scholarly journals Major project risk assessment method based on BP neural network

2019 ◽  
Vol 12 (4-5) ◽  
pp. 1053-1064 ◽  
Author(s):  
Lidong Liu ◽  
◽  
Fajie Wei ◽  
Shenghan Zhou ◽  
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Bp Neural Network ◽  
Assessment Method ◽  
Project Risk ◽  
Risk Assessment Method ◽  
Major Project ◽  
Project Risk Assessment

A Research Review of Project Risk Assessment Methods

2014 ◽  
Vol 496-500 ◽  
pp. 2857-2862
Author(s):  
Zhi Cheng Chen ◽  
Lu Yao ◽  
Jian Jun Yang
Keyword(s):  
Risk Assessment ◽  
Assessment Method ◽  
Assessment Methods ◽  
Research Review ◽  
Simulation Methods ◽  
Project Risk ◽  
Subjective Judgment ◽  
Advantages And Disadvantages ◽  
Risk Assessment Method ◽  
Project Risk Assessment

Based on the domestic and overseas researches of project risk assessment methods, this paper classifies project risk assessment methods into subjective judgment methods, evidence and deduction methods, system simulation methods and other methods. After exploring their definitions, advantages and disadvantages, this paper points out that subjective judgment and empirical analogy become two mainstream risk assessment methods. In the end, this paper claims that a risk assessment method does not need any unfathomable theory for its obvious engineering characteristics, but focuses on operability and applicability.

scholarly journals A Novel Project Risk Assessment Method Development via AHP-TOPSIS Hybrid Algorithm

2020 ◽  
Vol 4 (5) ◽  
pp. 390-410
Author(s):  
Emin Başar Baylan
Keyword(s):  
Risk Assessment ◽  
Assessment Method ◽  
Activity Level ◽  
Topsis Method ◽  
Project Risk ◽  
Decision Method ◽  
Project Activity ◽  
Risk Assessment Method ◽  
Output Quality ◽  
Project Risks

In project planning, risk assessment method plays vital role. Poorly assessed project risks cause degeneration at project cost, project completion time, and project output quality and project scope. Each project activity risk influence these project success factors. Implementation performance of a project activity triggers or smooth of its successor’s activity risks. Because of this; employing robust and detailed risk assessment methods is important to reach those project goals. In project risk assessment literature, when it is investigated, it is noticed that risk assessment and evaluation methods are only developed at whole project level. Actually, they are not comprehensive enough to evaluate the project risks at activity level. Besides that traditional risk assessment methods such as risk matrix does not able analyze project risk quantitatively. With this motivation, main aim of this study is developing a multi-criteria based decision method which prioritizing project risks at activity level. AHP and TOPSIS method are combined to developed novel method. In this hybrid method, Constructing AHP model is to prioritize work packages with respect to relative importance of project time, project output quality and project cost. Broken down structure of these work packages are used as input for weighted criteria for TOPSIS method. In second layer of this decision method, TOPSIS model is used for prioritizing predetermined activity risks according weighted project work packages success criteria. In the application of this method, a case study approach is followed. In this sense, “Global Furniture Ltd.” which is established in Istanbul, Turkey is chosen as a case to apply newly developed model. Results showed that application of AHP-Stochastic TOPSIS Hybrid Algorithm provides a platform that project risks could be analyzed as quantitative and also at project activity level.

A risk assessment method based on RBF artificial neural network - cloud model for urban water hazard

2014 ◽  
Vol 27 (5) ◽  
pp. 2409-2416 ◽  
Author(s):  
Dengfeng Liu ◽  
Dong Wang ◽  
Jichun Wu ◽  
Yuankun Wang ◽  
Lachun Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Artificial Neural Network ◽  
Cloud Model ◽  
Assessment Method ◽  
Urban Water ◽  
Risk Assessment Method ◽  
Water Hazard ◽  
Artificial Neural
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