Seismic Liquefaction Resistance Based on Strain Energy Concept Considering Fine Content Value Effect and Performance Parametric Sensitivity Analysis

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.

Improved Prediction of Slope Stability under Static and Dynamic Conditions Using Tree-BasedModels

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.

基于贝叶斯置信网络的CPT地震液化势混合评估方法（英文）

地震液化评估是一个复杂的非线性过程,受多种因素的不确定性和复杂性的影响。贝叶斯置信网络(BBN)是一个可靠有效的工具,可以提供一个合适的框架来处理这些不确定性和因果关系。本研究采用一种混合方法来建立基于静力触探试验(CPT)案例记录数据的贝叶斯置信网络(BBN)模型,以评估土壤的地震液化势。在这种混合方法中,先通过结合领域知识(DK)的解释结构建模(ISM)技术建立朴素模型,再在K2算法中嵌入朴素模型的相关信息建立BBN-K2和DK模型。将BBN模型的结果与现有的人工神经网络(ANN)和C4.5决策树(DT)模型进行了比较和验证,发现用混合方法建立的BBN模型在液化势评估中具有良好的适应性和应用前景。用混合方法建立的BBN模型为岩土工程师评估易受地震液化影响的场地环境提供了可行的工具。最后对基于混合方法的BBN模型进行了灵敏度分析,并对液化场地进行了最可能的解释,以了解液化现象的最可能情况。

不同荷载作用条件下水力裂缝起裂特性的数值模拟研究

水力裂缝的起裂特性对水力压裂工程的顺利实施至关重要,因此,需要提出准确预测水力裂缝起裂类型和位置的断裂准则。在本研究中,将带有拉伸截断的摩尔-库仑准则引入有限元程序中,用来确定水力裂缝的起裂类型和位置。该断裂准则考虑了裂缝倾角、岩石内摩擦角和载荷条件对裂缝尖端应力场分布的影响。研究结果表明,内摩擦角能减弱剪切断裂发生的可能。此外,随着内摩擦角的增加,需要更大的外部载荷来使水力裂缝扩展。随着注水压力的增大,拉伸裂缝类型最终决定了水力裂缝的起裂类型。但随着应力差的增大,则优先产生剪切裂缝类型。最大拉应力和等效最大剪应力均随着应力差的增大而减小,说明应力差越大,裂缝扩展所需的外载荷越大。本文的数值计算结果为研究不同条件下水力裂缝的起裂与扩展特性提供了理论依据。

Evaluation of Implementation Preparation for CE based on BEACON Model—Taking Construction Enterprises in Yemen as a Case of Illustration

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.

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

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.

Pharmacokinetic modelling of microencapsulated metronidazole

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.

In vitro-in vivo correlation study on nimesulide loaded hydroxypropylmethylcellulose microparticles

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.

A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability:Exploration from historical data

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.

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

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.

Factors influencing job satisfaction of medical representatives in Pakistan

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.