Safety Risk Assessment of Highway Special Maintenance Project Based on BP Neural Network

2011 ◽  
Vol 368-373 ◽  
pp. 3175-3179
Author(s):  
Wei Tian ◽  
Hui Min Li ◽  
Rui Qi Yan ◽  
Yun Xiang Hu
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Bp Neural Network ◽  
Safety Management ◽  
Construction Safety ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Safety Risk ◽  
Assessment Index ◽  
Assessment Index System

16 items of assessment indexes are selected to build up the safety risk assessment index system of special maintenance project according to the construction safety characteristics of highway special maintenance project, based on which, the safety risk assessment model of special maintenance project based on BP neural network is brought forward. The assessment model has been trained, checked and analyzed through example and it turns out that the effective safety risk assessment of highway special maintenance project can be calculated based on this model, which also supplies decision-making basis for the project safety management.

Application of the Evidence Theory in the Safety Evaluation of Tower Cranes

2012 ◽  
Vol 446-449 ◽  
pp. 2162-2167
Author(s):  
Su Ping Huang
Keyword(s):  
Risk Assessment ◽  
Index System ◽  
Safety Management ◽  
Evidence Theory ◽  
Safety Evaluation ◽  
Safety Risk ◽  
Assessment Index ◽  
Assessment Index System ◽  
Improved Algorithm

The safety management of tower cranes is a systematic engineering. Focused on the complexity and uncertainty of the safety evaluation of tower cranes, D-S evidence theory is applied to evaluate the the safety conditions of tower cranes in service, the safety risk assessment index system of tower cranes is built and the specific and improved algorithm of the evidence theory is given. through example calculation, this method is proved is feasible, effective and applicable in the safety evaluation of tower cranes.

A SOMNN Model for Transport Risk Assessment of Hazardous Materials by Rail

2012 ◽  
Vol 629 ◽  
pp. 778-783
Author(s):  
Ke Pan ◽  
Shouan Guan
Keyword(s):  
Risk Assessment ◽  
System Analysis ◽  
Safety Management ◽  
Hazardous Materials ◽  
Economic Loss ◽  
Assessment Model ◽  
Assessment Index ◽  
System A ◽  
China National Petroleum Corporation ◽  
Assessment Index System

Railway siding for transport of hazardous materials is an important way in transporting of hazardous materials in China and they often result in catastrophic consequences for environment and society with a great deal of economic loss. Risk assessment for railway siding is an effective way to ensure its operational safety. This paper focuses on the application of self-organizing neural network (SOMNN) to assess the risk of the railway siding operational system and classify its risk factors. In this work, the system analysis method based on the characteristics of railway siding for hazardous materials is first used to establish the transport risk assessment index system. A comprehensive risk assessment model of railway siding has been developed with the SOMNN theory to improve present methods available for risk assessment of rail siding’s safety. A field case study about 15 railway slides for transporting of oil in Jilin broach center of China National Petroleum Corporation is undertaken so that the effectiveness of the proposed approach could be verified. The result is in line with the actual situation and indicates that this method used is feasible and rational. This model provides a new method for transport risk assessment of hazardous materials by rail. The method is also proved more efficient for both risk assessment and safety management. The work specified in this paper can be as reference to the assessment work in China.

Research on Risk Assessment of Farmers’ Microcredit Based on BP Neural Network

2011 ◽  
Vol 121-126 ◽  
pp. 1068-1072
Author(s):  
Wei Ji ◽  
Xue Fang Zhang
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Bp Neural Network ◽  
Index System ◽  
Practical Application ◽  
Assessment Index ◽  
Rural Credit ◽  
Assessment Index System ◽  
Essential Support

Our government always pushes hard forward the farmers’ microcredit of the rural credit cooperation. But actually, its risk is higher than other kinds of loan. Based on analyzing the risk of farmers’ microcredit, this paper builds risk assessment index system of farmers’ microcredit by using BP neural network. Through the paper, it can provide essential support and practical application for relative departments of farmers’ microcredit.

scholarly journals A credit risk assessment model of borrowers in P2P lending based on BP neural network

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255216
Author(s):  
Zhengwei Ma ◽  
Wenjia Hou ◽  
Dan Zhang
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Credit Risk ◽  
Bp Neural Network ◽  
Assessment Model ◽  
P2p Network ◽  
Risk Assessment Model ◽  
Credit Risk Assessment ◽  
P2p Lending ◽  
Lm Algorithm

Peer-to-Peer (P2P) lending provides convenient and efficient financing channels for small and medium-sized enterprises and individuals, and therefore it has developed rapidly since entering the market. However, due to the imperfection of the credit system and the influence of cyberspace restrictions, P2P network lending faces frequent borrower credit risk crises during the transaction process, with a high proportion of borrowers default. This paper first analyzes the basic development of China’s P2P online lending and the credit risks of borrowers in the industry. Then according to the characteristics of P2P network lending and previous studies, a credit risk assessment indicators system for borrowers in P2P lending is formulated with 29 indicators. Finally, on the basis of the credit risk assessment indicators system constructed in this paper, BP neural network is built based on the BP algorithm, which is trained by the LM algorithm (Levenberg-Marquardt), Scaled Conjugate Gradient, and Bayesian Regularization respectively, to complete the credit risk assessment model. By comparing the results of three mentioned training methodologies, the BP neural network trained by the LM algorithm is finally adopted to construct the credit risk assessment model of borrowers in P2P lending, in which the input layer node is 9, the hidden layer node is 11 and output layer node is 1. The model can provide practical guidance for China and other countries’ P2P lending platforms, and therefore to establish and improve an accurate and effective borrower credit risk management system.

scholarly journals A Study on RB-XGBoost Algorithm-Based e-Commerce Credit Risk Assessment Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Weimin Yang ◽  
Lili Gao
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Index System ◽  
Operating Characteristic ◽  
Evaluation Index System ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Assessment Index ◽  
Credit Risk Assessment ◽  
Assessment Index System

The current method’s e-commerce credit risk assessment is prone to poor data balance and low evaluation accuracy. An RB-XGBoost algorithm-based e-commerce credit risk assessment model is proposed in this study. The adaptive random balance (RB) method is used to sample and process the obtained data to improve the balance degree of the data. An assessment index system is constructed based on the processed data. Based on the risk evaluation index system and the XGBoost algorithm, this paper constructed an e-commerce risk assessment model and assessed the e-commerce credit risk using this model. The experimental results show that the proposed method has good data balance, a high kappa coefficient, and a large receiver operating characteristic (ROC) curve area, which can effectively improve e-commerce credit risk assessment accuracy.

scholarly journals Construction Safety Risk Assessment for Existing Building Renovation Project Based on Entropy-Unascertained Measure Theory

2020 ◽  
Vol 10 (8) ◽  
pp. 2893
Author(s):  
Wenlong Li ◽  
Qin Li ◽  
Yijun Liu ◽  
Huimin Li ◽  
Xingwang Pei
Keyword(s):  
Risk Assessment ◽  
Measure Theory ◽  
Construction Safety ◽  
Risk Identification ◽  
Assessment Model ◽  
Entropy Weight ◽  
Safety Risk ◽  
Existing Building ◽  
Assessment Index ◽  
Entropy Weight Method

With the development of society, there are more and more existing building renovation projects. According to the common construction safety problems, and based on the characteristics of the construction process of renovation project, this paper established a construction safety risk assessment model of renovation project based on entropy-unascertained measure theory. Firstly, the assessment index system was determined by risk identification and analysis. Secondly, the unascertained measure theory was applied to the construction safety risk assessment of renovation project, and the weight of each index was determined by the entropy weight method. Finally, taking the actual renovation projects as examples to calculate its safety risk grade, it is found that the assessment results of the model are basically consistent with the actual situation of the site by comparison. The research shows that the model can provide a new idea to quantitatively assess the construction safety risk of renovation project and provide a reliable basis for the management and control of the construction safety of existing building renovation project.

scholarly journals Intelligent Recognition of Safety Risk in Metro Engineering Construction Based on BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Mengchu Li ◽  
Jingchun Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Safety Management ◽  
Rapid Development ◽  
Construction Safety ◽  
Urban Rail Transit ◽  
Safety Risk ◽  
Subway Construction ◽  
Under Construction ◽  
Assessment Problems

With the rapid development of urban economy, the development of urban rail transit is becoming more and more rapid. As an energy-saving, land-saving, and environment-friendly green travel mode, the subway provides realistic and feasible solutions to the increasingly prominent traffic environment and other urban diseases in our country and brings a booming development in the subway construction industry with efforts to promote and build in many large cities. For a large number of subway constructions, it is particularly important to judge the construction safety status in time during the entire safety management process. Regularly conducting safety risk assessments on subway construction status can accurately predict and judge the types of accidents that occur. In order to solve the current safety risk assessment problems in the process of subway construction in our country, this paper is based on the BP neural network to intelligently identify the safety risks of subway construction, choosing from three aspects: human factors, management factors, and risk factors. We evaluate the construction safety of subway projects under construction through the model, predict the types of accidents that may occur, so that the construction unit can take corresponding preventive and improvement measures, improve the relevant safety technology of subway construction in a targeted manner, and propose corresponding reductions. We provide suggestions and measures for risk probability, to ensure that the construction unit discovers the danger in time and takes safety measures. The rectification measures provided theoretical basis and guidance.

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