scholarly journals A two-step approach for damage detection in beam based on influence line and bird mating optimizer

2017 ◽  
Vol 14 ◽  
pp. 102-107
Author(s):  
Zhong Rong Lu ◽  
Dong Tan
Keyword(s):  
Damage Detection ◽  
Influence Line ◽  
Bird Mating Optimizer

scholarly journals A two-step method for damage identification in beam structures based on influence line difference and acceleration data

2018 ◽  
Vol 10 (7) ◽  
pp. 168781401878740 ◽  
Author(s):  
D Tan ◽  
ZR Lu ◽  
JK Liu
Keyword(s):  
Objective Function ◽  
Structural Damage ◽  
Damage Identification ◽  
Dynamic Responses ◽  
Beam Structure ◽  
Modal Assurance Criterion ◽  
Beam Structures ◽  
Influence Line ◽  
Acceleration Data ◽  
Bird Mating Optimizer

This article presents a two-step approach for structural damage identification in beam structure. Damages are located using the influence line difference before and after damage, the calculation of damage severity is accomplished by acceleration data and bird mating optimizer algorithm. Local damages are simulated as the reduction of both the elemental Young’s modulus and mass of the beam. The technique for damage localization based on displacement influence line difference and its derivatives for beam structure has been outlined. An objective function that comprises dynamic acceleration is utilized in bird mating optimizer. All data are originated from only a few measurement points. Two numerical examples, namely, a simply supported beam and a four-span continuous beam, are investigated in this article. Identification results from different objective functions are compared with results from objective function conventional modal assurance criterion, which shows the superiority of the proposed function. In addition, results of dynamic responses under different types of excitation are presented. The effect of measurement noise level on damage identification results is studied. Studies in the article indicate that the proposed method is efficient and robust for identifying damages in beam structures.

scholarly journals Analysis of Key Technologies of Bridge Damage Detection Based on Visual Recognition

2021 ◽  
Vol 276 ◽  
pp. 02031
Author(s):  
Tan Xixue ◽  
Zhang Yunkai ◽  
Li Guohua ◽  
Pei Xuewen
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Visual Recognition ◽  
Main Line ◽  
Fujian Province ◽  
Influence Line ◽  
Vehicle Load ◽  
Key Technologies ◽  
Bridge Damage

In the identification of bridge damage in the series of deflection-affecting lines, noise signals due to axle coupling often occur. Removing these noise signals has become a key technique for effectively identifying damage. Taking the main line bridge of a overpass project in Fuzhou City of Fujian Province as an example, we collected the deflection a data of the bridge by using HPON-X target-free bridge deflectometer and used YOLOv3 algorithm for deep learning of the vehicle load position. The data were measured and studied by using DB9 wavelet de-noising method. The research shows that this method can greatly reduce the influence of vehicle bridge interaction on deflection influence line, and can enhance the accuracy and speed of bridge damage detection.

Damage Detection Based on Structural Responses Induced by Traffic Load: Methodology and Application

2016 ◽  
Vol 16 (04) ◽  
pp. 1640026 ◽  
Author(s):  
Shouwang Sun ◽  
Limin Sun ◽  
Lin Chen
Keyword(s):  
Damage Detection ◽  
Traffic Load ◽  
Monitoring Data ◽  
Random Disturbance ◽  
Influence Line ◽  
Structural Deterioration ◽  
Structural Responses ◽  
Long Term Monitoring ◽  
Term Monitoring

In consideration of the important role that bridges play in transportation system, their safety, durability and serviceability have always been deeply concerned. Recently, many long-span bridges have been instrumented with Structural Health Monitoring Systems (SHMS) to provide bridge engineers with the information needed for decisions-making in management and maintenance. However, efficient use of monitoring data remains a challenge confronted before engineers. Recently, methodologies based on monitoring data while robust to random disturbance and sensitive to damage have received worldwide attention. In this context, this study proposes an innovative damage detection methodology based on structural responses induced by traffic load. First, vehicle-induced strain responses are found to be separable from the strain induced by operational loads, owing to their unique characteristics. This is achieved by a detailed investigation on the relationship between strain measurements and operational loads including temperature, wind as well as vehicles based on long-term monitoring data. From the vehicular load and pertinent strain response, the strain influence line (SIL) can be further identified. As a structural signature, SIL can be used to provide a reasonable assessment of the bridge health condition at least in the vicinity of strain monitoring point. Two damage indexes are therefore derived from the identified SIL, which are promising for damage evaluation because they are: (a) capable of revealing structural deterioration; (b) immune to influences of environmental changes; (c) adaptable to the random characteristic exhibited by long-term monitoring data. Besides, the SIL identification procedure and its theoretical basis are elaborated to respectively handle the case where the vehicle load is available or not, which is also applicable to identify the influence line of other measurements such as stresses. The proposed damage methodology is applied to the cable-stayed bridge spanning the main navigation channel of Shanghai Yangtze River Bridge, and the result shows its effectiveness.

Stress Influence Line Identification of Long Suspension Bridges Installed with Structural Health Monitoring Systems

2016 ◽  
Vol 16 (04) ◽  
pp. 1640023 ◽  
Author(s):  
Zhi Wei Chen ◽  
Qin Lin Cai ◽  
Jun Li
Keyword(s):  
Damage Detection ◽  
Health Monitoring ◽  
Suspension Bridge ◽  
Monitoring Systems ◽  
Suspension Bridges ◽  
Long Span ◽  
Influence Line ◽  
Induced Stress ◽  
Health Monitoring Systems ◽  
Stress Influence

Numerous long-span suspension bridges have been built worldwide over the past few decades. To ensure the safety of such bridges and their users during the bridge service life, several bridges have been equipped with Structural Health Monitoring Systems (SHMSs), which measure dynamic bridge responses and various loading types on-site. Integrating SHMS and damage detection technology for condition assessment of these bridges has become a new development trend. Recent studies have proven that stress influence line (SIL)-based damage indices achieve excellent damage detection performance for a long suspension bridge. However, an accurate and prompt manner of identifying the SIL of a long suspension bridge is important to facilitate the development of the SIL for an effective damage index. Identifying the SIL from field measurement data under in-service conditions has several advantages over the traditional static loading test. This study proposes and develops a new SIL identification method by integrating the least squares solution and Weighted Moving Average (WMA) based on the measured train information and the corresponding train-induced stress time history. The efficacy of the proposed method is validated through its application to Tsing Ma Bridge (TMB). The good agreement between the identified and baseline SILs for a typical diagonal truss member verifies the effectiveness of the proposed method. Furthermore, robustness testing is performed by identifying SIL on the basis of information on different trains and train-induced stress responses and by identifying the SIL of different types of bridge components. Results indicate the feasibility of the application of the proposed approach to SIL identification for long-span bridges.

Antioptimization for comparison of alternative structural models and damage detection

AIAA Journal ◽  
1999 ◽  
Vol 37 ◽  
pp. 857-864
Author(s):  
S. N. Gangadharan ◽  
E. Nikolaidis ◽  
K. Lee ◽  
R. T. Haftka ◽  
R. Burdisso
Keyword(s):  
Damage Detection ◽  
Structural Models
Sign in / Sign up

Export Citation Format

Share Document