Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network

<abstract> <p>This paper proposes a probabilistic graphical model that integrates interpretive structural modeling (ISM) and Bayesian belief network (BBN) approaches to predict cone penetration test (CPT)-based soil liquefaction potential. In this study, an ISM approach was employed to identify relationships between influence factors, whereas BBN approach was used to describe the quantitative strength of their relationships using conditional and marginal probabilities. The proposed model combines major causes, such as soil, seismic and site conditions, of seismic soil liquefaction at once. To demonstrate the application of the propose framework, the paper elaborates on each phase of the BBN framework, which is then validated with historical empirical data. In context of the rate of successful prediction of liquefaction and non-liquefaction events, the proposed probabilistic graphical model is proven to be more effective, compared to logistic regression, support vector machine, random forest and naive Bayes methods. This research also interprets sensitivity analysis and the most probable explanation of seismic soil liquefaction appertaining to engineering perspective.</p> </abstract>

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Probabilistic Evaluation of CPT-based Seismic Soil Liquefaction Potential: Towards the Integration of Interpretive Structural Modeling and Bayesian Belief Network

10.21203/rs.3.rs-245487/v1 ◽

2021 ◽

Author(s):

Mahmood Ahmad ◽

Xiao-Wei Tang ◽

Feezan Ahmad ◽

Nima Pirhadi ◽

Xusheng Wan ◽

...

Keyword(s):

Graphical Model ◽

Influence Factors ◽

Structural Modeling ◽

Bayesian Belief Network ◽

Soil Liquefaction ◽

Support Vector ◽

Probabilistic Graphical Model ◽

Interpretive Structural Modeling ◽

Liquefaction Potential ◽

Belief Network

Abstract This paper proposes a probabilistic graphical model that integrates interpretive structural modeling (ISM) and Bayesian belief network (BBN) approaches to predict CPT-based soil liquefaction potential. In this study, an ISM approach was employed to identify relationships between influence factors, whereas BBN approach was used to describe the quantitative strength of their relationships using conditional and marginal probabilities. The proposed model combines major causes, such as soil, seismic and site conditions, of seismic soil liquefaction at once. To demonstrate the application of the propose framework, the paper elaborates on each phase of the BBN framework, which is then validated with historical empirical data. In context of the rate of successful prediction of liquefaction and non-liquefaction events, the proposed probabilistic graphical model is proven to be more effective, compared to logistic regression, support vector machine, random forest and naïve Bayes methods. This research also interprets sensitivity analysis and the most probable explanation of seismic soil liquefaction appertaining to engineering perspective.

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Evaluating Seismic Soil Liquefaction Potential Using Bayesian Belief Network and C4.5 Decision Tree Approaches

Applied Sciences ◽

10.3390/app9204226 ◽

2019 ◽

Vol 9 (20) ◽

pp. 4226 ◽

Cited By ~ 2

Author(s):

Mahmood Ahmad ◽

Xiao-Wei Tang ◽

Jiang-Nan Qiu ◽

Feezan Ahmad

Keyword(s):

Decision Tree ◽

Domain Knowledge ◽

Probabilistic Reasoning ◽

Learning Algorithm ◽

Bayesian Belief Network ◽

Soil Liquefaction ◽

Point Of View ◽

Liquefaction Potential ◽

Belief Network ◽

C4.5 Decision Tree

Liquefaction is considered a damaging phenomenon of earthquakes and a major cause of concern in civil engineering. Therefore, its predictory assessment is an essential task for geotechnical experts. This paper investigates the performance of Bayesian belief network (BBN) and C4.5 decision tree (DT) models to evaluate seismic soil liquefaction potential based on the updated and relatively large cone penetration test (CPT) dataset (which includes 251 case histories), comparing them to a simplified procedure and an evolutionary-based approach. The BBN model was developed using the K2 machine learning algorithm and domain knowledge (DK) with data fusion methodology, while the DT model was created using a C4.5 algorithm. This study shows that the BBN model is preferred over the others for evaluation of seismic soil liquefaction potential. Owing to its overall performance, simplicity in practice, data-driven characteristics, and ability to map interactions between variables, the use of a BBN model in assessing seismic soil liquefaction is quite promising. The results of a sensitivity analysis show that ‘equivalent clean sand penetration resistance’ is the most significant factor affecting liquefaction potential. This study also interprets the probabilistic reasoning of the robust BBN model and most probable explanation (MPE) of seismic soil liquefied sites, based on an engineering point of view.

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Learning a Causal Model from Household Survey Data by Using a Bayesian Belief Network

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1836-05 ◽

2003 ◽

Vol 1836 (1) ◽

pp. 29-36 ◽

Cited By ~ 5

Author(s):

Francisco J. Torres ◽

Manfred Huber

Keyword(s):

Graphical Model ◽

Causal Model ◽

Household Survey ◽

Bayesian Belief Network ◽

Generation Process ◽

Data Set ◽

Belief Network ◽

Representation Structure ◽

Key Variables ◽

Household Survey Data

A Bayesian belief network (BBN) is a modeling and knowledge-representation structure used in artificial intelligence that consists of a graphical model depicting probabilistic relationships among variables of interest. This graphical model is a valuable tool for representing the causal relationships in a given set of variables. Because the number of possible BBNs for a given data set is exponential with respect to the number of variables, learning a BBN from data is a difficult and resource-consuming task. A greedy algorithm that automatically constructs a BBN from a data set of cases obtained from a household survey was implemented. The resulting BBN shows the dependencies among key variables that are associated with the trip-generation process.

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Interpretive structural modeling of knowledge network in car industry’ R&D centers

Independent Journal of Management & Production ◽

10.14807/ijmp.v9i2.693 ◽

2018 ◽

Vol 9 (2) ◽

pp. 562

Author(s):

Ali Rezaeian ◽

Rouhollah Bagheri

Keyword(s):

Network Model ◽

Applied Research ◽

Graphical Model ◽

Structural Modeling ◽

Knowledge Network ◽

Key Factors ◽

Interpretive Structural Modeling ◽

Car Industry ◽

Micmac Analysis

The current research has been done with the aim of knowledge network interpretive structural modeling in car industry’s R&D centers. The key factors for implementing a knowledge network in car industry’s R&D centers have been determined and then the final graphical model has been drawn by Interpretive Structural Modeling (ISM) approach.The method of the current applied research includes a survey of experts and then the variables extracted through investigating research background, after that the MATLAB R2013 software is used for making compatible matrix as well as drawing graphical relations of the model by Interpretive Structural Modeling approach.After studying related works & interviewing with under-studied firms’ managers, interpretive structural modeling (ISM) & MICMAC analysis was used to generate a model for knowledge network. Previous studies had not investigated the knowledge network in car industry’s R&D centers; however, the present study implemented the knowledge network model in R&D Centers.

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RUL Prediction of Bearings Based on Mixture of Gaussians Bayesian Belief Network and Support Vector Data Description

Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems - Communications in Computer and Information Science ◽

10.1007/978-981-10-2666-9_13 ◽

2016 ◽

pp. 118-130

Author(s):

Qianhui Wu ◽

Yu Feng ◽

Biqing Huang

Keyword(s):

Bayesian Belief Network ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description ◽

Belief Network ◽

Mixture Of Gaussians

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RUL Prediction of Bearings Based on Mixture of Gaussians Bayesian Belief Network and Support Vector Data Description

Challenges and Opportunity with Big Data - Lecture Notes in Computer Science ◽

10.1007/978-3-319-61994-1_14 ◽

2017 ◽

pp. 139-151

Author(s):

Qianhui Wu ◽

Yu Feng ◽

Biqing Huang

Keyword(s):

Bayesian Belief Network ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description ◽

Belief Network ◽

Mixture Of Gaussians

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Interpretive Structural Modeling and MICMAC Analysis for Identifying and Benchmarking Significant Factors of Seismic Soil Liquefaction

Applied Sciences ◽

10.3390/app9020233 ◽

2019 ◽

Vol 9 (2) ◽

pp. 233 ◽

Cited By ~ 3

Author(s):

Mahmood Ahmad ◽

Xiao-Wei Tang ◽

Jiang-Nan Qiu ◽

Feezan Ahmad

Keyword(s):

Earthquake Engineering ◽

Matrix Multiplication ◽

Structural Modeling ◽

Standard Penetration Test ◽

Soil Liquefaction ◽

Influential Factors ◽

Soil Parameters ◽

Interpretive Structural Modeling ◽

Ground Acceleration ◽

Significant Factors

Seismic soil liquefaction is considered as one of the most complex geotechnical earthquake engineering problems owing to the uncertainty and complexity involved in soil parameters, seismic parameters, and site condition factors. Each one of these parameters contains a variety of factors that trigger liquefaction and have varying degrees of importance. However, estimating accurate and reliable liquefaction-induced hazards requires identification and benchmarking of the most influential factors that control soil liquefaction. Seismic soil liquefaction factors were identified by Systematic Literature Review (SLR) approach and analyzed through Interpretive Structural Modeling (ISM) and the Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) methodologies. The ISM model presented the relationships between fifteen seismic soil liquefaction factors and their benchmarking position from higher to lower-level significant factors in hierarchy. MICMAC is used to examine the strength of the relationship between seismic soil liquefaction significant factors based on their driving and dependence power. This research characterizes the identification and benchmarking of the seismic soil liquefaction factors and their relationships. The results show that the factors—duration of earthquake, peak ground acceleration, drainage condition, and standard penetration test (SPT) blow counts—influence seismic soil liquefaction directly and soil type is the governing factor that forms the base of the ISM hierarchy and consequently triggers seismic soil liquefaction. The results provide a more accurate way of selecting significant factors for establishment of seismic soil liquefaction potential and liquefaction-induced hazards risk assessment models.

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