Construction Project Bidding Risk Assessment Model Based on Rough Set-TOPSIS

2010 ◽  
Author(s):  
Wen Guo-feng ◽  
Chen Li-wen
Keyword(s):  
Risk Assessment ◽  
Rough Set ◽  
Construction Project ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Model Based

Technical Risk Assessment of Large-Scale Construction Project Based on Triangle Whitening Weight Function

2014 ◽  
Vol 989-994 ◽  
pp. 5294-5299
Author(s):  
Wei Ma
Keyword(s):  
Risk Assessment ◽  
Weight Function ◽  
Large Scale ◽  
Construction Project ◽  
Scale Construction ◽  
Assessment Model ◽  
Grey Theory ◽  
Risk Assessment Model ◽  
Technical Risk

Technical risk assessment model of large-scale construction project has been established by using triangle whitening weight function of grey theory against the problems of technical risk assessment of large-scale construction project. In the end, through example verification, this model is approved to be feasible and have certain value of reference and utilization in similar problems.

scholarly journals Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network

2019 ◽  
Vol 11 (17) ◽  
pp. 4513 ◽  
Author(s):  
Xiaoqing Li ◽  
Qingquan Jiang ◽  
Maxwell K. Hsu ◽  
Qinglan Chen
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Assessment Model ◽  
Software Project ◽  
Risk Assessment Model ◽  
Backpropagation Neural Network ◽  
Model Based ◽  
Sample Data

Software supports continuous economic growth but has risks of uncertainty. In order to improve the risk-assessing accuracy of software project development, this paper proposes an assessment model based on the combination of backpropagation neural network (BPNN) and rough set theory (RST). First, a risk list with 35 risk factors were grouped into six risk categories via the brainstorming method and the original sample data set was constructed according to the initial risk list. Subsequently, an attribute reduction algorithm of the rough set was used to eliminate the redundancy attributes from the original sample dataset. The input factors of the software project risk assessment model could be reduced from thirty-five to twelve by the attribute reduction. Finally, the refined sample data subset was used to train the BPNN and the test sample data subset was used to verify the trained BPNN. The test results showed that the proposed joint model could achieve a better assessment than the model based only on the BPNN.

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