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.

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.

scholarly journals An Automatic Monitoring Method of Slope Deformation in Open-pit mine based on BP Neural Network and GIS Technology

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rapid Development ◽  
Automatic Monitoring ◽  
Deformation Monitoring ◽  
Open Pit Mine ◽  
Slope Deformation ◽  
Open Pit ◽  
Gis Technology ◽  
Monitoring Method

At present, the research on BP neural network has achieved good results in many industries and fields, but there are few projects in the application research of mineral resources mining. Under the social background of the rapid development of electronic information technology, BP neural network and GIS technology are combined to carry out research and application, which will provide a new research path for slope deformation monitoring and disaster prevention in mining area. Therefore, in the paper, the key technology of open-pit mine slope deformation automatic monitoring based on BP neural network and GIS technology was put forward. Firstly, the advantages of BP neural network were analyzed and BP neural network was selected as the prediction model of slope deformation. The artificial fish swarm algorithm was used to improve the BP neural network to improve the performance of the model. Based on the analysis and construction of GIS technology, the combination application of BP neural network and GIS technology was discussed. Through practice, the application effect of the technology was verified, and it has good theoretical and practical value

scholarly journals An Improved Prediction Model of IGBT Junction Temperature Based on Backpropagation Neural Network and Kalman Filter

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yu Dou
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Prediction Model ◽  
Bp Neural Network ◽  
High Speed ◽  
Rapid Development ◽  
Junction Temperature ◽  
Insulated Gate Bipolar Transistor ◽  
Network Estimation ◽  
The Stability

With the rapid development of emerging technologies such as electric vehicles and high-speed railways, the insulated gate bipolar transistor (IGBT) is becoming increasingly important as the core of the power electronic devices. Therefore, it is imperative to maintain the stability and reliability of IGBT under different circumstances. By predicting the junction temperature of IGBT, the operating condition and aging degree can be roughly evaluated. However, the current predicting approaches such as optical, physical, and electrical methods have various shortcomings. Hence, the backpropagation (BP) neural network can be applied to avoid the difficulties encountered by conventional approaches. In this article, an advanced prediction model is proposed to obtain accurate IGBT junction temperature. This method can be divided into three phases, BP neural network estimation, interpolation, and Kalman filter prediction. First, the validities of the BP neural network and Kalman filter are verified, respectively. Then, the performances of them are compared, and the superiority of the Kalman filter is proved. In the future, the application of neural networks or deep learning in power electronics will create more possibilities.

Risk Evaluation of the Wind Power Project Investment Based on BP Neural Network

2010 ◽  
Vol 108-111 ◽  
pp. 256-261 ◽  
Author(s):  
Wei Li ◽  
Shi Chao Li ◽  
Dan Wang
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Rapid Development ◽  
Evaluation Index System ◽  
Project Investment ◽  
High Profit ◽  
Risk Characteristics ◽  
Power Project

With the rapid development of the society, more and more countries have been increasingly optimistic about wind power projects because of its advantages, such as non-polluting, renewable, energy-saving and emission reduction. While facing the temptation of high profit, it is necessary to assess the risks of wind power project investment scientifically. Therefore, this article combines with the risk characteristics of wind power project under the current social environment to build a evaluation index system of wind power project to evaluate the risk of wind power project based on BP neural network.

Study on the Irrigation Measurement and Prediction of Greenhouse Flowers under Water Stress Acoustic Emission Based on Neural Network

2013 ◽  
Vol 347-350 ◽  
pp. 985-989
Author(s):  
Huan Zou ◽  
Xin Wang ◽  
Lin Lin Yang ◽  
Yan Xin Yang ◽  
Xue Ping Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Irrigation System ◽  
Rapid Development ◽  
Basic Principles ◽  
Processing Ability ◽  
Bp Neural Network Model ◽  
Self Learning ◽  
Irrigation Volume ◽  
Precision Irrigation

Recently, with the rapid development of Chinese flower market, the precision irrigation problem is generally concerned. This paper mainly introduces the basic principles of the automatic irrigation system, and realizes the automatic control of the irrigation volume in irrigation system by utilizing the self-learning characteristics of BP neural network model and the powerful data processing ability of the matlab software.

Agricultural Machinery Spare Parts Demand Forecast Based on BP Neural Network

2014 ◽  
Vol 635-637 ◽  
pp. 1822-1825 ◽  
Author(s):  
Yao Guang Hu ◽  
Shuo Sun ◽  
Jing Qian Wen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rapid Development ◽  
Spare Parts ◽  
Agricultural Machinery ◽  
Demand Forecast ◽  
The Third ◽  
Neural Network Prediction ◽  
Network Prediction

With the rapid development of agricultural machinery, forecasting the demand for spare parts is essential to ensure timely maintenance of agricultural machinery. Based on features of spare parts, BP neural network is chosen to forecast the demand. First, this paper analyzes factors that affect the demand for spare parts. Second, steps and processes of neural network prediction are described. The third part of this paper is case study based on certain brand of agricultural machinery spare parts. BP neural network turns out suitable for forecasting the demand for spare parts.

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