A hybrid approach for evaluating CPT-based seismic soil liquefaction potential using Bayesian belief networks

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.

A Software Risk Analysis Model Using Bayesian Belief Network

The uncertainty during the period of software project development often brings huge risks to contractors and clients. If we can find an effective method to predict the cost and quality of software projects based on facts like the project character and two-side cooperating capability at the beginning of the project,we can reduce the risk. Bayesian Belief Network(BBN) is a good tool for analyzing uncertain consequences, but it is difficult to produce precise network structure and conditional probability table.In this paper,we built up network structure by Delphi method for conditional probability table learning,and learn update probability table and nodes’confidence levels continuously according to the application cases, which made the evaluation network have learning abilities, and evaluate the software development risk of organization more accurately.This paper also introduces EM algorithm, which will enhance the ability to produce hidden nodes caused by variant software projects.

An EEGA-Based Bayesian Belief Network Model for Recognition of Human Activity in Smart Home

With the emerging of sensor networks, research on sensor-based activity recognition has attracted much attention. Many existing methods cannot well deal with the cases that contain hundreds of sensors and their recognition accuracy is requisite to be further improved. A novel framework for recognizing human activities in smart home was presented. First, small, easy-to-install, and low-cost state change sensors were adopted for recording state change or use of the objects. Then the Bayesian belief network (BBN) was applied to conducting activity recognition by modeling statistical dependencies between sensor data and human activity. An edge-encode genetic algorithm (EEGA) approach was proposed to resolve the difficulties in structure learning of the BBN model under a high dimension space and large data set. Finally, some experiments were made using one publicly available dataset. The experimental results show that the EEGA algorithm is effective and efficient in learning the BBN structure and outperforms the conventional approaches. By conducting human activity recognition based on the testing samples, the BBN is effective to conduct human activity recognition and outperforms the naive Bayesian network (NBN) and multiclass naive Bayes classifier (MNBC).

Prioritizing Indicators for Rapid Response in Global Health Security:A Bayesian Network Approach

This study explored a Bayesian belief networks(BBNs)approach,developing two distinct models for prioritizing the seven indicators related to the“rapid response to and mitigation of the spread of an epidemic”category within the context of both the specifc category and the Global Health Security Index(GHS index).Utilizing data from the 2021 GHS index,the methodology involves rigorous preprocessing,the application of the augmented naive Bayes algorithm for structural learning,and k-fold cross-validation.Key fndings show unique perspectives in both BBN models.In the mutual value of information analysis,“linking public health and security authorities”emerged as the key predictor for the“rapid response to and mitigation of the spread of an epidemic”category,while“emergency preparedness and response planning”assumed precedence for the GHS index.Sensitivity analysis highlighted the critical role of“emergency preparedness and response planning”and“linking public health and security authorities”in extreme performance states,with“access to communications infrastructure”and“trade and travel restrictions”exhibiting varied signifcance.The BBN models exhibit high predictive accuracy,achieving 83.3%and 82.3%accuracy for extreme states in“rapid response to and mitigation of the spread of an epidemic”and the GHS index,respectively.This study contributes to the literature on GHS by modeling the dependencies among various indicators of the rapid response dimension of the GHS index and highlighting their relative importance based on the mutual value of information and sensitivity analyses.

Developing a Bayesian belief network model for prediction of R&D project success

The project success is critical to the business performance in the era of fierce competition and globalization.The basis for project success lies in the capabilities of managing risks effectively.Innovation has always been considerably risky;however,managing Research and Development(R&D)project risks has become even more important given today’s tight schedules and limited resources.Risk management has to be an integral part of the development process.The purpose of this research is to develop a model to assess and estimate the risk exposure of an R&D project.A risk quantification model based on the Bayesian belief network is proposed,which is effective in capturing the interaction between various risk factors.The aim of this model is to empower the project managers to predict the failure risk probability of R&D projects.

Data learning and expert judgment in a Bayesian belief network for aiding human reliability assessment in offshore decommissioning risk assessment

Decommissioning of offshore facilities involve changing risk profiles at different decommissioning phases.Bayesian Belief Networks(BBN)are used as part of the proposed risk assessment method to capture the multiple interactions of a decommissioning activity.The BBN is structured from the data learning of an accident database and a modification of the BBN nodes to incorporate human reliability and barrier performance modelling.The analysis covers one case study of one area of decommissioning operations by extrapolating well workover data to well plugging and abandonment.Initial analysis from well workover data,of a 5-node BBN provided insights on two different levels of severity of an accident,the’Accident’and’Incident’level,and on its respective profiles of the initiating events and the investigation-reported human causes.The initial results demonstrate that the data learnt from the database can be used to structure the BBN,give insights on how human reliability pertaining to well activities can be modelled,and that the relative frequencies from the count analysis can act as initial data input for the proposed nodes.It is also proposed that the integrated treatment of various sources of information(database and expert judgement)through a BBN model can support the risk assessment of a dynamic situation such as offshore decommissioning.

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.

Bayesian belief-based model for reliability improvement of the digital reactor protection system

The digital reactor protection system(RPS)is one of the most important digital instrumentation and control(I&C)systems utilized in nuclear power plants(NPPs).It ensures a safe reactor trip when the safety-related parameters violate the operational limits and conditions of the reactor.Achieving high reliability and availability of digital RPS is essential to maintaining a high degree of reactor safety and cost savings.The main objective of this study is to develop a general methodology for improving the reliability of the RPS in NPP,based on a Bayesian Belief Network(BBN)model.The structure of BBN models is based on the incorporation of failure probability and downtime of the RPS I&C components.Various architectures with dual-state nodes for the I&C components were developed for reliability-sensitive analysis and availability optimization of the RPS and to demonstrate the effect of I&C components on the failure of the entire system.A reliability framework clarified as a reliability block diagram transformed into a BBN representation was constructed for each architecture to identify which one will fit the required reliability.The results showed that the highest availability obtained using the proposed method was 0.9999998.There are 120 experiments using two common component importance measures that are applied to define the impact of I&C modules,which revealed that some modules are more risky than others and have a larger effect on the failure of the digital RPS.

Factors related to the functionality of community-based rural water supply and sanitation program in Indonesia

This study used multinomial logistic regression and Bayesian belief networks(BBN)to analyze factors influenc-ing the functionality of the community-based rural drinking water supply and sanitation program(PAMSIMAS)in Indonesia.28,936 PAMSIMAS projects in 33 provinces in Indonesia were analyzed.The data indicates that 85.4%of the water supply systems were fully functioning,9.1%were partially functioning,and 5.5%were not functioning.In the regression analysis,good management is positively associated with functionality and a high investment per capita is negatively associated with the functionality.The latter suggests the need for comprehen-sive economic analysis in the feasibility study in scattered housing sites and remote-undeveloped areas.We also found that high community participation at the beginning of the project was associated with the not functioning system,while women’s participation was positively associated with the functionality.Furthermore,the household connection is more likely to be functioning than communal connection.BBN analysis shows if the beneficiaries do not pay for water,the probability of not functioning systems is 20 times higher than systems with fee collec-tion.Moreover,the combination of strong management,strong financial status,and household connection rather than communal connection increases the probability of fully functioning to 98%.Improvement of data collection is also necessary to monitor the current conditions of all PAMSIMAS systems in Indonesia.This study offers a country-level perspective for better implementation of the community-based rural water supply and sanitation program in developing countries.

基于贝叶斯网的作战效能评估方法研究

对贝叶斯网理论进行了拓展，建立了基于贝叶斯网的作战效能评估模型，并研究了模型的求解方法。最后，以反辐射导弹攻击雷达站作为算例进行了应用研究。

飞机作战效能评估中人的可靠性的引入方法

分析了目前的研究现状,提出了在飞机作战效能评估中引入人的可靠性的思想,分析了飞行员可靠性的特点,对在作战仿真和解析计算评估中的引入方法作了相应的探讨和研究。

基于贝叶斯置信网的日志服务系统容侵方法研究

文章针对服务器系统被攻破之后,如何保护服务器系统所记录的日志,为以后系统的恢复提供依据,并且提高系统自身生存能力的难点,提出将日志记录按照一定的格式进行分片,将不同的分片存储在不同的日志服务器上的容侵策略。当需要进行日志还原时,再将日志分片组合成原来的日志。构建了系统的异常发现贝叶斯网络模型,该模型根据用户访问日志服务器所提供的特征信息,可以判断出该次访问是否异常行为和所访问日志类型,从而在海量日志信息中快速定位受攻击的服务器及其日志片段,以最小的系统开销恢复可能已经被破坏掉的某一类日志记录。该方法在一定程度上保证了日志记录服务器中日志记录的准确性和正确性。

基于多智能体技术的反辐射导弹作战效能评估建模研究

分析了机载反辐射导弹的作战流程;建立了基于多智能体技术的复杂电磁环境下反辐射导弹作战效能评估模型框架;研究了基于Bayesian网的反辐射导弹作战效能评估模型。提出的建模方法对其他复杂装备系统作战效能评估研究具有参考作用。

Improving herdsmen’s well-being through scenario planning:A case study in Xilinhot City,Inner Mongolia Autonomous Region

Grassland ecosystems support well-being with food,shelter,income,and culture of herdsmen.While the associa-tion between ecosystem services and human well-being has been widely studied,such association is understudied in grassland ecosystems.This study aims to fill this gap through a case study of Xilinhot City,Inner Mongolia Autonomous Region,China.We examined the association between grassland provisioning services and herds-men’s well-being between 1985 and 2015 through participatory observations,interviews,surveys,and Bayesian belief network modeling.Considering the uncertainties of weather and sheep prices,we developed four scenarios to examine the future well-being of herdsmen.Our results show that the most important factor for herdsmen’s well-being was income,which is highly sensitive to the market price of sheep and precipitation.Considering the uncertainties of sheep prices and precipitation,scenario analysis revealed a divergence between income and well-being.While herdsmen’s income is most likely to increase with low precipitation and increased sheep prices,their well-being is most likely to improve with abundant precipitation and increased sheep prices.Based on our find-ings,we argue that developing alternative income sources(e.g.,tourism),reducing dependence on government subsidies through commercial insurance,and branding lamb with grassland ecosystem to alleviate the impact of price fluctuations would help improve herdsmen’s well-being in all scenarios.

Dempster-Shafer Evidence Theory and Study of Some Key Problems

As one of the most important mathematical methods, the Dempster-Shafer（D-S）evidence theory has been widely used in date fusion, risk assessment, target identification, knowledge reasoning,and other fields. This paper summarized the development and recent studies of the explanations of D-S model, evidence combination algorithms, and the improvement of the conflict during evidence combination, and also compared all explanation models,algorithms, improvements, and their applicable conditions. We are trying to provide a reference for future research and applications through this summarization.

Study on UAV Path Planning Approach Based on Fuzzy Virtual Force

This article proposes a novel fuzzy virtual force （FVF） method for unmanned aerial vehicle （UAV） path planning in compli-cated environment. An integrated mathematical model of UAV path planning based on virtual force （VF） is constructed and the corresponding optimal solving method under the given indicators is presented. Specifically,a fixed step method is developed to reduce computational cost and the reachable condition of path planning is proved. The Bayesian belief network and fuzzy logic reasoning theories are applied to setting the path planning parameters adaptively,which can reflect the battlefield situation dy-namically and precisely. A new way of combining threats is proposed to solve the local minima problem completely. Simulation results prove the feasibility and usefulness of using FVF for UAV path planning. Performance comparisons between the FVF method and the A＊ search algorithm demonstrate that the proposed approach is fast enough to meet the real-time requirements of the online path planning problems.

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.

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.