scholarly journals MODELLING OF TIME, COST AND RISK OF CONSTRUCTION WITH USING FUZZY LOGIC

2021 ◽  
Vol 27 (6) ◽  
pp. 412-426
Author(s):  
Edyta Plebankiewicz ◽  
Krzysztof Zima ◽  
Damian Wieczorek
Keyword(s):  
Risk Factors ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Cost Calculation ◽  
Time Cost ◽  
Investment Process ◽  
The Cost ◽  
Available Information ◽  
Construction Schedule

The paper presents an overview of the literature from recent years devoted to planning the time, costs and risk of a construction investment using fuzzy logic. It also presents three own original models concerning the issue. The first model is used to build a fuzzy construction schedule taking into account fuzzy norms and the number of workers. The costing model uses fuzzy inference from CBR cases. The aim was to increase the accuracy and correctness of the cost calculation performed for the investor in the construction and investment process with a certain degree of vagueness of the available information about materials. In the last of the presented models, fuzzy sets were used to assess the effects of technological and construction (implementation) risk factors. The presented examples prove the usefulness of fuzzy logic in solving problems in construction, where we have incomplete and imprecise information.

scholarly journals Issues of Estimating the Quality of the Facilities Management of the Emercom of Russia at the Elimination of the Emergency Situations Consequences

2018 ◽  
Vol 7 (4.38) ◽  
pp. 704
Author(s):  
Valeriy Borisovich Vilkov ◽  
Andrey Kliment’evich Chernykh ◽  
Alexander Alekseevich Tarantsev ◽  
Yuri Evgenievich Aktersky ◽  
Ilya Danilovich Cheshko
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Facilities Management ◽  
Inference Algorithm ◽  
Emergency Situations ◽  
The Russian Federation ◽  
Civil Defence ◽  
Sets Theory

The article deals with the problem of multiobjective optimization with regard to the decision making on the use of the forces and facilities of the EMERCOM of Russia (Ministry of the Russian Federation for Affairs for Civil Defence, Emergencies and Elimination of Consequences of Natural Disasters). The purpose of the article is to create a method for prompt and reasonable calculations when making a decision on the use of the EMERCOM forces and facilities to eliminate the consequences of emergency situations. The proposed method uses fuzzy sets, fuzzy logic, and the Mamdani fuzzy inference algorithm. The work gives a substantial example illustrating the application of the mentioned theory to solve the problem of choosing the optimal version of the task performed by the facilities of the EMERCOM of Russia. Regarding the novelty, it should be noted that the quality characteristics of the solutions are fuzzy and not unambiguously defined, and therefore allow applying the effective mathematical apparatus of fuzzy sets theory, fuzzy logic and the Mamdani fuzzy inference algorithm in solving this problem. 

Some Recent Defuzzification Methods

2017 ◽  
pp. 31-48 ◽  
Author(s):  
Harendra Kumar
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Fuzzy Number ◽  
Fuzzy Inference ◽  
Fuzzy Logic System ◽  
Single Number ◽  
Logic System

Defuzzification is a process that converts a fuzzy set or fuzzy number into a crisp value or number. Defuzzification is used in fuzzy modeling and in fuzzy control system to convert the fuzzy outputs from the systems to crisp values. This process is necessary because all fuzzy sets inferred by fuzzy inference in the fuzzy rules must be aggregated to produce one single number as the output of the fuzzy model.There are numerous techniques for defuzzifying a fuzzy set; some of the more popular techniques are included in fuzzy logic system. In the present chapter some recent defuzzification methods used in the literature are discussed with examples.

Some Recent Defuzzification Methods

Fuzzy Systems ◽  
2017 ◽  
pp. 1003-1019
Author(s):  
Harendra Kumar
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Fuzzy Number ◽  
Fuzzy Inference ◽  
Fuzzy Logic System ◽  
Single Number ◽  
Logic System

Defuzzification is a process that converts a fuzzy set or fuzzy number into a crisp value or number. Defuzzification is used in fuzzy modeling and in fuzzy control system to convert the fuzzy outputs from the systems to crisp values. This process is necessary because all fuzzy sets inferred by fuzzy inference in the fuzzy rules must be aggregated to produce one single number as the output of the fuzzy model.There are numerous techniques for defuzzifying a fuzzy set; some of the more popular techniques are included in fuzzy logic system. In the present chapter some recent defuzzification methods used in the literature are discussed with examples.

scholarly journals Establishing a New Link between Fuzzy Logic, Neuroscience, and Quantum Mechanics through Bayesian Probability: Perspectives in Artificial Intelligence and Unconventional Computing

Molecules ◽  
2021 ◽  
Vol 26 (19) ◽  
pp. 5987
Author(s):  
Pier Luigi Gentili
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Quantum Mechanics ◽  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Human Interaction ◽  
Cognitive Domain ◽  
Bayesian Probability ◽  
Total Probability ◽  
Unconventional Computing

Human interaction with the world is dominated by uncertainty. Probability theory is a valuable tool to face such uncertainty. According to the Bayesian definition, probabilities are personal beliefs. Experimental evidence supports the notion that human behavior is highly consistent with Bayesian probabilistic inference in both the sensory and motor and cognitive domain. All the higher-level psychophysical functions of our brain are believed to take the activities of interconnected and distributed networks of neurons in the neocortex as their physiological substrate. Neurons in the neocortex are organized in cortical columns that behave as fuzzy sets. Fuzzy sets theory has embraced uncertainty modeling when membership functions have been reinterpreted as possibility distributions. The terms of Bayes’ formula are conceivable as fuzzy sets and Bayes’ inference becomes a fuzzy inference. According to the QBism, quantum probabilities are also Bayesian. They are logical constructs rather than physical realities. It derives that the Born rule is nothing but a kind of Quantum Law of Total Probability. Wavefunctions and measurement operators are viewed epistemically. Both of them are similar to fuzzy sets. The new link that is established between fuzzy logic, neuroscience, and quantum mechanics through Bayesian probability could spark new ideas for the development of artificial intelligence and unconventional computing.

scholarly journals Fuzzy Model for Risk Assessment of Machinery Failures

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 525 ◽  
Author(s):  
Dejan V. Petrović ◽  
Miloš Tanasijević ◽  
Saša Stojadinović ◽  
Jelena Ivaz ◽  
Pavle Stojković
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Mining Industry ◽  
Fuzzy Model ◽  
Assessment Model ◽  
Risk Level ◽  
Engineering System ◽  
Risk Assessment Model

The main goal of this research was the development of an algorithm for the implementation of negative risk parameters in a synthesis model for a risk level assessment for a specific machine used in the mining industry. Fuzzy sets and fuzzy logic theory, in combination with statistical methods, were applied to analyze the time picture state of the observed machine. Fuzzy logic is presented through fuzzy proposition and a fuzzy composition module. Using these tools, the symmetric position of the fuzzy sets with regard to class was used, and the symmetric fuzzy inference approach was used in an outcome calculation. The main benefit of the proposed model is being able to use numerical and linguistic data in a risk assessment model. The proposed risk assessment model, using fuzzy logic conclusions and min–max composition, was used on a mobile crushing machine. The results indicated that the risk level of the mobile crushing machine was in the “high” category, which means that it is necessary to introduce maintenance policies based on this high risk. The proposed risk assessment model is useful for any engineering system.

scholarly journals Fuzzification Based Osteoporosis Prediction Model

2021 ◽  
Vol 10 (6) ◽  
pp. 28-31
Author(s):  
Shilpa Kumar ◽  
Shubangi D C
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Dual Energy ◽  
Literature Survey ◽  
Inference System ◽  
X Ray ◽  
The World

Osteoporosis is a disease in which bones become fragile and more likely to break. Osteoporosis can progress painlessly until it causes a bone fracture or a bone break. Dual Energy X-ray Absorptiometry (DEXA) is more costly and not accessible easily so we are using Fuzzy Inference system to predict osteoporosis. In this fuzzy logic, we collect risk factors and rules for osteoporosis and build a interface which take inputs and predicts if a person has osteoporosis. In the following Literature survey, we will take risk factors, rules, and ways to implement them. Around the world, 33% of women and 20% men over the age of 50 will suffer a fracture caused by Osteoporosis. Osteoporosis is a disease in which Bones become shallow and are fractured. If predicted before, quality of life will increase and severe surgery may be avoided.

scholarly journals The elimination of the consequences of transport emergencies

2021 ◽  
Vol 34 (06) ◽  
pp. 1677-1688
Author(s):  
Valeriy Borisovich Vilkov ◽  
Andrey Klimentevich Chernykh ◽  
Igor Gennadevich Malygin ◽  
Yuriy Dmitrievich Motorygin ◽  
Alexandr Vladimirovich Skripka
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Multicriteria Optimization ◽  
Optimization Problem ◽  
Fuzzy Inference ◽  
Inference Algorithm ◽  
Emergency Situations ◽  
Input Variables ◽  
Civil Defence ◽  
Multicriteria Optimization Problem

The problem of multicriteria optimization in relation to the decisions made about organizing the material and technical support for equipment and personnel of the Ministry of the Russian Federation for Civil Defence, Emergencies and Elimination of Consequences of Natural Disasters (EMERCOM of Russia) in the context of emergency response on transport has been explored in this article. The existing approaches have been indicated, and another approach to building a single generalized criterion by the given partial criteria for the multicriteria optimization problem has been proposed. The verbal statement of the considered problem of multicriteria optimization has been provided. The goal of the study is to develop a method for solving this multicriteria optimization problem using fuzzy sets, fuzzy logic, and the Mamdani's fuzzy inference algorithm. A substantial example has been provided, illustrating the application of the stated theoretical provisions for solving the problem of choosing the best option for the equipment and personnel of the EMERCOM of Russia to liquidate the consequences of emergency situations on transport. In terms of novelty, it must be noted that the indicator (output variable) and parameters (input variables) of the problem have been defined ambiguously, fuzzily, which allows to use the efficient mathematical tools of the theory of fuzzy sets, fuzzy logic, and the Mamdani's fuzzy inference algorithm to solve this problem.

scholarly journals Bankruptcy Prediction Model with Risk Factors using Fuzzy Logic Approach

2021 ◽  
Vol 6 (2) ◽  
pp. 102-110
Author(s):  
Teoh Yeong Kin ◽  
Akmal Haziq Ahmad Aizam ◽  
Suzanawati Abu Hasan ◽  
Anas Fathul Ariffin ◽  
Norpah Mahat
Keyword(s):  
Risk Factors ◽  
Fuzzy Logic ◽  
Prediction Model ◽  
Fuzzy Inference ◽  
Bankruptcy Prediction ◽  
Financial Flexibility ◽  
Inference System ◽  
Financial Firms ◽  
Fuzzy Logic Approach ◽  
Logic Approach

Forecasting bankruptcy remains crucial, especially during this pandemic. Managers, financial institutions, and government agencies rely on the information regarding an impending bankruptcy threat to make decisions. This paper developed a straightforward bankruptcy prediction model using the fuzzy logic approach for individuals and companies to evaluate their performance and analyse the tendency of getting bankrupt. A sample of 250 respondents from banks and financial firms were tested using the qualitative risk factors, namely, industrial risk, management risk, financial flexibility, credibility, competitiveness, and operational risk. This study provides a comprehensive analysis using the Fuzzy Inference System (FIS) editor in the MATLAB software, where the model's accuracy is compared to the actual results. The results show an accuracy rate of 99.20%, indicating that this approach can determine the likelihood of bankruptcy. The fuzzy logic approach can improve prediction accuracy while also guiding decision-makers in detecting and preventing possible financial crises in their early phases.

scholarly journals Optimization of National Rice Production with Fuzzy Logic using Mamdani Method

2021 ◽  
Vol 1 (1) ◽  
pp. 36-43
Author(s):  
Wawan Wawan ◽  
Mai Zuniati ◽  
Agus Setiawan
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Raw Materials ◽  
Rice Production ◽  
Production Costs ◽  
Price Stability ◽  
Distribution Costs ◽  
Materials Used ◽  
Inference Methods

The purpose of this article is to optimization of national rice production with fuzzy logic using Mamdani method. Based on the results of the study, it is known that four parameters need to be considered to maintain the price stability of necessities, namely production; availability; demand and distribution. Optimization of production by producers and optimization of the ordering of goods by distributors are important steps to maintain price stability for necessities. Optimization of production and ordering of staple goods will have a significant impact on the financial sector because it is closely related to the prediction of the number of raw materials used, production costs, storage costs, and also distribution costs of goods. One of the fuzzy inference methods that can be used for this optimization is the Mamdani method. To get the output on the application of the fuzzy logic of the Mamdani method, four stages are needed, formation of fuzzy sets; application of implication functions; composition of rules and defuzzification. Fuzzy logic Mamdani method can be used to predict the amount of national rice that must be produced. If it is known that the need is 21,908,784 tons of rice and the supply is 65,457,456 tons,  the amount of national rice that must be produced is 14,624,592 tons.

scholarly journals The idea of fuzzy logic usage in a sheet-based FMEA analysis of mechanical systems

2018 ◽  
Vol 183 ◽  
pp. 03009 ◽  
Author(s):  
Grzegorz Filo ◽  
Joanna Fabiś-Domagała ◽  
Mariusz Domagała ◽  
Edward Lisowski ◽  
Hassan Momeni
Keyword(s):  
Fuzzy Logic ◽  
Risk Analysis ◽  
Fuzzy Sets ◽  
Failure Mode ◽  
Fuzzy Inference ◽  
Logic Model ◽  
Analysis Method ◽  
Inference Process ◽  
Fmea Analysis ◽  
Logic Inference

The main purpose of the work which was carried out and is presented in this paper was to examine the possibility of using fuzzy logic inference for conducting a risk analysis with the help of a sheet-based Failure Mode and Effects Analysis method (FMEA). At the beginning, the main features of the analysed method were presented, with particular emphasis put on the Risk, Priority and Number parameters. Then, a proposal has been made which suggests using Matlab Fuzzy Logic Toolbox package in order to convert the factors into the form of fuzzy sets and to define rules for fuzzy inference process has been made. Finally, the created fuzzy logic model was used to present an example analysis of a turbocharger failure in the fuzzified form.

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