Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a ...Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon.展开更多
The fracture initiation behavior for hydraulic fracturing treatments highlighted the necessity of proposing fracture criteria that precisely predict the fracture initiation type and location during the hydraulic fract...The fracture initiation behavior for hydraulic fracturing treatments highlighted the necessity of proposing fracture criteria that precisely predict the fracture initiation type and location during the hydraulic fracturing process.In the present study,a Mohr-Coulomb criterion with a tensile cut-off is incorporated into the finite element code to determine the fracture initiation type and location during the hydraulic fracturing process.This fracture criterion considers the effect of fracture inclination angle,the internal friction angle and the loading conditions on the distribution of stress field around the fracture tip.The results indicate that the internal friction angle resists the shear fracture initiation.Moreover,as the internal friction angle increases,greater external loads are required to maintain the hydraulic fracture extension.Due to the increased pressure of the injected water,the tensile fracture ultimately determines the fracture initiation type.However,the shear fracture preferentially occurs as the stress anisotropy coefficient increases.Both the maximum tensile stress and equivalent maximum shear stress decrease as the stress anisotropy coefficient increases,which indicates that the greater the stress anisotropy coefficient,the higher the external loading required to propagate a new fracture.The numerical results obtained in this paper provide theoretical supports for establishing basis on investigating of the hydraulic fracturing characteristics under different conditions.展开更多
Slope stability prediction plays a significant role in landslide disaster prevention and mitigation.This paper’s reduced error pruning(REP)tree and random tree(RT)models are developed for slope stability evaluation a...Slope stability prediction plays a significant role in landslide disaster prevention and mitigation.This paper’s reduced error pruning(REP)tree and random tree(RT)models are developed for slope stability evaluation and meeting the high precision and rapidity requirements in slope engineering.The data set of this study includes five parameters,namely slope height,slope angle,cohesion,internal friction angle,and peak ground acceleration.The available data is split into two categories:training(75%)and test(25%)sets.The output of the RT and REP tree models is evaluated using performance measures including accuracy(Acc),Matthews correlation coefficient(Mcc),precision(Prec),recall(Rec),and F-score.The applications of the aforementionedmethods for predicting slope stability are compared to one another and recently established soft computing models in the literature.The analysis of the Acc together with Mcc,and F-score for the slope stability in the test set demonstrates that the RT achieved a better prediction performance with(Acc=97.1429%,Mcc=0.935,F-score for stable class=0.979 and for unstable case F-score=0.935)succeeded by the REP tree model with(Acc=95.4286%,Mcc=0.896,F-score stable class=0.967 and for unstable class F-score=0.923)for the slope stability dataset The analysis of performance measures for the slope stability dataset reveals that the RT model attains comparatively better and reliable results and thus should be encouraged in further research.展开更多
The aim of present study is to develop a pharmacokinetic model for microencapsulated metroni- dazole to predict drug absorption pattern in healthy human and validate this model internally. Metronidazole was microencap...The aim of present study is to develop a pharmacokinetic model for microencapsulated metroni- dazole to predict drug absorption pattern in healthy human and validate this model internally. Metronidazole was microencapsulated into ethylcellulose shells followed by the conversion of these microcapsules into tablets. Dissolution study of tablets was conducted in 450 mL double distilled water, 0.1 mol·L-1 HCl and phosphate buffer (pH 6.8) maintained at (37 ± 0.5) ℃ using USP apparatus II at 50, 100 and 150 r·min-1. Three metronidazole tablets (T1: fast release, T2: moderate release, T3: slow release and reference) were administered to twenty four healthy human volunteers and serial blood samples were collected for 12 hours followed by their analysis using RP-HPLC. Drug release data were analyzed by various model dependent and independent approaches. Drug absorbed (%) was determined by Wagner-Nelson method from plasma concentration profile. Internal predictability was checked from Cmax and AUC. Optimum dissolution profile was observed in double distilled water and 50 r·min-1. A good level A correlation was observed between drug dissolution and absorption profiles (correlation coefficient, R2 = 0.900 9, 0.942 6, 0.901 5 and 0.932 for T1, T2, T3 and reference, respectively). Internal predictability was found less than 10%. Good correlation coefficients and low prediction errors elaborate the validity of this mathematical in-vitro in-vivo correlation model as a predictive tool for the determination of pharmacokinetics from dissolution data.展开更多
Liquefaction is one of the most destructive phenomena caused by earthquakes,which has been studied in the issues of potential,triggering and hazard analysis.The strain energy approach is a common method to investigate...Liquefaction is one of the most destructive phenomena caused by earthquakes,which has been studied in the issues of potential,triggering and hazard analysis.The strain energy approach is a common method to investigate liquefaction potential.In this study,two Artificial Neural Network(ANN)models were developed to estimate the liquefaction resistance of sandy soil based on the capacity strain energy concept(W)by using laboratory test data.A large database was collected from the literature.One group of the dataset was utilized for validating the process in order to prevent overtraining the presented model.To investigate the complex influence of fine content(FC)on liquefaction resistance,according to previous studies,the second database was arranged by samples with FC of less than 28%and was used to train the second ANN model.Then,two presented ANN models in this study,in addition to four extra available models,were applied to an additional 20 new samples for comparing their results to show the capability and accuracy of the presented models herein.Furthermore,a parametric sensitivity analysis was performed through Monte Carlo Simulation(MCS)to evaluate the effects of parameters and their uncertainties on the liquefaction resistance of soils.According to the results,the developed models provide a higher accuracy prediction performance than the previously publishedmodels.The sensitivity analysis illustrated that the uncertainties of grading parameters significantly affect the liquefaction resistance of soils.展开更多
This study involves mathematical simulation model such as in vitro-in vivo correlation(IVIVC) development for various extended release formulations of nimesulide loaded hydroxypropylmethylcellulose(HPMC) microparticle...This study involves mathematical simulation model such as in vitro-in vivo correlation(IVIVC) development for various extended release formulations of nimesulide loaded hydroxypropylmethylcellulose(HPMC) microparticles(M1,M2 and M3 containing 1,2,and 3 g HPMC,respectively and 1 g drug in each) having variable release characteristics.In vitro dissolution data of these formulations were correlated to their relevant in vivo absorption profiles followed by predictability worth analysis of these Level A IVIVC.Nimaran was used as control formulation to validate developed formulations and their respective models.The regression coefficients of IVIVC plots for M1,M2,M3 and Nimaran were 0.834 9,0.831 2,0.927 2 and 0.898 1,respectively.The internal prediction error for all formulations was within limits,i.e.,<10%.A good IVIVC was found for controlled release nimesulide loaded HPMC floating M3 microparticles.In other words,this mathematical simulation model is best fit for biowaiver studies which involves study parameters as those adopted for M3 because the value of its IVIVC regression coefficient is the closest to 1 as compared to M1 and M2.展开更多
The objective of this extensive study was to analyze the motivational problems of medical representatives(MRs) and to examine the effects of environment,job characteristics and personality variables on job satisfactio...The objective of this extensive study was to analyze the motivational problems of medical representatives(MRs) and to examine the effects of environment,job characteristics and personality variables on job satisfaction.The statistical analysis has revealed that MRs have a variety of different responses for working harder which is strictly required.An interesting job and satisfaction with various aspects of their work especially their position,task area and pay induce them to exert extra efforts.Over all,this study has provided evidence that in order to understand the factors influencing employer's satisfaction,researcher must examine the combined effects of above mentioned factors.展开更多
After decades of civil war,Yemen is in a desperate situation,and the construction industry has been suffering from low productivity and poor performance.In order to improve the productivity for the Yemeni construction...After decades of civil war,Yemen is in a desperate situation,and the construction industry has been suffering from low productivity and poor performance.In order to improve the productivity for the Yemeni construction industry,Construction enterprises must adopt the best and new technologies,new management concepts and philosophies such as Total Quality Management(TQM)and concurrent engineering(CE)owing to achieve improvements in the process of product development.To ensure the successful implementation of CE in the Yemeni construction industry,it is necessary to assess the readiness of those companies to implement CE.In this paper,the BEACON model is used to assess the readiness of the Yemeni companies to implement the concept of CE,that assist in overcoming the construction industry's poor productivity and performance.A study assessing CE implementation readiness will help to promote successful CE implementation in the construction industry and enhance the efficiency of construction companies.The results show that most of the construction companies in the Yemen are not ready to implement CE.The main reason is that the enterprises rely heavily on traditional management methods,and need to improve the organization and management technology.The research results can provide theoretical support for construction companies,especially Yemen companies,to establish basis in implementing an appropriate CE approach for improving performance,and also help international construction companies entering the Yemen construction market to cooperate and implement CE.展开更多
This study investigates the performance of four machine learning(ML)algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the ...This study investigates the performance of four machine learning(ML)algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network(BBN)learning software Netica.The BBN structures that were developed by ML algorithms-K2,hill climbing(HC),tree augmented naive(TAN)Bayes,and Tabu search were adopted to perform parameter learning in Netica,thereby fixing the BBN models.The performance measure indexes,namely,overall accuracy(OA),precision,recall,F-measure,and area under the receiver operating characteristic curve,were used to evaluate the training and testing BBN models’performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models.The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors,whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential.The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.展开更多
The unprecedented liquefaction-related land damage during earthquakes has highlighted the need to develop a model that better interprets the liquefaction land damage vulnerability(LLDV)when determining whether liquefa...The unprecedented liquefaction-related land damage during earthquakes has highlighted the need to develop a model that better interprets the liquefaction land damage vulnerability(LLDV)when determining whether liquefaction is likely to cause damage at the ground's surface.This paper presents the development of a novel comprehensive framework based on select case history records of cone penetration tests using a Bayesian belief network(BBN)methodology to assess seismic soil liquefaction and liquefaction land damage potentials in one model.The BBN-based LLDV model is developed by integrating multi-related factors of seismic soil liquefaction and its induced hazards using a machine learming(ML)algorithm-K2 and domain knowledge(DK)data fusion methodology.Compared with the C4.5 decision tree-J48 model,naive Bayesian(NB)classifier,and BBN-K2 ML prediction methods in terms of overall accuracy and the Cohen's kappa coefficient,the proposed BBN K2 and DK model has a better performance and provides a substitutive novel LLDV framework for characterizing the vulnerability of land to liquefaction-induced damage.The proposed model not only predicts quantitatively the seismic soil liquefaction potential and its ground damage potential probability but can also identify the main reasons and fault-finding state combinations,and the results are likely to assist in decisions on seismic risk mitigation measures for sustainable development.The proposed model is simple to perform in practice and provides a step toward a more sophisticated liquefaction risk assessment modeling.This study also interprets the BBN model sensitivity analysis and most probable explanation of seismic soil liquefed sites based on an engineering point of view.展开更多
Liquefaction-induced lateral displacement is responsible for considerable damage to engineered structures during major earthquakes.Therefore,an accurate estimation of lateral displacement in liquefaction-prone regions...Liquefaction-induced lateral displacement is responsible for considerable damage to engineered structures during major earthquakes.Therefore,an accurate estimation of lateral displacement in liquefaction-prone regions is an essential task for geotechnical experts for sustainable development.This paper presents a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian belief network(BBN)approach based on an interpretive structural modeling technique.The BBN models are trained and tested using a wide-range casehistory records database.The two BBN models are proposed to predict lateral displacements for free-face and sloping ground conditions.The predictive performance results of the proposed BBN models are compared with those of frequently used multiple linear regression and genetic programming models.The results reveal that the BBN models are able to learn complex relationships between lateral displacement and its influencing factors as cause-effect relationships,with reasonable precision.This study also presents a sensitivity analysis to evaluate the impacts of input factors on the lateral displacement.展开更多
基金Projects(2016YFE0200100,2018YFC1505300-5.3)supported by the National Key Research&Development Plan of ChinaProject(51639002)supported by the Key Program of National Natural Science Foundation of China
文摘Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon.
基金Project(2017YFC1503102)supported by the National Key Research and Development ProgramProjects(51874065,U1903112)supported by the National Natural Science Foundation of China。
文摘The fracture initiation behavior for hydraulic fracturing treatments highlighted the necessity of proposing fracture criteria that precisely predict the fracture initiation type and location during the hydraulic fracturing process.In the present study,a Mohr-Coulomb criterion with a tensile cut-off is incorporated into the finite element code to determine the fracture initiation type and location during the hydraulic fracturing process.This fracture criterion considers the effect of fracture inclination angle,the internal friction angle and the loading conditions on the distribution of stress field around the fracture tip.The results indicate that the internal friction angle resists the shear fracture initiation.Moreover,as the internal friction angle increases,greater external loads are required to maintain the hydraulic fracture extension.Due to the increased pressure of the injected water,the tensile fracture ultimately determines the fracture initiation type.However,the shear fracture preferentially occurs as the stress anisotropy coefficient increases.Both the maximum tensile stress and equivalent maximum shear stress decrease as the stress anisotropy coefficient increases,which indicates that the greater the stress anisotropy coefficient,the higher the external loading required to propagate a new fracture.The numerical results obtained in this paper provide theoretical supports for establishing basis on investigating of the hydraulic fracturing characteristics under different conditions.
基金supported by the National Key Research and Development Plan of China under Grant No.2021YFB2600703.
文摘Slope stability prediction plays a significant role in landslide disaster prevention and mitigation.This paper’s reduced error pruning(REP)tree and random tree(RT)models are developed for slope stability evaluation and meeting the high precision and rapidity requirements in slope engineering.The data set of this study includes five parameters,namely slope height,slope angle,cohesion,internal friction angle,and peak ground acceleration.The available data is split into two categories:training(75%)and test(25%)sets.The output of the RT and REP tree models is evaluated using performance measures including accuracy(Acc),Matthews correlation coefficient(Mcc),precision(Prec),recall(Rec),and F-score.The applications of the aforementionedmethods for predicting slope stability are compared to one another and recently established soft computing models in the literature.The analysis of the Acc together with Mcc,and F-score for the slope stability in the test set demonstrates that the RT achieved a better prediction performance with(Acc=97.1429%,Mcc=0.935,F-score for stable class=0.979 and for unstable case F-score=0.935)succeeded by the REP tree model with(Acc=95.4286%,Mcc=0.896,F-score stable class=0.967 and for unstable class F-score=0.923)for the slope stability dataset The analysis of performance measures for the slope stability dataset reveals that the RT model attains comparatively better and reliable results and thus should be encouraged in further research.
文摘The aim of present study is to develop a pharmacokinetic model for microencapsulated metroni- dazole to predict drug absorption pattern in healthy human and validate this model internally. Metronidazole was microencapsulated into ethylcellulose shells followed by the conversion of these microcapsules into tablets. Dissolution study of tablets was conducted in 450 mL double distilled water, 0.1 mol·L-1 HCl and phosphate buffer (pH 6.8) maintained at (37 ± 0.5) ℃ using USP apparatus II at 50, 100 and 150 r·min-1. Three metronidazole tablets (T1: fast release, T2: moderate release, T3: slow release and reference) were administered to twenty four healthy human volunteers and serial blood samples were collected for 12 hours followed by their analysis using RP-HPLC. Drug release data were analyzed by various model dependent and independent approaches. Drug absorbed (%) was determined by Wagner-Nelson method from plasma concentration profile. Internal predictability was checked from Cmax and AUC. Optimum dissolution profile was observed in double distilled water and 50 r·min-1. A good level A correlation was observed between drug dissolution and absorption profiles (correlation coefficient, R2 = 0.900 9, 0.942 6, 0.901 5 and 0.932 for T1, T2, T3 and reference, respectively). Internal predictability was found less than 10%. Good correlation coefficients and low prediction errors elaborate the validity of this mathematical in-vitro in-vivo correlation model as a predictive tool for the determination of pharmacokinetics from dissolution data.
基金supported by the Scientific Innovation Group for Youths of Sichuan Province under Grant No.2019JDTD0017。
文摘Liquefaction is one of the most destructive phenomena caused by earthquakes,which has been studied in the issues of potential,triggering and hazard analysis.The strain energy approach is a common method to investigate liquefaction potential.In this study,two Artificial Neural Network(ANN)models were developed to estimate the liquefaction resistance of sandy soil based on the capacity strain energy concept(W)by using laboratory test data.A large database was collected from the literature.One group of the dataset was utilized for validating the process in order to prevent overtraining the presented model.To investigate the complex influence of fine content(FC)on liquefaction resistance,according to previous studies,the second database was arranged by samples with FC of less than 28%and was used to train the second ANN model.Then,two presented ANN models in this study,in addition to four extra available models,were applied to an additional 20 new samples for comparing their results to show the capability and accuracy of the presented models herein.Furthermore,a parametric sensitivity analysis was performed through Monte Carlo Simulation(MCS)to evaluate the effects of parameters and their uncertainties on the liquefaction resistance of soils.According to the results,the developed models provide a higher accuracy prediction performance than the previously publishedmodels.The sensitivity analysis illustrated that the uncertainties of grading parameters significantly affect the liquefaction resistance of soils.
基金Higher Education Commission of Pakistan for proving financial support for research.
文摘This study involves mathematical simulation model such as in vitro-in vivo correlation(IVIVC) development for various extended release formulations of nimesulide loaded hydroxypropylmethylcellulose(HPMC) microparticles(M1,M2 and M3 containing 1,2,and 3 g HPMC,respectively and 1 g drug in each) having variable release characteristics.In vitro dissolution data of these formulations were correlated to their relevant in vivo absorption profiles followed by predictability worth analysis of these Level A IVIVC.Nimaran was used as control formulation to validate developed formulations and their respective models.The regression coefficients of IVIVC plots for M1,M2,M3 and Nimaran were 0.834 9,0.831 2,0.927 2 and 0.898 1,respectively.The internal prediction error for all formulations was within limits,i.e.,<10%.A good IVIVC was found for controlled release nimesulide loaded HPMC floating M3 microparticles.In other words,this mathematical simulation model is best fit for biowaiver studies which involves study parameters as those adopted for M3 because the value of its IVIVC regression coefficient is the closest to 1 as compared to M1 and M2.
文摘The objective of this extensive study was to analyze the motivational problems of medical representatives(MRs) and to examine the effects of environment,job characteristics and personality variables on job satisfaction.The statistical analysis has revealed that MRs have a variety of different responses for working harder which is strictly required.An interesting job and satisfaction with various aspects of their work especially their position,task area and pay induce them to exert extra efforts.Over all,this study has provided evidence that in order to understand the factors influencing employer's satisfaction,researcher must examine the combined effects of above mentioned factors.
基金This paper was part of a research work sponsored by the National Thirteenth-Five-year Research Program of China(Project No.:2018YFC0705901)The authors would like to thank the anonymous reviewersfor their valuable comments and suggestions to improvethe quality of the paper. They also gratefully acknowledgethe Public Works Office in Yemen for facilitating duringthe data collection stage.
文摘After decades of civil war,Yemen is in a desperate situation,and the construction industry has been suffering from low productivity and poor performance.In order to improve the productivity for the Yemeni construction industry,Construction enterprises must adopt the best and new technologies,new management concepts and philosophies such as Total Quality Management(TQM)and concurrent engineering(CE)owing to achieve improvements in the process of product development.To ensure the successful implementation of CE in the Yemeni construction industry,it is necessary to assess the readiness of those companies to implement CE.In this paper,the BEACON model is used to assess the readiness of the Yemeni companies to implement the concept of CE,that assist in overcoming the construction industry's poor productivity and performance.A study assessing CE implementation readiness will help to promote successful CE implementation in the construction industry and enhance the efficiency of construction companies.The results show that most of the construction companies in the Yemen are not ready to implement CE.The main reason is that the enterprises rely heavily on traditional management methods,and need to improve the organization and management technology.The research results can provide theoretical support for construction companies,especially Yemen companies,to establish basis in implementing an appropriate CE approach for improving performance,and also help international construction companies entering the Yemen construction market to cooperate and implement CE.
基金The work presented in this paper was part of research sponsored by the National Key Research&Development Plan of China(Nos.2018YFC1505305 and 2016YFE0200100)the Key Program of the National Natural Science Foundation of China(No.51639002).
文摘This study investigates the performance of four machine learning(ML)algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network(BBN)learning software Netica.The BBN structures that were developed by ML algorithms-K2,hill climbing(HC),tree augmented naive(TAN)Bayes,and Tabu search were adopted to perform parameter learning in Netica,thereby fixing the BBN models.The performance measure indexes,namely,overall accuracy(OA),precision,recall,F-measure,and area under the receiver operating characteristic curve,were used to evaluate the training and testing BBN models’performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models.The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors,whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential.The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.
基金The research presented in this paper was part of the research sponsored by the National Key Research&Development Plan of China(Nos.2018YFC1505305 and 2016YFE0200100)Key Program of the National Natural Science Foundation of China(Grant No.51639002)Much gratitude is extended to the experts for their opinions on the BBN model building.
文摘The unprecedented liquefaction-related land damage during earthquakes has highlighted the need to develop a model that better interprets the liquefaction land damage vulnerability(LLDV)when determining whether liquefaction is likely to cause damage at the ground's surface.This paper presents the development of a novel comprehensive framework based on select case history records of cone penetration tests using a Bayesian belief network(BBN)methodology to assess seismic soil liquefaction and liquefaction land damage potentials in one model.The BBN-based LLDV model is developed by integrating multi-related factors of seismic soil liquefaction and its induced hazards using a machine learming(ML)algorithm-K2 and domain knowledge(DK)data fusion methodology.Compared with the C4.5 decision tree-J48 model,naive Bayesian(NB)classifier,and BBN-K2 ML prediction methods in terms of overall accuracy and the Cohen's kappa coefficient,the proposed BBN K2 and DK model has a better performance and provides a substitutive novel LLDV framework for characterizing the vulnerability of land to liquefaction-induced damage.The proposed model not only predicts quantitatively the seismic soil liquefaction potential and its ground damage potential probability but can also identify the main reasons and fault-finding state combinations,and the results are likely to assist in decisions on seismic risk mitigation measures for sustainable development.The proposed model is simple to perform in practice and provides a step toward a more sophisticated liquefaction risk assessment modeling.This study also interprets the BBN model sensitivity analysis and most probable explanation of seismic soil liquefed sites based on an engineering point of view.
基金This study was part of research work sponsored by the National Key Research&Development Plan of China(Nos.2018YFC 1505300-5.3 and 2016YFE0200100)the Key Program of the National Natural Science Foundation of China(Grant No.51639002).
文摘Liquefaction-induced lateral displacement is responsible for considerable damage to engineered structures during major earthquakes.Therefore,an accurate estimation of lateral displacement in liquefaction-prone regions is an essential task for geotechnical experts for sustainable development.This paper presents a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian belief network(BBN)approach based on an interpretive structural modeling technique.The BBN models are trained and tested using a wide-range casehistory records database.The two BBN models are proposed to predict lateral displacements for free-face and sloping ground conditions.The predictive performance results of the proposed BBN models are compared with those of frequently used multiple linear regression and genetic programming models.The results reveal that the BBN models are able to learn complex relationships between lateral displacement and its influencing factors as cause-effect relationships,with reasonable precision.This study also presents a sensitivity analysis to evaluate the impacts of input factors on the lateral displacement.
