scholarly journals Determination of Bridge Prestress Loss under Fatigue Load Based on PSO-BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yongguang Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Prestressed Concrete ◽  
Concrete Beam ◽  
Fatigue Load ◽  
Prestress Loss ◽  
Test Beam ◽  
Prestressed Concrete Beam ◽  
Network Algorithm ◽  
Neural Network Algorithm

During the service period of a prestressed concrete bridge, as the number of cyclic loads increases, cumulative fatigue damage and prestress loss will occur inside the structure, which will affect the safety, durability, and service life of the structure. Based on this, this paper studies the loss of bridge prestress under fatigue load. First, the relationship between the prestress loss of the prestressed tendons and the residual deflection of the test beam is analyzed. Based on the test results and the main influencing factors of fatigue and creep, a concrete fatigue and creep calculation model is proposed; then, based on the static cracking check calculation method and POS-BP neural network algorithm, a prestressed concrete beam fatigue cracking check model under repeated loads is proposed. Finally, the mechanical performance of the prestressed concrete beam after fatigue loading is analyzed, and the influence of the fatigue load on the bearing capacity of the prestressed concrete beam is explored. The results show that the bridge prestress loss characterization model based on the POS-BP neural network algorithm has the advantages of high calculation efficiency and strong applicability.

Nature Frequency Identification of Prestressed Concrete Beam with RBF Network

2012 ◽  
Vol 446-449 ◽  
pp. 362-365 ◽  
Author(s):  
Rui Ge Li ◽  
Guo Li Yang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Test Data ◽  
Basis Function ◽  
Prestressed Concrete ◽  
Concrete Beam ◽  
Test Beam ◽  
Rbf Network ◽  
Prestressed Concrete Beam ◽  
Radial Basis

Abstract. The relationship of pre-stressed concrete (PSC) beam natural frequency and pre-stressed force is difficult to described accurately with mechanical model. The past experimental data are collected. Then five unbonded post-tensioned PSC beams are designed. Frequencies and damps are collected in the dynamic experiment of five PSC beams. Radial basis function neural network is constructed to identify the natural frequencies of prestressed beam with different levels prestressing force based on previous test data and new dynamic test beam data. Then the input and output node numbers of neural network are selected and the appropriate training algorithm and expansion coefficient is determined. In order to verify that the network performance, one prestressed concrete beam test data are left to simulation test. Simulation results show that the radial basis function neural network is feasibility to recognize the frequency of PSC beams.

Precision Power System Harmonic Analysis Algorithm Based on ABC-BPNN

2013 ◽  
Vol 483 ◽  
pp. 630-634
Author(s):  
Shu Chuan Gan ◽  
Ling Tang ◽  
Li Cao ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Power System ◽  
Harmonic Analysis ◽  
Bp Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Bee Colony ◽  
Network Algorithm ◽  
Neural Network Algorithm

An algorithm of artificial colony algorithm to optimize the BP neural network algorithm was presented and used to analyze the harmonics of power system. The artificial bee colony algorithm global searching ability, convergence speed for the BP neural network algorithm for harmonic analysis is easy to fall into local optimal solution of the disadvantages, and the initial weights of the artificial bee colony algorithm also greatly enhance whole algorithm model generalization capability. This algorithm using MATLAB for Artificial bee colony algorithm and BP neural network algorithm simulation training toolbox found using artificial bee colony algorithm to optimize BP neural network algorithm converges faster results with greater accuracy, with better harmonic analysis results.

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