Risk assessment of cost overrun using fuzzy logic model

2022 ◽  
Author(s):  
G.B.S. Alekhya ◽  
K. Shashikanth ◽  
M. Anjaneya Prasad
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Logic Model ◽  
Cost Overrun ◽  
Fuzzy Logic Model

scholarly journals A FUZZY LOGIC APPROACH FOR DRONE CAPABILITY ANALYSIS ON DISASTER RISK ASSESSMENT

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 485-489
Author(s):  
P. Zlateva ◽  
S. Hristozov ◽  
D. Velev
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Disaster Risk ◽  
External Input ◽  
Logic Model ◽  
Disaster Risk Assessment ◽  
Fuzzy Logic Model ◽  
Capability Analysis ◽  
Fuzzy Logic Approach ◽  
Logic Approach

<p><strong>Abstract.</strong> The paper proposes a fuzzy logic approach for drone capability analysis on disaster risk assessment. In particular, a fuzzy logic model is designed as a hierarchical system with several inputs and one output. The system inputs corresponds to the linguistic variables, describing the of levels of the external and internal input factors, which determine the capability levels of analysed drone in respect to disaster risk assessment. As external input factors are used, for example: disaster type (flood, landslide, wildfire); weather conditions (wind speed, fog, cloud cover); operational area (urban, mountain, plain), etc. As internal input factors are considered the drone characteristics such as drone type, flight performance (stall speed, turn radius, flight endurance), payload capabilities (camera resolution, accuracy, weight, sensors), etc. The fuzzy logic system output gives the level of the drone capability on disaster risk assessment in defined conditions. The model is designed in <i>Matlab</i> computer environment using Fuzzy Logic Toolbox. Several computer simulations are carried out to validate the proposed model. The designed fuzzy logic model is part of an information system for disaster risk management using drones, which is under development.</p>

scholarly journals Identification of Safe Assembly Points in Emergencies in a Gas Refinery of the South Pars Gas Complex Using Fuzzy Logic Model

2019 ◽  
pp. 275-286
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Crisis Management ◽  
Oil And Gas ◽  
Treatment Plant ◽  
Logic Model ◽  
Fuzzy Logic Model ◽  
Accuracy And Precision ◽  
Focus Group Meeting ◽  
Gis Environment

INTRODUCTION: Crisis management is of critical importance in the oil and gas industries due to the increasing occurrence of accidents in these areas. One of the most important issues regarding crisis management in such industries is the identification of safety assembly points where employees should gather in emergencies. This study aimed to identify the safe points in a refinery using geographic information system (GIS) and fuzzy logic for emergency assembly. METHODS: Regarding the aim of the study purpose, the required data were collected, and a focus group meeting was held with experts to determine the criteria influencing the safety point zoning as well as high-risk units using the HAZOP method. After the identification of the criteria and sub-criteria affecting the zoning, the weight of each zoning parameter was calculated, and the safety zones were determined using the fuzzy logic model and its operators in the GIS environment. FINDINGS: According to the results of the risk assessment, the criteria and sub-criteria affecting zoning were divided into three categories of inconsistent (layer weight: 0.740), consistent (layer weight: 0.094), and access to exit routes (layer weight: 0.167). Moreover, the map results based on the fuzzy logic model revealed three safe points, including the vicinity of the fire station, clinic, and wastewater treatment plant in this refinery where the employees should gather in the event of emergencies. CONCLUSION: The results of this study showed that the selection of appropriate criteria in safe point zoning is of great importance in the emergencies in the industries. Moreover, an initial risk assessment can be effective in determining these criteria and sub-criteria. In addition, the fuzzy logic model has high accuracy and precision in determining the appropriate safe places.

A Fuzzy Logic Model to Predict Coral Reef Development under Nutrient and Sediment Stress

1998 ◽  
Vol 12 (5) ◽  
pp. 957-965 ◽  
Author(s):  
Erik H. Meesters ◽  
Rolf P. M. Bak ◽  
Susie Westmacott ◽  
Mark Ridgley ◽  
Steve Dollar
Keyword(s):  
Fuzzy Logic ◽  
Coral Reef ◽  
Logic Model ◽  
Reef Development ◽  
Fuzzy Logic Model

scholarly journals A Fuzzy Logic Model for Hourly Electrical Power Demand Modeling

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 448
Author(s):  
Marco Antonio Islas ◽  
José de Jesús Rubio ◽  
Samantha Muñiz ◽  
Genaro Ochoa ◽  
Jaime Pacheco ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
New England ◽  
Electrical Power ◽  
Logic Model ◽  
Power Demand ◽  
Demand Modeling ◽  
Mapping Model ◽  
Fuzzy Logic Model ◽  
Radial Basis

In this article, a fuzzy logic model is proposed for more precise hourly electrical power demand modeling in New England. The issue that exists when considering hourly electrical power demand modeling is that these types of plants have a large amount of data. In order to obtain a more precise model of plants with a large amount of data, the main characteristics of the proposed fuzzy logic model are as follows: (1) it is in accordance with the conditions under which a fuzzy logic model and a radial basis mapping model are equivalent to obtain a new scheme, (2) it uses a combination of the descending gradient and the mini-lots approach to avoid applying the descending gradient to all data.

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