Damage identification method of prestressed concrete beam bridge based on convolutional neural network

2020 ◽  
Author(s):  
Sanqiang Yang ◽  
Yong Huang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Prestressed Concrete ◽  
Concrete Beam ◽  
Damage Identification ◽  
Identification Method ◽  
Prestressed Concrete Beam ◽  
Beam Bridge

scholarly journals Analysis of Sunshine Temperature Field and Temperature Effect of Prestressed Concrete Beam Bridge

2020 ◽  
Vol 571 ◽  
pp. 012082
Author(s):  
Liuyu Zhang ◽  
Shufa He ◽  
Guitong Zhang ◽  
Jingwen Zhou ◽  
Lezhou Huang ◽  
...  
Keyword(s):  
Temperature Field ◽  
Temperature Effect ◽  
Prestressed Concrete ◽  
Concrete Beam ◽  
Prestressed Concrete Beam ◽  
Beam Bridge

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.

Prestressed Concrete Beam Bridge Design and Main Parameters of Prestressed Steel Beam Calculations and Arrangement

2014 ◽  
Vol 496-500 ◽  
pp. 2501-2504
Author(s):  
Xi Jun Yin
Keyword(s):  
Cross Section ◽  
Prestressed Concrete ◽  
Concrete Beam ◽  
Steel Beam ◽  
Prestressed Concrete Beam ◽  
Steel Welds ◽  
Prestressed Reinforcement ◽  
Edge Cracking ◽  
Beam Bridge ◽  
Beam Edge

Prestressed concrete beam bridge cross-section using T-shaped cross-section. Anti-shrinkage reinforcement requirements adopted under the dense arrangement of all the memorial steel welds are double-sided welding, using a special form of prestressed reinforcement arranged in assembly is completed, tension on the deck to prevent the beam edge cracking.

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.

Sign in / Sign up

Export Citation Format

Share Document