbance data by image processing based on the region of interest.The absorbance data were used to establish three maturity recognition models—i.e.,multivariate linear,multivariate nonlinear and SoftMax regression classifier.The results showed that the multivariate nonlinear model had the highest identification accuracy(which is over 94%)in the greenhouse.Therefore,this method has considerable potential as a means for rapid recognition of strawberry maturity.展开更多
基金Project(51275414)supported by the National Natural Science Foundation of ChinaProject(2015JM5204)supported by the Natural Science Foundation of Shaanxi Province,China+1 种基金Project(Z2015064)supported by the Graduate Starting Seed Fund of the Northwestern Polytechnical University,ChinaProject(130-QP-2015)supported by the Research Fund of the State Key Laboratory of Solidification Processing(NWPU),China
文摘In order to study the work-ability and establish the optimum hot formation processing parameters for industrial 1060 pure aluminum, the compressive deformation behavior of pure aluminum was investigated at temperatures of 523?823 K and strain rates of 0.005?10 s?1 on a Gleeble?1500 thermo-simulation machine. The influence rule of processing parameters (strain, strain rate and temperature) on flow stress of pure aluminum was investigated. Nine analysis factors consisting of material parameters and according weights were optimized. Then, the constitutive equations of multilevel series rules, multilevel parallel rules and multilevel series ¶llel rules were established. The correlation coefficients (R) are 0.992, 0.988 and 0.990, respectively, and the average absolute relative errors (AAREs) are 6.77%, 8.70% and 7.63%, respectively, which proves that the constitutive equations of multilevel series rules can predict the flow stress of pure aluminum with good correlation and precision.
基金funded by the National Natural Science Foundation of China(Grant No.42372194)the Natural Science Foundation of Shanxi Province,China(No.20210302123165),the Chinese Postdoctoral Science Foundation(No.2024T170634)the Open Fund Project of Provincial Center of Technology Innovation for Coal Measure Gas Co-production(ZZGSSASMCYJ2024-0306).
文摘Total organic carbon(TOC)content is a crucial evaluation parameter in the process of shale gas exploration and development.Marine-continental transitional shale is characterized by strong heterogeneity and thin single-layer thickness.The discrete TOC data measured by experimental methods are unable to accurately reflect the reservoir characteristics of marine-continental transitional shale.In this paper,a multivariate nonlinear regression prediction model(R-MNR)was established,and the model was applied to predict the TOC content of shale for the first time.TheΔlgR model,multiple linear regression model(MLR),BP neural network model(BP model),and R-MNR model were built to predict the TOC of shale in Benxi Formation.The coefficient of determination(R2),mean-absolute-percentage-error(MAPE),root-mean-square-error(RMSE),and the number of input layer parameters(NILP)were employed to assess the efficacy of the model through the analytic hierarchy process(AHP)method.The total weight of R-MNR is 0.361,and that of BP model is 0.336.The weights of the two traditional models are 0.104 and 0.199,respectively.The results indicate that the R-MNR is comparable to the BP model in terms of prediction accuracy,and both models are significantly more accurate than the traditional prediction model.The R-MNR is capable of obtaining a clear TOC prediction formula,which is convenient for verification and promotion.During the training process of the R-MNR,the influence of each parameter and coupling relationship on the prediction results is elucidated,which enables researchers to gain a deeper understanding of the geophysical significance and geological process of the model.The result of this study suggests that the R-MNR can be employed to predict the TOC content of marine-continental transitional shale effectively in the future.
基金Shanghai Leading Academic Discipline Project,China(No.B602)
文摘Design for six sigma （DFSS） is a powerful approach of designing products, processes, and services with the objective of meeting the needs of customers in a cost-effective maimer. DFSS activities are classified into four major phases viz. identify, design, optimize, and validate （IDOV）. And an adaptive design for six sigma （ADFSS） incorporating the traits of artifidai intelligence and statistical techniques is presented. In the identify phase of the ADFSS, fuzzy relation measures between customer attributes （CAs） and engineering characteristics （ECs） as well as fuzzy correlation measures among ECs are determined with the aid of two fuzzy logic controllers （FLCs）. These two measures are then used to establish the cumulative impact factor for ECs. In the next phase （ i. e. design phase）, a transfer function is developed with the aid of robust multiple nonlinear regression analysis. Furthermore, 1this transfer function is optimized with the simulated annealing （ SA ） algorithm in the optimize phase. In the validate phase, t-test is conducted for the validation of the design resulted in earlier phase. Finally, a case study of a hypothetical writing instrument is simulated to test the efficacy of the proposed ADFSS.
基金supported by the National Natural Science Foundation of China,NSFC(No.42202318).
文摘Understanding the strength characteristics and deformation behaviour of the tunnel surrounding rock in a fault zone is significant for tunnel stability evaluation.In this study,a series of unconfined compression tests were conducted to investigate the mechanical characteristics and failure behaviour of completely weathered granite(CWG)from a fault zone,considering with height-diameter(h/d)ratio,dry densities(ρd)and moisture contents(ω).Based on the experimental results,a regression mathematical model of unconfined compressive strength(UCS)for CWG was developed using the Multiple Nonlinear Regression method(MNLR).The research results indicated that the UCS of the specimen with a h/d ratio of 0.6 decreased with the increase ofω.When the h/d ratio increased to 1.0,the UCS increasedωwith up to 10.5%and then decreased.Increasingρd is conducive to the improvement of the UCS at anyω.The deformation and rupture process as well as final failure modes of the specimen are controlled by h/d ratio,ρd andω,and the h/d ratio is the dominant factor affecting the final failure mode,followed byωandρd.The specimens with different h/d ratio exhibited completely different fracture mode,i.e.,typical splitting failure(h/d=0.6)and shear failure(h/d=1.0).By comparing the experimental results,this regression model for predicting UCS is accurate and reliable,and the h/d ratio is the dominant factor affecting the UCS of CWG,followed byρd and thenω.These findings provide important references for maintenance of the tunnel crossing other fault fractured zones,especially at low confining pressure or unconfined condition.
基金Projects(51878567,51878568,51578458)supported by the National Natural Science Foundation of ChinaProjects(2017G007-F,2017G007-H)supported by China Railway Science and Technology Research and Development Plan。
文摘Estimation of support pressure is extremely important to the support system design and the construction safety of tunnels.At present,there are many methods for the estimation of support pressure based on different rock mass classification systems,such as Q system,GSI system and RMR system.However,various rock mass classification systems are based on different tunnel geologic conditions in various regions.Therefore,each rock mass classification system has a certain regionality.In China,the BQ-Inex(BQ system)has been widely used in the field of rock engineering ever since its development.Unfortunately,there is still no estimation method of support pressure with BQ-index as parameters.Based on the field test data from 54 tunnels in China,a new empirical method considering BQ-Inex,tunnel span and rock weight is proposed to estimate the support pressure using multiple nonlinear regression analysis methods.And then the significance and necessity of support pressure estimation method for the safety of tunnel construction in China is explained through the comparison and analysis with the existing internationally widely used support pressure estimation methods of RMR system,Q system and GSI system.Finally,the empirical method of estimating the support pressure based on BQ-index was applied to designing the support system in the China’s high-speed railway tunnel—Zhengwan high-speed railway and the rationality of this method has been verified through the data of field test.
基金Under the auspices of the Excellent Youth Talent Project of Jilin Science and Technology Development Program(No.20170520078JH)Science and Technology Basic Work of Science and Technology(No.2014FY210800–4)National Natural Science Foundation of China(No.41601382)
文摘Soil surface roughness, denoted by the root mean square height(RMSH), and soil moisture(SM) are critical factors that affect the accuracy of quantitative remote sensing research due to their combined influence on spectral reflectance(SR). In regards to this issue, three SM levels and four RMSH levels were artificially designed in this study; a total of 12 plots was used, each plot had a size of 3 m × 3 m. Eight spectral observations were conducted from 14 to 30 October 2017 to investigate the correlation between RMSH, SM, and SR. On this basis, 6 commonly used bands of optical satellite sensors were selected in this study, which are red(675 nm), green(555 nm), blue(485 nm), near infrared(845 nm), shortwave infrared 1(1600 nm), and shortwave infrared 2(2200 nm). A negative correlation was found between SR and RMSH, and between SR and SM. The bands with higher coefficient of determination R^2 values were selected for stepwise multiple nonlinear regression analysis. Four characterized bands(i.e., blue, green, near infrared, and shortwave infrared 2) were chosen as the independent variables to estimate SM with R^2 and root mean square error(RMSE) values equal to 0.62 and 2.6%, respectively. Similarly, the four bands(green, red, near infrared, and shortwave infrared 1) were used to estimate RMSH with R^2 and RMSE values equal to 0.48 and 0.69 cm, respectively. These results indicate that the method used is not only suitable for estimating SM but can also be extended to the prediction of RMSH. Finally, the evaluation approach presented in this paper highly restores the real situation of the natural farmland surface on the one hand, and obtains high precision values of SM and RMSH on the other. The method can be further applied to the prediction of farmland SM and RMSH based on satellite and unmanned aerial vehicle(UAV) optical imagery.
基金supported by the National Natural Science Foundation of China(Grant No.52278387).
文摘The construction of urban metro systems in Southwest China is invariably challenged by adverse geological conditions characteristic of water-rich karst formations.This study investigates the damage evolution mechanisms in the cavern-containing stratum and the extent of stratum damage through a comprehensive analysis of the shield tunnel construction in the Shenzhen-Huizhou double-line project.Firstly,the ground surface settlement curve was calculated by numerical modeling and theoretical calculation,and the results were verified by on-site monitoring data.The analysis confirmed that the tunnel construction simulation employed a synchronized excavation method for both parallel tunnels.Subsequently,the stratum dissolution rate(δ)and fissure surface roughness(f)were introduced,and PFC^(2D)was utilized to study the damage evolution mechanisms in the cavern-containing stratum.The data were then compared with the actual monitoring data to ensure the accuracy of the numerical simulation.It was found that the cave-containing stratum under the tunnel was most prone to collapse.Finally,to study the extent of damage to the stratum under such conditions,four factors,namely,fissure width e,fissure surface roughness f,dynamic viscosity of water u,and karst water pressure P_(w),were introduced to study and predict the extension of single-cave fissures.Additionally,the degree of connectivity(L^(*))between multiple caves was introduced to obtain the law between the L^(*)and karst water pressure and caved location.The results of the study provided some guidance for the prevention and control of water inrush disasters in karst areas.
基金This research was supported by the National Training Program of Innovation and Entrepreneurship for Undergraduates(No.201710019174).
文摘In recent years,there have been many studies on the recognition of strawberry maturity,but there are still problems such as low recognition accuracy and expensive experimental instruments.These factors make their methods difficult for farmers to use.To solve these problems,we developed a fast,non-destructive,accurate and convenient method for strawberry maturity identification using smartphones.In this paper,strawberry maturity is divided into three levels:mature,nearly-mature and immature.Considering the actual strawberry harvest process and postharvest handling,we focus on the differentiation between the mature and the nearly-mature ones to help farmers reduce possible damage in transit and improve profitability.We obtained the images of strawberries with different maturities at 535 nm and 670 nm wavelengths through a smartphone and got absorbance data by image processing based on the region of interest.The absorbance data were used to establish three maturity recognition models—i.e.,multivariate linear,multivariate nonlinear and SoftMax regression classifier.The results showed that the multivariate nonlinear model had the highest identification accuracy(which is over 94%)in the greenhouse.Therefore,this method has considerable potential as a means for rapid recognition of strawberry maturity.
