scholarly journals Intelligent Classification Method for Tunnel Lining Cracks Based on PFC-BP Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hao Ding ◽  
Xinghong Jiang ◽  
Ke Li ◽  
Hongyan Guo ◽  
Wenfeng Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Structural Parameters ◽  
Crack Depth ◽  
Tunnel Lining ◽  
Parameter Analysis ◽  
Training Data ◽  
Concrete Lining ◽  
Common Disease ◽  
Lining Structure

Tunnel lining crack is the most common disease and also the manifestation of other diseases, which widely exists in plain concrete lining structure. Proper evaluation and classification of engineering conditions directly relate to operation safety. Particle flow code (PFC) calculation software is applied in this study, and the simulation reliability is verified by using the laboratory axial compression test and 1 : 10 model experiment to calibrate the calculation parameters. Parameter analysis is carried out focusing on the load parameters, structural parameters, dimension, and direction which affect the crack diseases. Based on that, an evaluation index system represented by tunnel buried depth (H), crack position (P), crack length (L), crack width (W), crack depth (D), and crack direction (A) is put forward. The training data of the back propagation (BP) neural network which takes load-bearing safety and crack stability as the evaluation criteria are obtained. An expert system is introduced into the BP neural network for correction of prediction results, realizing classified dynamic optimization of complex engineering conditions. The results of this study can be used to judge the safety state of cracked lining structure and provide guidance to the prevention and control of crack diseases, which is significant to ensure the safety of tunnel operation.

Crack-Depth Determination by a Neural Network with a Synthetic Training Data Set

1993 ◽  
pp. 803-810 ◽  
Author(s):  
M. Takadoya ◽  
M. Notake ◽  
M. Kitahara ◽  
J. D. Achenbach ◽  
Q. C. Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Crack Depth ◽  
Training Data ◽  
Data Set ◽  
Depth Determination ◽  
Synthetic Training Data

Longitudinal vibration analysis and suppression of electric wheel system driven by in-wheel motor considering unbalanced magnetic pull

2018 ◽  
Vol 233 (11) ◽  
pp. 2729-2745 ◽  
Author(s):  
Shuguang Zuo ◽  
Duoqiang Li ◽  
Yu Mao ◽  
Wenzhe Deng
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Dynamic Model ◽  
Electric Vehicles ◽  
Bp Neural Network ◽  
Longitudinal Vibration ◽  
Structural Parameters ◽  
Torque Ripple ◽  
Longitudinal Dynamics ◽  
Unbalanced Magnetic Pull

With the blowout of electric vehicles recently, the key parts of the electric vehicles driven by in-wheel motors named the electric wheel system become the core of development research. The torque ripple of the in-wheel motor mainly results in the longitudinal dynamics of the electric wheel system. The excitation sources are first analyzed through the finite element method, including the torque ripple induced by the in-wheel motor and the unbalanced magnetic pull produced by the relative motion between the stator and rotor. The accuracy of the finite element model is verified by the back electromotive force test of the in-wheel motor. Second, the longitudinal-torsional coupled dynamic model is established. The proposed model can take into account the unbalanced magnetic pull. Based on the model, the modal characteristics and the longitudinal dynamics of the electric wheel system are analyzed. The coupled dynamic model is verified by the vibration test of the electric wheel system. Two indexes, namely, the root mean square of longitudinal vibration of the stator and the signal-to-noise ratio of the tire slip rate, are proposed to evaluate the electric wheel longitudinal performance. The influence of unbalanced magnetic pull on the evaluation indexes of the longitudinal dynamics is analyzed. Finally, the influence of motor’s structural parameters on the average torque, torque ripple, and equivalent electromagnetic stiffness are analyzed through the orthogonal test. A surrogate model between the structural parameters of the in-wheel motor and the average torque, torque ripple, and equivalent electromagnetic stiffness is established based on the Bp neural network. The torque ripple and the equivalent electromagnetic stiffness are then reduced through optimizing the structural parameters of the in-wheel motor. It turns out that the proposed Bp neural network–based method is effective to suppress the longitudinal vibration of the electric wheel system.

BP Neural Network Application Research in Petrochemical Tower System Corrosion Prediction

2014 ◽  
Vol 488-489 ◽  
pp. 487-491 ◽  
Author(s):  
Yu Guang Fan ◽  
Min He ◽  
Hong Xian Lin ◽  
Bing Chen ◽  
San Ping Zhou
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Original Data ◽  
Training Data ◽  
Application Research ◽  
Fractionation Tower ◽  
Network Application ◽  
Standardized Method ◽  
Tower System

This paper takes the monitoring data sample from the top of fractionation tower system of one petrochemical company and uses prediction model which is constructed by BP neural network to study the corrosion prediction of catalytic fractionation tower top system. It uses min-max and z-score standardized method to deal with the original data and compare the impacts. The result shows that the BP neural constructing prediction model can provide basis of corrosion control for refinery. It also shows that better accuracy can be achieved by using min-max standardized method and when the number of training data quantity is over 20, the prediction result is more accurate and stable.

scholarly journals ANALYSIS OF THE LOCATION OF NANNING LARGE-SCALE MALL BASED ON BP NEURAL NETWORK

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 975-979
Author(s):  
H. Huang ◽  
L. L. Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Site Selection ◽  
Large Scale ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Location Analysis ◽  
Analysis Model ◽  
Reference Information ◽  
Neural Network Algorithm

Abstract. Site selection is a key first step in the operation of large-scale shopping malls, and most of the existing site selection methods lack practicality and efficiency. Therefore, it is necessary to carry out a scientific modeling of the site selection problem and provide effective reference information for site selection. With the development of machine learning algorithms, the modeling of such problems becomes more and more simple. In this paper, using matlab software as a tool, based on BP neural network algorithm, Nanning urban area is selected as the research object. After analyzing the influencing factors of location problem, the large-scale mall location analysis modeling is carried out. After repeated training and testing of the training data and the test data, the data for testing the usability is input into the model and applied for analysis. It turns out that the large-scale mall location analysis model is usable and can meet the site selection needs of the mall.

scholarly journals State of Charge Estimation of Power Battery Using Improved Back Propagation Neural Network

Batteries ◽  
2018 ◽  
Vol 4 (4) ◽  
pp. 69 ◽  
Author(s):  
Chuan-Wei Zhang ◽  
Shang-Rui Chen ◽  
Huai-Bin Gao ◽  
Ke-Jun Xu ◽  
Meng-Yue Yang
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Lithium Ion ◽  
Operating Conditions ◽  
State Of Charge ◽  
Training Data ◽  
Particle Swarm Algorithm ◽  
Power Battery

Accurately estimating the state of charge (SOC) of power batteries in electric vehicles is of great significance to the measurement of the endurance mileage of electric vehicles, as well as the safety protection of the power battery. In view of lithium ion batteries’ nonlinear relation between SOC estimation and current, voltage, and temperature, the improved Back Propagation (BP) neural network method is proposed to accurately estimate the SOC of power batteries. To address the inherent limitations of BP neural network, particle swarm algorithm is adopted to modify the relevant weighting coefficients. In this paper, the lithium iron phosphate battery (3.2 V/20 Amper-Hour) was studied. Charge and discharge experiments were conducted under a constant temperature. The training data were used to construct the surrogate model using the improved BP neural network. It is noted that the accuracy of the developed algorithm is increased by 2% as compared to that of conventional BP. Finally, an actual vehicle condition experiment was designed to further verify the accuracy of these two algorithms. The experimental results show that the improved algorithm is more suitable for real vehicle operating conditions than the traditional algorithm, and the estimation accuracy can meet the industry standards to a greater extent.

Modeling on Hydrocyclone Separation Performance by Neural Network

2011 ◽  
Vol 105-107 ◽  
pp. 185-188
Author(s):  
Feng Qin He ◽  
Ping Zhou ◽  
Jian Gang Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Structural Parameters ◽  
Input Parameter ◽  
Network Models ◽  
Separation Performance ◽  
Sampled Data ◽  
Neural Network Models

A 17-27-5 type BP neural network model was built, whose sampled data was got by hydrocyclone separation experiments; another 6-30-5 type BP neural network was also built, whose sampled data came from the simulation results of the LZVV of a hydrocyclone with CFD code FLUENT. The two neural network models also have good predictive validity aimed at hydrocyclone separation performance. It demonstrates LZVV structural parameters can embody hydrocyclone separation performance and reduce input parameter numbers of neural network model. It also indicates that the predictive model of hydrocyclone separation performance can be built by neural network.

Numerical Simulation on the Stress and Strain of Lining Structure with Double Shield Tunneling Construction

2012 ◽  
Vol 605-607 ◽  
pp. 2425-2429
Author(s):  
Feng Wang ◽  
Tie Jun Cui
Keyword(s):  
Ground Surface ◽  
Calculation Model ◽  
Tunnel Lining ◽  
Concrete Lining ◽  
Shield Tunnel ◽  
Tunnel Construction ◽  
Three Dimension ◽  
Shield Tunneling ◽  
Deformation Law ◽  
Lining Structure

This paper takes the Section 201 of shield construction engineering in Dalian Metro Line 2 as an example to analyze the deformation law of surrounding soil and the tunnel lining structure stress during shield tunnel construction. The shield tunnel construction is simulated dynamically by ADINA and the shield tunnel structure model of the concrete lining is established. This model is a three-dimension nonlinear finite element calculation model concerned with the grouting soil and original state soil. Taking the soil lithology in the upper layer and interact influences, we analyzed the dynamic process of shield construction, soil grouting and lining supporting and the stress distribution in difference reinforced concrete supporting segments and the ground settlement characteristics. Through numerical analysis method to study the deformation law of soil surrounding tunnel and the stress in tunnel lining, we get the results to compare with the results of Peck formula under the same condition. After generating the conclusions, we can provide several suggestions for shield tunneling construction, lining segment design and control of the ground surface settlement during the construction.

Refractive index sensing performance analysis of photonic crystal Mach–Zehnder interferometer based on BP neural network optimization

2015 ◽  
Vol 29 (10) ◽  
pp. 1550040
Author(s):  
Ying Chen ◽  
Teng Liu ◽  
Wenyue Wang ◽  
Qiguang Zhu ◽  
Weihong Bi
Keyword(s):  
Neural Network ◽  
Photonic Crystal ◽  
Bp Neural Network ◽  
Structural Parameters ◽  
Back Propagation ◽  
High Sensitivity ◽  
Photonic Devices ◽  
Zehnder Interferometer ◽  
Photon Localization ◽  
Mach Zehnder Interferometer

According to the band gap and photon localization characteristics, the single-arm notching and the double-arm notching Mach–Zehnder interferometer (MZI) structures based on 2D triangular lattice air hole-typed photonic crystal waveguide are proposed. The back-propagation (BP) neural network is introduced to optimize the structural parameters of the photonic crystal MZI structure, which results in the normalized transmission peak increasing from 85.3% to 97.1%. The sensitivity performances of the two structures are compared and analyzed using the Salmonella solution samples with different concentrations in the numerical simulation. The results show that the sensitivity of the double-arm notching structure is 4583 nm/RIU, which is about 6.4 times of the single-arm notching structure, which can provide some references for the optimization of the photonic devices and the design of high-sensitivity biosensors.

Early Stage Tunnel Lining Experimental Study of Jinhuashan Railway Tunnel Construction within Soft and Weak Rock Mass

2012 ◽  
Vol 204-208 ◽  
pp. 1532-1537
Author(s):  
Li Qiao Jin ◽  
Tai Quan Zhou ◽  
Bao Hua Lv
Keyword(s):  
Rock Mass ◽  
Reinforced Concrete ◽  
Early Stage ◽  
Tunnel Lining ◽  
Fiber Reinforced Concrete ◽  
Concrete Lining ◽  
Fiber Reinforced ◽  
Railway Tunnel ◽  
Tunnel Section ◽  
Lining Structure

Polypropylene fiber reinforced concrete can improve the common concrete flexibility and it is beneficial for interaction between concrete lining structure and rock mass. The use of fiber reinforced concrete with wet sprayed concrete technique can improve the concrete lining structure construction quality and improve the rock mass self-bearing capacity. The wet-sprayed fiber reinforced concrete is first introduced in Jinhuashan railway tunnel early stage lining structure within soft and weak rock mass. The design of Jinhuashan railway tunnel lining structure using fiber reinforced concrete is introduced and the requirement of material used is explained. To evaluate the lining effect using wet-sprayed fiber reinforced concrete, the online monitoring method is used to measure the rock mass pressure and the concrete lining layer stress for both the experimental tunnel sections and comparison tunnel section. The monitoring data result shows that the rock mass pressure in experimental section is even distribution with lower rock mass pressure and lower concrete lining layer stress. The value of rock mass pressure and tunnel lining layer stress in comparison tunnel section is a little higher than that in experimental tunnel section. The experimental tunnel section using fiber reinforced concrete has good lining effect.

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