Development of project cost contingency estimation model using risk analysis and fuzzy expert system

A fuzzy logic expert system to estimate intrinsic extinction vulnerabilities of marine fishes to fishing

In this paper, a fuzzy expert system is described for decision-making in geographical information systems (GIS), because fuzzy logic and expert system technology are available methods for approximating human reasoning and enhancing the level of intelligence in GIS. This system computes a potentiality map for problem solution with a problem specification provided by the user and expert knowledge (by means of production rules). It is a fuzzy expert system using a generalised modus ponens based on the fuzzy implication of Brouwer-Godel which is implemented by a fast algorithm. Several kind of rules are accepted and especially gradual rules. We have experimented our system on "Palni Hills", a mountainous region of meridional India to locate areas where the profitability of agriculture could be generally the best. The system is explicitly designed to provide the user with a decision-making environment for the cartographic problem solution to be carried out in a flexible manner.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This article describes the development of an intelligent system for railway risk analysis using fuzzy reasoning approach (FRA) and fuzzy analytical hierarchy decision making process (Fuzzy-AHP), which is specially designed and developed for the railways. In the system, FRA is employed to estimate the risk level (RL) of each failure event in terms of failure probability (FP) and consequent severity (CS). This allows imprecision or approximate information in risk analysis process. Fuzzy-AHP technique is incorporated into the risk model to use its advantage in determining the relative importance of the risk contributors, i.e. weight factor (WF) so that the risk assessment can be progressed from component level to the subsystem level and finally to system level. This risk assessment system can evaluate both qualitative and quantitative risk data and information associated with a railway system efficiently and effectively, which will provide railway risk analysts, managers and engineers with a method and tool to improve their safety management of railway systems and set safety standards. A case study on rolling stock asset risk analysis is used to illustrate the application of the intelligent system. (6 pages)

In Flanders and by extension in the whole world, accidents involving the transport of dangerous goods occur. Therefore, Flanders is developing a risk analysis and assessment system for the external human risks of transporting dangerous goods. The general outline of the system has now been established, based on a number of external research projects. The system allows to obtain a profile of the risks of serious accidents with the transportation of dangerous goods on roads, railways, waterways, pipelines and in ports. In brief, the risk analysis system consists of the following major elements: dividing of the transport routes into segments, determining the probability of a major accident per segment, determining the damage per segment, determining the risks for each segment and determining the risks per route. The next step was the developing of a risk assessment system to be able to decide whether or not the risk of the transport of dangerous goods is acceptable, whether or not extra safety measures have to be taken and which of the alternative routes is the most favourable one for external safety. The system consists of two major steps. First, the risk per segment is studied and if necessary, the ALARP principle is applied. Second, the risk of the total route is calculated for all alternative routes and is compared.

With the increasing need for flexibility and adaptivity in computerized systems, the application of fuzzy expert systems is becoming increasingly commonplace in today's industry. Fuzzy logic expert systems often improve performance by allowing knowledge to generalize without requiring the knowledge engineer to anticipate all possible situations. Thus, for many types of applications, soft such as Fuzzy logic can incur lower overhead in terms of representing and engineering task knowledge. My project investigates the application of fuzzy expert systems to motion tracking. Previous research showed that Fuzzy Logic can be used to track the motion of a brightly colored object against a dark background, with relatively low development and run-time costs. The system I am developing identifies, tracks, and predicts the motion of multiple objects using unique identification patterns against a dark background. My poster will describe the fuzzy inference systems for tracking and motion prediction of such objects. An essential step to obtaining the fuzzy inputs for the motion tracking fuzzy inference system is to use convolution correlation data to obtain the centers of mass of the objects. Image processing information from the region around the center of each object provides good fuzzy inputs for recognizing object patterns and determining orientation. I am investigating two fuzzy inference systems for motion tracking and prediction in order to identify their strengths and weaknesses. In the long run, I hope to exploit the strengths of both by dividing the task between the two approaches. The Adaptive Neuro Fuzzy Inference System (ANFIS) is effective for pattern recognition. Fuzzy Proportional Derivative (FPD) is useful for computing errors and tuning inferences. Cascading the two fuzzy systems allows the system first to acquire generalized patterns and then fine-tune them for error tolerance. To perform this work, I am using the Matlab fuzzy logic toolkit, which is a simple and flexible tool for designing fuzzy inference systems.

The fuzzy-associative metaheuristic approach addresses the urgent task of developing a marketing decision support system based on a fuzzy trained associative rules expert system, aimed at improving the accuracy and efficiency of consumer preference analysis. The proposed system combines the interpretability of fuzzy logic with data-driven learning via associative rules and parameter identification using an adaptive multi-agent optimization method. To achieve this goal, associative rule learning techniques (Apriori and FP-Growth) were used to extract frequent consumer behavior patterns. A fuzzy expert system was developed, in which the parameters of membership functions are optimized by the Adaptive Vibrating Particle System (AVPS) metaheuristic. Unlike traditional vibrating particle systems, AVPS integrates iteration-dependent control of particle positions, enabling global search in early iterations and local refinement at later stages, thus improving convergence speed and solution precision. The architecture was implemented using Python-based tools (TensorFlow, Keras, Pandas, mlxtend, Scikit-Fuzzy), and validated on the “Consumer Behavior and Shopping Habits” dataset. The fuzzy expert system achieved an accuracy of 0.98, outperforming human experts (0.80), traditional VPS optimization (0.93), and backpropagation-based training (0.90). The system also reduces reliance on manually tuned parameters and increases robustness to data incompleteness and noise. Scientific novelty lies in combining a fuzzy associative rule-learning framework with AVPS-based optimization, offering a scalable and interpretable decision-making mechanism. The developed system contributes to the advancement of intelligent recommendation engines, personalized marketing tools, and decision support systems in consumer-oriented analytics.

Abstract: The paper describes a model‐driven decision support system for risk analysis in product development, based on an European Commission‐funded project. The model can be used to simulate the life cycle of a large batch of products and track products in operation and service phases with consideration of design and manufacturing phases. This provides some useful benefits – in particular, being able to generate composite results when the product life is subject to unpredictable events, which makes the system's behaviour non‐linear. A case study is conducted through a domestic appliance in the field trial from a manufacturing company, and the results indicate that the model can enable developers or manufacturers to understand the behaviour and characteristics of products or systems in the future use and to predict the cost of any changes in the pattern of their life.

Objectives: Operational effectiveness is an ultimate objective of any manufacturing organization. On the shop floor the main focus is on elimination of waste and delays. The main target is designing a product for which manufacturing is a low cost process. Once organizations select appropriate tools and methods, the decision on performance measures can be taken through which progress can be monitored. The present work offers fuzzy expert system for manufacturing method selection in a dynamic environment where the organization’s objectives are subject to constant amendments. Methods/ Statistical Analysis: In order to incorporate ambiguity into the manufacturing environment, the authorshave designed and developed a fuzzy expert system using Sugeno model based on the dynamic ranges of triangular membership functions for input objectives which is extremely efficient as compared to the Mamdani method in classifying vague data. In this paper, the authors proposearchitecture for fuzzy expert system based on multi objective criteria for selection of manufacturing method employing Sugeno model with constant output. Fuzzy expert system is created outside MATLAB and MATLAB is used only for creating user interface for querying methods based on objectives and for the evaluation of rules. Findings: As more than one manufacturing method may serve a single objective, each manufacturing method is encoded using a binary digit and the output is decimal representation of this binary encoding. Also, it is observed that, the number of fuzzy rules increase exponentially as a function of number of objectives. In order to cater to this problem, instead of generating so many rules the required modifications are performed at the code level to incorporate multi objective criteria. Finally a simulink model is developed for selective methods and objectives. The results obtained using Sugeno Fuzzy Expert System is compared with that obtained using Mamdani method and crisp expert system. Results: It is found that the crisp expert system and Sugeno type FIS yield similar results for classification while Mamdani type FIS offers more flexibility in method selection due to the nature of output members functions which are overlapping fuzzy sets. This yields the manager a greater freedom in method selection based on infrastructure and other resource availability. Keywords: Expert System, Fuzzy Logic, Manufacturing, MATLAB, Membership Functions, Simulink.

The aim of this study is to design adaptive neural-fuzzy inference system (ANFIS) model and fuzzy expert system for determination of concrete mix designs and finally compare their results. Idea of these systems based on two surveys: first, ACI structures and principles, second a concrete mix designs dataset that collected via Prof. I-Cheng Yeh. Datasets that loaded in to ANFIS has 552 mix designs and based on ACI mix designs. Moreover, in this study, we have designed fuzzy expert system. Input fields of fuzzy expert system are Slump, Maximum Size of Aggregate (Dmax), Concrete Compressive Strength (CCS), and Fineness Modulus. Output fields are quantities of water, cement, fine aggregate (F.A.) and coarse aggregate (C.A.). In the ANFIS model, we have four layers (four ANFIS models): the first layer takes values of Dmax and Slump and then determines the quantity of Water, the second layer takes values of Water (computed in the past layer) and CCS then measures the value of Cement, the third layer takes values of Dmax and Slump to compute C.A. and the fourth layer takes values of Water, Cement, and C.A. (determined in past layers) and then measures the value of F.A. When these systems were designed and tested, comparison between two systems (FIS and ANFIS) results showed that results of ANFIS model are better than fuzzy expert system’s results. In the ANFIS model, for Water output field, training and average testing errors are 0.86 and 0.8. For cement field, training error and average testing error are in the orders of 0.21 and 0.22. Training and average testing error of C.A. are in the orders of 0.0001 and 0.0004 and finally, training and average testing errors of F.A. are in the orders of 0.0049 and 0.0063. Results of fuzzy expert system in comparison to ACI results follow average errors: average error of Water, Cement, C.A., and F.A. are in the orders of 9.5%, 27.6%, 96.5%, and 49%.

This paper presents a fuzzy expert system that forecasts the wind speed at a wind energy conversion system (WECS) site and the electrical power that will be generated. The user can define the forecast horizon, which can range from some minutes up to several hours ahead. After training, the system can make reliable wind speed forecasts in less than a second. The system implements wind speed and direction measuring stations that are installed around and in the WECS site. The stations send measurements via wireless modems to a central computer running the fuzzy expert system, which exploits any spatial correlation existing among the measuring stations' wind speed time series. For the training of the fuzzy expert system two genetic algorithm implementations were used and compared.

A framework for assessing sustainability in multi-tier supply chains using empirical evidence and fuzzy expert system

Due to the complexity of the earthquake systems, it is impossible to produce any acceptable earthquake risk map. This paper suggests a fuzzy approach for reforming earthquake map by the integration of fuzzy set theory, risk analysis and geographical information system(GIS) technology. The implications of the incorporation of fuzzy set methodologies into earthquake map for GIS application are twofold: first, the interior-outer-set model is introduced to calculate fuzzy probabilities of earthquakes for showing the imprecision of the probabilities from incomplete data; and second, graphical ways to express the outcomes of the calculation is provided to show how fuzzy set methodologies may be incorporated into GIS technologies. In addition, examples are given to demonstrate the method. The map using the fuzzy probabilities is called soft risk map. It is shown that soft risk maps are capable of providing more reliable information than traditional earthquake maps for rationally regulating decision-makings.

This article outlines the methodology of the IT system risk analysis and management, including various categories of risk factors significant from the point of view of the sensitive data processing and completeness of the procedure for determining the IT system risk level. The presented methodology is divided into the IT system risk analysis and the risk management method. The IT system risk level assessed by the risk analysis method described in this article constitutes an input value for the risk management method outlined in the further part hereof, referring to the IT systems used for the processing of documents at different levels of sensitivity.

Personalized Guide through Educational Exhibition: Fuzzy Expert System as Museum Exhibition “Virtual Guide” Lukas Najbrt, Jana Kapounova Abstract The development of information and communication technologies in recent decades has been very stormy. Modern mobile devices (smartphones, tablets) are ideal tools to use as virtual guides through educational exhibitions. They allow a personalized approach to the visitors (each visitor controls his/her own device), they can present not only text, but also sound and moving images and using wireless technology, they can communicate with a control server and can navigate through the exhibition. The paper describes the model of such a virtual guide using a fuzzy expert system. It's purpose is to replace the physical exhibition guide. Based on an initial analysis of a visitor, the virtual guide proposes a tour through the exhibition so that it brings the visitor the maximal educational benefit while at the same time offers information about the displayed exhibits in such a way that is most comprehensible. A fuzzy expert system is used for reasons of better and simpler communication with the visitor. Fuzzy logic allows handling of conceptual vagueness in the visitor's responses to the system's queries and also allows the use of fuzzy borders between different categories of visitors. The output is a tour route proposal adapted to practically any visitor. The aim of the virtual guide is to function in an environment of a real (physical) exhibition. First, however, to ensure better system diagnostics, the whole system should be tested in an environment of a virtual museum on the Internet. The testing involves not only the system's functionality, but also the educational benefits of such a guide. Full Text: PDF DOI: 10.15640/jehd.v4n1a15

This paper presents a Qualitative Risk Analysis Framework to identify and prioritize environmental risks encountered in infrastructure projects, which is applied to developing countries. The framework incorporates consensus and quality of experts in the process of evaluating environmental risk events and is composed of (1) Fuzzy Expert System (FES) to determine attributes of experts; (2) Fuzzy Similarity Aggregation Algorithms to aggregate experts’ opinions; and (3) three-dimensional prioritization approach to rank the risks, qualitatively. The FES determines an importance weight factor for each expert, based on a set of predetermined qualification attributes. Experts’ opinions are aggregated in a linguistic framework, based on the proximity of their opinions on the scale to ensure that their aggregated decision is a result of common agreement. The importance weight factor is combined with the consensus weight factor of each expert in the aggregation process using a scalar modifier and the Euclidean Distance Measure Function is used to determine the linguistic criticality of every environmental risk event. A three-dimensional prioritization approach applies a set of ranking rules to every risk that enables experts to rank and visualize the priority of the risks in a three-dimensional space. The framework contributes to the Construction industry by solving a major problem for project teams in developing countries to qualitatively evaluate environmental risks in a fully supported linguistic framework, using fuzzy logic, which addresses the vagueness and imprecision that exist in the decision-making process. It provides an