A new method to identify bridge bearing damage based on Radial Basis Function Neural Network

Chen, Zhaowei; Fang, Hui; Ke, Xinmeng; Zeng, Yiming

doi:10.12989/eas.2016.11.5.841

Cited by 9 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…proposed a novel method to recognize bridge bearing damage based on the Neural Network theory [9]. However, it is no doubt that the damaged bearings affect the dynamic responses of the train-track-bridge system, few studies have paid attention to this research field.…”

Section: Introductionmentioning

confidence: 99%

Damage Model of Bridge Bearing and Its Influence on Train-Track-Bridge Dynamic System

CHEN

2022

mech

View full text Add to dashboard Cite

Bridge bearing damage is unavoidable in railway engineering. This work presents an investigation on the damage model of bridge bearing and its influence on the dynamic responses of the high-speed train-track-bridge system. Primarily, the mathematical description of bridge bearing damage model is explained in detail, which is then compared with the traditional linear model. Then a 3D train-track-bridge dynamic model considering bearing damage is established based on the train-track-bridge dynamic interaction theory. Adopting the model, the influences of single-point-damage and multi-point-damage on the dynamic behaviors of the coupled system are deeply investigated. Results show that the calculated obtained by the proposed model and the traditional linear model are different, especially in frequency-domain. Compared to results of traditional linear model, the results obtained by the proposed model are smaller due to the additional damping effect. Bridge bearing damage has almost no influence on the train vibrations, while has great effect on bridge vibrations. The damage in z-direction mainly affects the vertical vibrations, while damage in y-direction mainly influences the lateral vibrations. Among all the damaged bearings in this work, the influence of damage in x-direction has the least influence on the system. The damage in the fixed bearing is larger than that in other bearings, indicating that more attentions should be paid to the health monitoring of fixed bearings in railway engineering.

show abstract

Section: Introductionmentioning

confidence: 99%

Damage Model of Bridge Bearing and Its Influence on Train-Track-Bridge Dynamic System

CHEN

2022

mech

View full text Add to dashboard Cite

show abstract

“…Vibration-based structural damage identification methods have been obtained and widely used in various civil engineering examples [9,10], which provide a possible solution to the bearing damage detection. Chen et al [11] investigated the feasibility and sensitivity of bearing damage identification using bridge vibration modes and the radial basis function neural network; the results indicate that modal information can reflect the bearing damage clearly. Ni et al [12] adopted correlation and sensitivity analysis to put forward a damage identification indicator of the bridge pot rubber bearing and then combined with the support vector machine to form a safety-level discriminant model to assess the health state of the bridge pot rubber bearing.…”

Section: Introductionmentioning

confidence: 99%

Bearing Damage Detection of a Bridge under the Uncertain Conditions Based on the Bayesian Framework and Matrix Perturbation Method

Lei

Zhou

et al. 2021

Shock and Vibration

View full text Add to dashboard Cite

The bearing of a bridge, as a critical component, is important in the force transformation of the superstructure; however, due to the service condition and repeated impact load, the bearing is prone to be damaged but difficult to detect the damage; the present research has few studies that focused on the damage detection of the structural bearing. Meanwhile, practical engineering is always surrounded by variational environmental conditions, and sometimes, the element and bearing damage both exist in the structure. Thus, these uncertain conditions all cause inaccurate damage identification results using the vibration-based damage detection method. In order to detect the damage of the structural bearing and improve the precision, firstly, the structural dynamic characteristic equation considering uncertain conditions has been deduced; then, a damage detection framework constructed by the Bayesian theory and perturbation method has been developed in this article; a numerical example of an 8-span concrete continuous beam and a practical example of I-40 steel-concrete composite bridge are utilized to validate the feasibility of the proposed method, and single type and two types of damage cases are studied. The outcomes demonstrate that the damage of structural elements and bearings can be detected with high accuracy. The proposed method is of great applicability and good potential.

show abstract

“…The RBFNN method is also efficient in the parameter identifications of the bridge bearings. 14 In nature, the RS method is a traditional optimization approach based on nonlinear fitting using quadratic polynomials. However, the SVR method is a machine learning method based on statistical learning theory, and the RBFNN method employs artificial intelligence models.…”

Section: Introductionmentioning

confidence: 99%

Finite element model updating for the Tsing Ma Bridge tower based on surrogate models

Fan

Xiao

2021

Journal of Low Frequency Noise, Vibration and Active Control

View full text Add to dashboard Cite

This paper investigates the efficiency of three surrogate model-based dynamic finite element model updating methods, the response surface, the support vector regression, and the radial basis function neural network, using the engineering background of the Tsing Ma Bridge tower. The influences of two different sampling methods (central composite design sampling and Box–Behnken design sampling) on the model updating results are also assessed. It was deduced that the impact of the surrogate model type on the updating results is not significant. More precisely, the models updated using the response surface method and the support vector regression method are similar in terms of reproducing the dynamic characteristics of the physical truth. However, the effects of the employed sampling method on the model updating results are significant as the model updating quality using the central composite design sampling method is higher than that using the Box–Behnken design sampling method in some considered cases.

show abstract

A new method to identify bridge bearing damage based on Radial Basis Function Neural Network

Cited by 9 publications

References 16 publications

Damage Model of Bridge Bearing and Its Influence on Train-Track-Bridge Dynamic System

Damage Model of Bridge Bearing and Its Influence on Train-Track-Bridge Dynamic System

Bearing Damage Detection of a Bridge under the Uncertain Conditions Based on the Bayesian Framework and Matrix Perturbation Method

Finite element model updating for the Tsing Ma Bridge tower based on surrogate models

Contact Info

Product

Resources

About