Activity vulnerability index for delay risk forecasting

2006 ◽  
Vol 33 (10) ◽  
pp. 1261-1270 ◽  
Author(s):  
Jungwuk Kim ◽  
Sangyoub Lee ◽  
Taehoon Hong ◽  
Seungwoo Han
Keyword(s):  
Risk Assessment ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Vulnerability Index ◽  
Construction Operations ◽  
Subjective Knowledge ◽  
Risk Forecasting ◽  
The Relationship ◽  
Activity Criticality

The vulnerability of construction activities to delay factors controls the magnitude of the delay impact. Vulnerability characteristics of construction activities result in different patterns of duration variation. To identify the relationship between the delay factors and an activity's vulnerability, an activity vulnerability index was developed based on the subjective knowledge of experienced experts in construction operations. Incorporating the fuzzy set theory, the index provides not only information about the quantified vulnerability characteristics of an activity but also a reference for an activity criticality assessment. This study intends to suggest a framework for delay risk assessment and forecasting.Key words: construction delay, vulnerability index, fuzzy set theory.

Uncertainty Analysis in Quantitative Risk Assessment

1996 ◽  
Vol 118 (1) ◽  
pp. 121-124 ◽  
Author(s):  
S. Quin ◽  
G. E. O. Widera
Keyword(s):  
Risk Assessment ◽  
Uncertainty Analysis ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Failure Modes ◽  
The Other ◽  
Quantitative Risk Assessment ◽  
Engineering Practice ◽  
Criticality Analysis

Of the quantitative approaches applied to inservice inspection, failure modes, effects,criticality analysis (FMECA) methodology is recommended. FMECA can provide a straightforward illustration of how risk can be used to prioritize components for inspection (ASME, 1991). But, at present, it has two limitations. One is that it cannot be used in the situation where components have multiple failure modes. The other is that it cannot be used in the situation where the uncertainties in the data of components have nonuniform distributions. In engineering practice, these two situations exist in many cases. In this paper, two methods based on fuzzy set theory are presented to treat these problems. The methods proposed here can be considered as a supplement to FMECA, thus extending its range of applicability.

Risk Assessment of Enterprise Merger and Acquisition Based on Event Tree Method and Fuzzy Set Theory

2013 ◽  
Vol 13 (21) ◽  
pp. 4819-4825 ◽  
Author(s):  
Guang-Rong Li ◽  
Chun-He Li ◽  
Xiu-Hong Niu ◽  
Li-Ping Yang
Keyword(s):  
Risk Assessment ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Merger And Acquisition ◽  
Event Tree ◽  
Tree Method

A Computerized Method for Delay Risk Assessment Based on Fuzzy Set Theory using MS Project™

2018 ◽  
Vol 22 (8) ◽  
pp. 2714-2725 ◽  
Author(s):  
Cenk Budayan ◽  
Irem Dikmen ◽  
M. Talat Birgonul ◽  
Aydın Ghaziani
Keyword(s):  
Risk Assessment ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Computerized Method

scholarly journals Analysis of the acoustic climate of a spa park using the fuzzy set theory

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Małgorzata Sztubecka ◽  
Jacek Sztubecki
Keyword(s):  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Urban Areas ◽  
Environmental Pollutants ◽  
Sound Level ◽  
The Road ◽  
The People ◽  
The Individual ◽  
The Relationship

Abstract The paper describes the differences between the actual results of the measurement of equivalent sound level and the feelings of people visiting "a Spa Park". Noise, as one of the environmental pollutants, cause detrimental effects on the recipient. Measurements of noise are usually performed in urban areas, especially in the road environments, providing a basis for measures to limit their impact on the environment. Often in the measurement there are ignored areas for recreation. Usually, they do not determine the relationship between the results of measurements of noise equivalent sound level and the individual feelings of the people living in these areas. The analysis was performed with the use of fuzzy set theory. The evaluation of the acoustic climate on the "Spa Park" should be determined on the basis of sound level measurements and questionnaires.

Incorporating Fuzzy Logic in Data Mining Tasks

2011 ◽  
pp. 884-891
Author(s):  
Lior Rokach
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Major Change ◽  
Human Cognition ◽  
Unstable State ◽  
Subjective Knowledge ◽  
Small Change

In this chapter we discuss how fuzzy logic extends the envelop of the main data mining tasks: clustering, classification, regression and association rules. We begin by presenting a formulation of the data mining using fuzzy logic attributes. Then, for each task, we provide a survey of the main algorithms and a detailed description (i.e. pseudo-code) of the most popular algorithms. There are two main types of uncertainty in supervised learning: statistical and cognitive. Statistical uncertainty deals with the random behavior of nature and all existing data mining techniques can handle the uncertainty that arises (or is assumed to arise) in the natural world from statistical variations or randomness. Cognitive uncertainty, on the other hand, deals with human cognition. Fuzzy set theory, first introduced by Zadeh in 1965, deals with cognitive uncertainty and seeks to overcome many of the problems found in classical set theory. For example, a major problem faced by researchers of control theory is that a small change in input results in a major change in output. This throws the whole control system into an unstable state. In addition there was also the problem that the representation of subjective knowledge was artificial and inaccurate. Fuzzy set theory is an attempt to confront these difficulties and in this chapter we show how it can be used in data mining tasks.

Sign in / Sign up

Export Citation Format

Share Document