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

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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Application of total interpretive structural modeling for analyzing factors of additive manufacturing and industry 4.0 integration

Rapid Prototyping Journal ◽

10.1108/rpj-02-2021-0028 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Vishal Ashok Wankhede ◽

Vinodh S.

Keyword(s):

Additive Manufacturing ◽

Industry 4.0 ◽

Structural Model ◽

Matrix Multiplication ◽

Structural Modeling ◽

Influential Factors ◽

Time Data ◽

Interpretive Structural Modeling ◽

Content Type ◽

Micmac Analysis

Purpose The purpose of this paper is to develop a model based on the total interpretive structural modeling (TISM) approach for analysis of factors of additive manufacturing (AM) and industry 4.0 (I4.0) integration. Design/methodology/approach AM integration with I4.0 is attributed due to various reasons such as developing complex shapes with good quality, real-time data analysis, augmented reality and decentralized production. To enable the integration of AM and I4.0, a structural model is to be developed. TISM technique is used as a solution methodology. TISM approach supports establishing a contextual relationship-based structural model to recognize the influential factors. Cross-impact matrix multiplication applied to classification (MICMAC) analysis has been used to validate the TISM model and to explore the driving and dependence power of each factor. Findings The derived structural model indicated the dominant factors to be focused on. Dominant factors include sensor integration (F9), resolution (F12), small build volumes (F19), internet of things and lead time (F14). MICMAC analysis showed the number of driving, dependent, linkage and autonomous factors as 3, 2, 12 and 3, respectively. Research limitations/implications In the present study, 20 factors are considered. In the future, additional factors could be considered based on advancements in I4.0 technologies. Practical implications The study has practical relevance as it had been conducted based on inputs from industry practitioners. The industry decision-makers and practitioners may use the developed TISM model to understand the inter-relationship among the factors to take appropriate measures before adoption. Originality/value The study on developing a structural model for analysis of factors influencing AM and I4.0 is the original contribution of the authors.

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Supply Chain Finance Factors: An Interpretive Structural Modeling Approach

Central European Management Journal ◽

10.7206/cemj.2658-0845.42 ◽

2021 ◽

Vol 29 (1) ◽

pp. 88-111

Author(s):

Zericho Marak ◽

◽

Deepa Pillai ◽

Keyword(s):

Decision Making ◽

Supply Chain ◽

Information Sharing ◽

Structural Modeling ◽

Influential Factors ◽

Previous Literature ◽

Critical Factors ◽

Interpretive Structural Modeling ◽

Modeling Methodology ◽

Supply Chain Finance

Purpose: The present study aims to identify the critical factors of supply chain finance and the interrelationship between the factors using interpretive structural modeling. Methodology: Factors of supply chain finance were identified from the literature and experts from both industry and academia were consulted to assess the contextual relationships between the factors. Then, we applied interpretive structural modeling to examine the interrelationships between these factors and find out the critical factors. Findings: The model outcome indicates information sharing and workforce to be the most influential factors, followed by the automation of trade and financial attractiveness. Originality/value: Previous literature identified various factors that influence supply chain finance. However, studies showing interrelationships between these factors are lacking. This study is unique in the field as it applies total interpretive structural modeling for assessing the factors that affect supply chain finance. Our model will aid practitioners’ decision-making and the adoption of supply chain finance by providing a necessary framework.

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Soil Liquefaction Assessment Using Soft Computing Approaches Based on Capacity Energy Concept

Geosciences ◽

10.3390/geosciences10090330 ◽

2020 ◽

Vol 10 (9) ◽

pp. 330

Author(s):

Zhixiong Chen ◽

Hongrui Li ◽

Anthony Teck Chee Goh ◽

Chongzhi Wu ◽

Wengang Zhang

Keyword(s):

Relative Density ◽

Soft Computing ◽

Earthquake Engineering ◽

Textural Properties ◽

Confining Pressure ◽

Soil Liquefaction ◽

Multivariate Adaptive Regression Splines ◽

Soil Parameters ◽

Liquefaction Resistance ◽

Energy Concept

Soil liquefaction is one of the most complicated phenomena to assess in geotechnical earthquake engineering. The conventional procedures developed to determine the liquefaction potential of sandy soil deposits can be categorized into three main groups: Stress-based, strain-based, and energy-based procedures. The main advantage of the energy-based approach over the remaining two methods is the fact that it considers the effects of strain and stress concurrently unlike the stress or strain-based methods. Several liquefaction evaluation procedures and approaches have been developed relating the capacity energy to the initial soil parameters, such as the relative density, initial effective confining pressure, fine contents, and soil textural properties. In this study, based on the capacity energy database by Baziar et al. (2011), analyses have been carried out on a total of 405 previously published tests using soft computing approaches, including Ridge, Lasso & LassoCV, Random Forest, eXtreme Gradient Boost (XGBoost), and Multivariate Adaptive Regression Splines (MARS) approaches, to assess the capacity energy required to trigger liquefaction in sand and silty sands. The results clearly prove the capability of the proposed models and the capacity energy concept to assess liquefaction resistance of soils. It is also proposed that these approaches should be used as cross-validation against each other. The result shows that the capacity energy is most sensitive to the relative density.

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Total Interpretive Structural Modeling (TISM): approach and application

Journal of Advances in Management Research ◽

10.1108/jamr-10-2016-0087 ◽

2017 ◽

Vol 14 (2) ◽

pp. 162-181 ◽

Cited By ~ 24

Author(s):

J. Jena ◽

Sumati Sidharth ◽

Lakshman S. Thakur ◽

Devendra Kumar Pathak ◽

V.C. Pandey

Keyword(s):

Design Methodology ◽

Matrix Multiplication ◽

Structural Modeling ◽

Decision Makers ◽

Interpretive Structural Modeling ◽

Content Type ◽

Mutual Dependence ◽

Step Procedure ◽

Power And Dependence ◽

Practical Implications

Purpose The purpose of this paper is to elucidate the methodology of total interpretive structural modeling (TISM) in order to provide interpretation for direct as well as significant transitive linkages in a directed graph. Design/methodology/approach This study begins by unfolding the concepts and advantages of TISM. The step-by-step methodology of TISM is exemplified by employing it to analyze the mutual dependence among inhibitors of smartphone manufacturing ecosystem development (SMED). Cross-impact matrix multiplication applied to the classification analysis is also performed to graphically represent these inhibitors based on their driving power and dependence. Findings This study highlights the significance of TISM over conventional interpretive structural modeling (ISM). The inhibitors of SMED are explored by reviewing existing literature and obtaining experts’ opinions. TISM is employed to classify these inhibitors in order to devise a five-level hierarchical structure based on their driving power and dependence. Practical implications This study facilitates decision makers to take required actions to mitigate these inhibitors. Inhibitors (with strong driving power), which occupy the bottom level in the TISM hierarchy, require more attention from top management and effective monitoring of these inhibitors can assist in achieving the organizations’ goals. Originality/value By unfolding the benefits of TISM over ISM, this study is an endeavor to develop insights toward utilization of TISM for modeling inhibitors of SMED. This paper elaborates step-by-step procedure to perform TISM and hence makes it simple for researchers to understand its concepts. To the best of the authors’ knowledge, this is the first study that analyzes the inhibitors of SMED by utilizing TISM approach.

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A Neural Network Model for Evaluating Gravel Liquefaction Using Dynamic Penetration Test

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.275-277.2620 ◽

2013 ◽

Vol 275-277 ◽

pp. 2620-2623

Author(s):

Qing Xu ◽

Fei Kang ◽

Jun Jie Li

Keyword(s):

Neural Networks ◽

Earthquake Engineering ◽

Soil Liquefaction ◽

Soil Parameters ◽

Success Rates ◽

Penetration Test ◽

Liquefaction Potential ◽

Gravelly Soil ◽

Gravelly Soils ◽

Dynamic Penetration

Evaluation of liquefaction potential of soils is important in geotechnical earthquake engineering. Significant phenomena of gravelly soil liquefaction were reported in 2008 Wenchuan earthquake. Thus, further studies on the liquefaction potential of gravelly soil are needed. This paper investigates the potential of artificial neural networks-based approach to assess the liquefaction potential of gravelly soils form field data of dynamic penetration test. The success rates for occurrence and non-occurrence of liquefaction cases both are 100%. The study suggests that neural networks can successfully model the complex relationship between seismic parameters, soil parameters, and the liquefaction potential of gravelly soils.

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

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2021454 ◽

2021 ◽

Vol 18 (6) ◽

pp. 9233-9252

Author(s):

Mahmood Ahmad ◽

◽

Feezan Ahmad ◽

Jiandong Huang ◽

Muhammad Junaid Iqbal ◽

...

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> <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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SmartISM: Implementation and Assessment of Interpretive Structural Modeling

Sustainability ◽

10.3390/su13168801 ◽

2021 ◽

Vol 13 (16) ◽

pp. 8801

Author(s):

Naim Ahmad ◽

Ayman Qahmash

Keyword(s):

Scoping Review ◽

Success Factors ◽

Matrix Multiplication ◽

Structural Modeling ◽

Interpretive Structural Modeling ◽

Specific Domain ◽

Final Model ◽

Critical Success ◽

Visual Basic For Applications ◽

Correct Application

Interpretive Structural Modeling (ISM) is a technique to establish the interrelationships between elements of interest in a specific domain through experts’ knowledge of the context of the elements. This technique has been applied in numerous domains and the list continues to grow due to its simplistic concept, while sustainability has taken the lead. The partially automated or manual application of this technique has been prone to errors as witnessed in the literature due to a series of mathematical steps of higher-order computing complexity. Therefore, this work proposes to develop an end-to-end graphical software, SmartISM, to implement ISM technique and MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to classification)), generally applied along with ISM to classify variables. Further, a scoping review has been conducted to study the applications of ISM in the previous studies using Denyer and Tranfield’s (2009) framework and newly developed SmartISM. For the development of SmartISM, Microsoft Excel software has been used, and relevant algorithms and VBA (Visual Basic for Applications) functions have been illustrated. For the transitivity calculation the Warshall algorithm has been used and a new algorithm reduced conical matrix has been introduced to remove edges while retaining the reachability of variables and structure of digraph in the final model. The scoping review results demonstrate 21 different domains such as sustainability, supply chain and logistics, information technology, energy, human resource, marketing, and operations among others; numerous types of constructs such as enablers, barriers, critical success factors, strategies, practices, among others, and their numbers varied from 5 to 32; number of decision makers ranged between 2 to 120 with a median value of 11, and belong to academia, industry, and/or government; and usage of multiple techniques of discourse and survey for decision making and data collection. Furthermore, the SmartISM reproduced results show that only 29 out of 77 studies selected have a correct application of ISM after discounting the generalized transitivity incorporation. The outcome of this work will help in more informed applications of this technique in newer domains and utilization of SmartISM to efficiently model the interrelationships among variables.

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Modelling of Cloud Computing Enablers Using MICMAC Analysis and TISM

Cloud Security ◽

10.4018/978-1-5225-8176-5.ch045 ◽

2019 ◽

pp. 876-888 ◽

Cited By ~ 1

Author(s):

Nitin Chawla ◽

Deepak Kumar

Keyword(s):

Cloud Computing ◽

Matrix Multiplication ◽

Structural Modeling ◽

Relationship Matrix ◽

Interpretive Structural Modeling ◽

Micmac Analysis ◽

Business Efficiency ◽

It Departments ◽

Power And Dependence ◽

The Relationship

This article describes how Cloud Computing is not just a buzzword but a shift from IT departments to the outsourcing vendors without impacting business efficiency. Some organizations are moving towards cloud computing but many have resistance to adopting cloud computing due to limitations in knowledge and awareness of the classifying elements, which effect decisions on the acceptance of cloud computing. Therefore, this article has focused on accumulating the elements, which can act as enablers, by reviewing existing literature and studies from both professional and academic viewpoints. All the identified enablers have been structurally modeled to develop the relationship matrix and establish the driving power and dependence power of every element. This is done by employing Total Interpretive Structural Modeling (TISM) and Cross Impact Matrix Multiplication Applied to Classification (MICMAC) analysis.

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