Fuzzy Structural Analysis Research Articles

Fuzzy Interpretive Structural Modeling regarding Factors Affecting Employees’ Experience Management in Mashhad University of Medical Sciences

Background: Employees are considered internal customers of the organization, and today, employee’s experience has become a prominent topic in the field of human resources. This study aims to model the factors affecting employees’ experience management in Mashhad University of Medical Sciences using fuzzy interpretive structural modeling. Methods: This applied study was descriptive-analytical conducted in 2023. 10 faculty members, managers, and staff of Mashhad University of Medical Sciences were selected using a purposive sampling method. Interpretive Structural Modeling (ISM) questionnaire consisting of a 7x7 matrix was then given to them, and they were asked to find the contextual relationship between the variables based on the pairwise comparison scale. In the analysis section, fuzzy interpretive structural modeling and MICMAC analysis were used to structure and analyze the status of the factors. Results: 7 organized themes (organizational, physical environment, technological, job, managerial, cultural, and individual factors) were identified for employees’ experience in Mashhad University of Medical Sciences. Findings showed that cultural and managerial factors are at the lowest level of the structure and have more influence and less dependence. In other words, these are the most influential factors on employees’ experience. Job, technology, and organizational factors were placed at the second level. In contrast, individual and physical factors were at the highest level of the structure and had less influence and more dependence on other factors. Conclusion: The findings indicate that Mashhad University of Medical Sciences should focus on strengthening and improving cultural and managerial factors to improve employees’ experience. Additionally, attention to job, technology, and organizational factors, along with addressing individual employee’s needs and providing a favorable physical environment, are essential for employees’ effective experience management at the university. This study, by providing a comprehensive model of factors influencing employees’ experience, assists universities and other organizations in gaining a deeper understanding of this concept and developing effective strategies for its enhancement.

Fuzzy Structural Analysis Using Improved Jaya-based Optimization Approach

A new approach to performing the α-level optimization in the fuzzy analysis of structural systems is developed in this study. The method uses a simple global optimizer, the Jaya algorithm, together with an innovative dimension reduction technique. The dimension reduction technique aims to transform the original large α-level optimization problem into a low-dimension one by making use of the monotonic behavior of the system output with respect to the input variables. Then, the Jaya algorithm is applied to solve the reduced max/min α-level optimization problems to determine the bounds of the fuzzy output. Two numerical examples, including a 2D truss and a 3D truss, with a relatively large number of fuzzy input variables are analyzed and the fuzzy displacements under static loads are predicted. It is demonstrated that the proposed approach can save a significant computational amount and also estimate the fuzzy displacement with high accuracy.

Multi-tool methodology to evaluate action levers to close the loop on critical materials – Application to precious metals used in catalytic converters

Implementing circular economy (CE) practices can lead to both environmental savings and competitive advantages for companies. While transitioning from a linear production system to a closed-loop system is not straightforward, adequate methodology and tools can support industrialists in this sustainable shift. This paper proposes a multi-tool approach to systematically identify, classify, and assess the contribution of influence parameters and action levers to close the loop on products and key materials. Industrial ecology and model-based engineering tools are combined to ensure a systemic analysis and evaluation. The developed multi-tool approach combines, in a stepwise methodology, material flow analysis, fuzzy cognitive mapping, structural analysis, and system dynamics, to model and qualify the impact of potential and promising CE strategies. To illustrate each step of this multi-tool methodology, a case study is carried out on a real-world industrial product: a catalytic converter, which contains a non-negligible amount of platinum, considered as a critical raw material by the European Commission. New insights to close the loop on platinum from catalytic converters are thus provided and discussed. Notably, the connections between key action levers to close the loop on platinum are identified and highlighted, including regulations to limit the number of exports, mandatory recycling and reuse rate, end-users behaviors, based on regulatory constraints and financial motivations, and platinum price fluctuation. These findings could help to generate and fine-tune an ad hoc system dynamics model to evaluate the impact of key action levers through more specific scenarios. For instance, the broader implication of this multi-tool methodology could support an original equipment manufacturer in the evaluation of potential CE strategies, through the simulation of selected action levers on the circularity and sustainability performance of their value chain. Ultimately, it could provide quantitative insights to relevant prospective questions, such as, what if a take-back scheme is proposed to augment the collection rate by a given percentage, or what if the design is modified for easy disassembly.

Enhanced uncertain structural analysis with time- and spatial-dependent (functional) fuzzy results

Abstract Uncertain structural analysis is the computation of uncertain structural response for uncertain input quantities. In terms of structural analysis, the result quantities (e.g. displacements, stresses, damages, …) are time- and spatial-dependent (τ and θ respectively), such that x u ↦ z u ( τ , θ ) needs to be solved. The usual procedure is to a priori select a small amount of Quantities of Interest (QoI), e.g. for a specific time and spatial location. For this small amount, the uncertainty analysis is performed, but these results do not represent the structural behaviour; they are just an excerpt based on a priori knowledge (in general not available). The consideration of time- and spatial-dependent results yields a high amount of uncertain result quantities. The main goal of this contribution is the formulation of time and spatial dependency based on the continuous space, which allows the general application to structural analysis. Furthermore, the computation of a large amount of fuzzy result quantities is addressed. Therefore, a fuzzy analysis on the basis of a specific fuzzy sampling is introduced. The third aspect discussed is the enhanced fuzzy structural analysis, an approach to compute, visualise and evaluate functional fuzzy results. The necessity and usefulness of these procedures, in terms of identifying failure modes and weak points, is discussed by two examples.

Modelling and analysis of energy efficiency drivers by fuzzy ISM and fuzzy MICMAC approach

The need nowadays has arisen which has forced today's generation to sit and think about taking pains regarding energy efficiency. This huge wastage of energy and the ever-increasing cost have not only hindered the smooth economical run but also have sent huge Greenhouse gas emissions into the environment. So, it has become inescapable for us to turn a blind eye. Keeping this thing in mind, this research paper presents identification drivers of energy efficiency which helps us in making a framework which could guide industries in applying the energy efficiency in their setups. These 17 drivers were levelled and their effect was noticed through fuzzy interpretive structural modelling (ISM) and fuzzy MICMAC analysis. The results reveal that inadequate availability of energy, excessive losses, and energy audits have high driving power and low dependence power and are at the lowest level in fuzzy ISM digraph, while government promotional benefits, management commitment, research development, and planning for future drivers have high dependence power and low driving power. The results were clear enough to suggest that which drivers we have to take in attention in order to ensure that the energy efficiency is applied in an industry.

Fuzzy relaxed-finite step size method to enhance the instability of the fuzzy first-order reliability method using conjugate discrete map

Fuzzy reliability analysis can be implemented using two discrete optimization maps in the processes of reliability and fuzzy analysis. Actually, the efficiency and robustness of the iterative reliability methods are two main factors in the fuzzy-based reliability analysis due to the huge computational burdens and unstable results. In the structural fuzzy reliability analysis, the first-order reliability method (FORM) using discrete nonlinear map can provide a C membership function. In this paper, a discrete nonlinear conjugate map is proposed using a relaxed-finite step size method for fuzzy structural reliability analysis, namely Fuzzy conjugate relaxed-finite step size method fuzzy CRS. A discrete conjugate map is stabilized using two adaptive factors to compute the relaxed factor and step size in FORM. The framework of the proposed fuzzy structural reliability method is established using two linked iterative discrete maps as an outer loop, which constructs the membership function of the response using alpha level set optimization based on genetic operator, and the inner loop, implemented for reliability analysis using proposed conjugate relaxed-finite step size method. The fuzzy CRS and fuzzy HL-RF methods are compared to evaluate the membership functions of five structural problems with highly nonlinear limit state functions. Results demonstrated that the fuzzy CRS method is computationally more efficient and is strongly more robust than the HL-RF for fuzzy-based reliability analysis of the nonlinear structural reliability problems.

Fuzzy time dependent structural reliability analysis using alpha level set optimization method based on genetic algorithm

Fuzzy time dependent structural reliability analysis using alpha level set optimization method based on genetic algorithm

Supporting the selection of a variant of the adaptation of a historical building with the use of fuzzy modelling and structural analysis

Abstract Changing the function of a historical building through adapting it to new use is one of the more effective ways of extending the life-cycle of degraded historical structures. The process of selecting a new method of use of a historical building requires the involvement of conscious decision-makers, whose view of the benefits associated with the adaptation reaches beyond economic gain, but also includes the importance of the protection of cultural heritage, as well as other benefits associated with the concept of sustainable development. The will to include interdisciplinary benefits, as well as the distinct nature of the decision-making problem that is the imprecise nature of information and the conflicts between the parties invested in the decision-making process adds to the complexity of the problem. The proper approach to these kinds of decision-making problems is making an attempt at deconstructing the problem and expressing it in a synthetic manner in the form of a model, followed by performing its analysis. The literature on the subjects provides a number of multi-criteria methods that can be used to support making decisions of this type. However, most of these methods do not take into account the relationships between the selection criteria, while none of them is suited to processing the imprecise and uncertain character of the initial data, which is a typical feature of decision-making problem at hand. Wishing to address these issues, the authors of this article propose a complex, multi-criteria approach that incorporates the above elements. Finally, the authors provide a practical example of the practical implementation of the aforementioned approach as a tool supporting the selection of a new form of use for the Great Armoury historical building, located in Gdansk, Poland.

Approximate fuzzy analysis of linear structural systems applying intervening variables

This contribution presents an approach for performing approximate fuzzy structural analysis of linear static systems. The proposed approach is based on an approximate representation of the structural displacement (which is constructed using intervening variables) and is capable of dealing with uncertainty present in both structural parameters and loading. The most salient features of the proposed approach are its capability of capturing the nonlinearity of the structural response with respect to uncertain parameters and its numerical efficiency.

Application of Fuzzy Structural Analysis for Damage Prediction Considering Uncertain S/N Curve

The paper presents chosen traditional (based on probability theory) and non-traditional (based on possibility theory) computational tools for analysis of the material, geometric or loading uncertainties in mechanical structures. Uncertainties are introduced as bounded possible values intervals or as fuzzy sets, assuming possibility theory and as random parameters in the case of the probability theory. The main goal was to propose numerical algorithms for fuzzy analysis of stochastic oscillated FE model (truss structure) and to predict fuzzy fatigue damage considering fuzzy S/N curve.

Fuzzy structural analysis based on fundamental reliability concepts

Fuzzy structural analysis is oriented to estimate the membership function of an output structural variable given the membership functions of the input ones, which are discretized in so-called α-cut levels. Such an estimation requires the solution of two optimization problems for each of them. In this paper an alternative approach is presented. It consists in solving a single optimization problem for the entire problem, which is that of FORM (first-order reliability analysis). It is shown that FORM solution has a valuable property, namely that it allows organizing the order statistics of the output variable along the design point vector. This property is exploited by a nonlinear projection of a large amount of standard Gaussian numbers, truncated according to the membership functions, onto a bi-dimensional space. From this mapping the samples necessary for directly solving the optimization problems are easily drawn using two curves, whose equations are derived from a similar transformation of the second-order approximation of the limit state function. A detailed structural example shows that the desired membership function of the response can be accurately estimated by the proposed method.

Numerical structural monitoring with the uncertainty model fuzzy randomness

In this paper, an approach is introduced in which uncertain structural analysis, assessment of damage state and performance by indicators as well as prognosis of future structural behaviour and lifetime are combined. This approach is referred to as numerical structural monitoring and may be supported by results of in-situ monitoring. The numerical structural monitoring is based on the numerical simulation of the load and modification process. The parameters of this structural process are normally uncertain parameters which are mathematically described by means of imprecise probabilities. Here, the generalised uncertainty model fuzzy randomness is applied. A fuzzy stochastic structural analysis in the time domain is adopted to take into account fuzzy randomness of the load and modification process in the numerical simulation.

Fuzzy Structural Analysis of a Tuned Mass Damper Subject to Random Vibration

The uncertainty is a typical feature of each human activity since the greatest part of the information is always affected by a sure level of scattering. Different methodologies which deal with the uncertainty of the real problems exist. The principal aim of this paper is to present an innovative hybrid approach which combines fuzzy and stochastic theories in facing the structural analysis of a tuned mass damper subject to a dynamic random load, modelled by a modulated filtered white noise. In this work the parameters involved in the structural analysis will be considered uncertain and supposed fuzzy sets to take into account the effects of lexical and informal uncertainties which cannot be studied in a probabilistic way. The system analysis is conducted by means of α‐level optimization technique. Successively, a numerical example is presented to show the effectiveness of the proposed procedure. Moreover, a sensitivity analysis is performed to expose the variation of the structural response membership function considering different input values. Finally, a comparison between the response nominal value and the fuzzificated one is proposed to obtain a structural amplification factor.

Fuzzy dynamic structural analysis of two-dimensional frame

In this paper, a dynamic analysis based on the fuzzy set theory is presented as a possible complementary tool to the classical stochastic methods for dynamic analyses. Material parameters of a structure are influenced by uncertainties and therefore they are considered to be fuzzy quantities with given distributions, that means fuzzy numbers with given membership functions. The fuzzy dynamic analysis is conducted with help of fuzzy arithmetic defined on the so-called -cuts. The results of the analysis are also obtained in the form of fuzzy numbers, which compared to the stochastic methods is less computationaly expensive while at the same time they still provide information about the distribution of a quantity. This method is demonstrated on an analysis of a two-dimensional frame subjected to possible seismic load, where the uncertain eigenmodes and eigenfrequencies are used in the modal analysis.

The negotiation position amid member states of the EU

The European integration process is very important and it has been paid attention to for the last 15 years. The abstract deals with the negotiation field and position in the structure of the current expanded EU. For better orientation in this equivocal situation, a modern cartography method of relationship in the arbitrary group called dynamic sociometry was used. The method is based on classical sociometry Morena and furthermore it uses the instrument of fuzzy set, typology and structural analysis. The output of this method is a sociomap. The map holds information on the relative closeness or the distance of individual elements, their configuration but also some quality information. The graphic chart is similar to a topographic map. In our case, the sociomap was created from the data of foreign business among the member countries. The following analysis of the sociomap we detected and described characteristic features of the analysed group. It consists of formal and informal links in the group, the role and the position of each member within the group, the structure and relations in the group. In the concrete, we attained data to answer the questions about the present climate in interrelationships in the EU, which means the relationships among the members as the whole entities but also the relationships separately among members themselves. The position analysis of the Czech Republic in the system of the created sociomaps was considered as very important. According to the allocation of the Czech Republic in sociomaps, we easily defined the position of the state, the closest partners for negotiation, and also the possible problem relations which might complicate the negotiation in the EU. From the resultant sociomaps, we also tried to determine the sociometric star, i.e. the leader of the European Community. We anticipated that it would be rather an aggregation of several states than an individual state. The contribution focuses on the negotiation field and position in the structure of the current expanded EU. The sociomap created using foreign business data of member states gives the bases for answering questions about present climate in interrelationships in the EU which means the relationships between the members as whole entities but also the relationships separately between the members themselves.

Uncertain structural design based on nonlinear fuzzy analysis

AbstractIn this paper a comprehensive application of fuzzy set theory in structural engineering is presented. Non‐stochastic uncertainty is quantified using fuzzy values. The fuzzy structural parameters are processed on the basis of a generally applicable numerical method for arbitrary linear and nonlinear fuzzy structural analyses. This method is formulated in terms of α‐level optimization combined with a modified evolution strategy. The fuzzy structural responses are compared with permissible values and assessed using an analog to the Shannon entropy and defuzzification algorithms. Referring to permissible fuzzy structural responses uncertain structural design parameters are derived by applying a fuzzy cluster analysis to the fuzzy structural parameters. The nonlinear fuzzy structural analysis including uncertain structural design is demonstrated by way of an example.

Discussion on “Structural reliability analysis through fuzzy number approach, with application to stability”

We comment on the fuzzy number approach presented by Marco Savoia [Comput. Struct. 80 (2002) 1087]. Savoia’s statement regarding computational procedures for fuzzy structural analysis is corrected. A general and powerful numerical algorithm for nonlinear fuzzy structural analysis is presented.

Fuzzy structural analysis using α-level optimization

In this paper new concepts and developments are presented for structural analysis involving uncertain parameters. Based on a classification of the uncertainties in structural analysis the uncertainty “fuzziness” is identified and its quantification is demonstrated. On the basis of fuzzy set theory a general method for fuzzy structural analysis is developed and formulated in terms of the α-level optimization with the application of a modified evolution strategy. Every known analysis algorithm for the realistic simulation of load-bearing behavior may be applied in the fuzzy structural analysis in the sense of a deterministic fundamental solution. By way of example, geometrically and physically nonlinear algorithms are adopted in the presented study as a deterministic fundamental solution for the analysis of steel and reinforced concrete structures. The paper also describes coupling between α-level optimization and the deterministic fundamental solution.

Possibility Theory Based Safety Assessment

An assessment of the safety of structures may be carried out on the basis of different known safety concepts (global safety factor, semiprobabilistic approach using partial safety factors, probabilistic approximation solution using first‐ and second‐order reliability theory, probabilistic “exact” solution). The application of these concepts presupposes special knowledge concerning input values, e.g., permissible or prescribed values, quantile values, distribution functions. This especially applies to probabilistic methods. Inaccuracies and statistically nondescribable uncertainties in the input data are either ignored in these methods or only accounted for approximately using crisp bounds.Fuzzy set theory enables such uncertainties to be described mathematically and processed in the analysis of structures. The uncertain results of fuzzy structural analysis may be evaluated by various methods. In this article a safety assessment method is described based on possibility theory. In contrast to the above‐mentioned concepts, the safety assessment of structures described here takes into account nonstochastic uncertainties and subjective estimates of objective values by experts based on possibility theory. It is possible by this means to obtain sufficiently reliable descriptions of the input data for further processing. The uncertainties introduced into the analysis are later reflected in the results. A realistic description of system behavior is obtained by applying high‐quality algorithms in the structural analysis. The uncertain results serve as a starting point for the safety assessment. The method described here forms a supplement to safety concepts already in use.