scholarly journals Disassembling-Based Structural Damage Detection Using Static Measurement Data

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Eun-Taik Lee ◽  
Hee-Chang Eun
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Degrees Of Freedom ◽  
Measurement Data ◽  
Truss Structures ◽  
Detection Methods ◽  
Internal Stresses ◽  
Structural Damage Detection ◽  
Constraint Forces ◽  
Entire Structure

Damage detection methods can be classified into global and local approaches depending on the division of measurement locations in a structure. The former utilizes measurement data at all degrees of freedom (DOFs) for structural damage detection, while the latter utilizes data of members and substructures at a few DOFs. This paper presents a local method to detect damages by disassembling an entire structure into members. The constraint forces acting at the measured DOFs of the disassembled elements at the damaged state, and their internal stresses, are predicted. The proposed method detects locally damaged members of the entire structure by comparing the stress variations before and after damage. The static local damage can be explicitly detected when it is positioned along the constraint load paths. The validity of the proposed method is illustrated through the damage detection of two truss structures, and the disassembling (i.e., local) and global approaches are compared using numerical examples. The numerical applications consider the noise effect and single and multiple damage cases, including vertical, diagonal, and chord members of truss structures.

Non-baseline method for damage detection in truss structures using displacement and strain measurements

2018 ◽  
Vol 22 (3) ◽  
pp. 818-830 ◽  
Author(s):  
Peng Ren ◽  
Zhi Zhou ◽  
Jinping Ou
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Experimental Studies ◽  
Measurement Data ◽  
Element Model ◽  
Truss Structures ◽  
Detection Methods ◽  
Detection Approach ◽  
Potential Development ◽  
System States

Realistic problems restrict the application of many existing structural damage detection methods. Due to the requirement of a comparison between two system states, lack of appropriate baseline data may become one of the limitations to undertake structural health monitoring strategy. This article suggests a non-baseline damage detection approach based on the mixed measurements and the transmissibility concept and demonstrates it in truss structures. The algorithm uses the measurement data from the strains of the truss elements and the displacements of the truss joints, in which the displacements are utilized to estimate the baseline strains based on the transmissibility matrix from an initial finite element model. Wavelet-based damage-sensitive features are extracted from both estimated and measured strains to detect damages of the target elements. Numerical and experimental studies are performed to investigate the feasibility and effectiveness of the proposed approach. It is concluded from the instances that the robustness of the algorithm is realized when handling the measurement noise, modeling errors and the operational condition variability. These permit the potential development of the damage detection method for real structures in site.

scholarly journals A Universal Fast Algorithm for Sensitivity-Based Structural Damage Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Q. W. Yang ◽  
J. K. Liu ◽  
C.H. Li ◽  
C.F. Liang
Keyword(s):  
Damage Detection ◽  
Fast Algorithm ◽  
Structural Damage ◽  
Research Area ◽  
Detection Methods ◽  
Structural Damage Detection ◽  
Response Data ◽  
Flexibility Matrix ◽  
New Research ◽  
Measured Response

Structural damage detection using measured response data has emerged as a new research area in civil, mechanical, and aerospace engineering communities in recent years. In this paper, a universal fast algorithm is presented for sensitivity-based structural damage detection, which can quickly improve the calculation accuracy of the existing sensitivity-based technique without any high-order sensitivity analysis or multi-iterations. The key formula of the universal fast algorithm is derived from the stiffness and flexibility matrix spectral decomposition theory. With the introduction of the key formula, the proposed method is able to quickly achieve more accurate results than that obtained by the original sensitivity-based methods, regardless of whether the damage is small or large. Three examples are used to demonstrate the feasibility and superiority of the proposed method. It has been shown that the universal fast algorithm is simple to implement and quickly gains higher accuracy over the existing sensitivity-based damage detection methods.

Structural damage detection via combining weighted strategy with trace Lasso

2018 ◽  
Vol 22 (3) ◽  
pp. 597-612 ◽  
Author(s):  
Chengbin Chen ◽  
Chudong Pan ◽  
Zepeng Chen ◽  
Ling Yu
Keyword(s):  
Damage Detection ◽  
Swarm Intelligence ◽  
Structural Damage ◽  
Optimization Problem ◽  
Detection Method ◽  
Rapid Development ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Structural Damage Detection ◽  
Selection Operator

With the rapid development of computation technologies, swarm intelligence–based algorithms become an innovative technique used for addressing structural damage detection issues, but traditional swarm intelligence–based structural damage detection methods often face with insufficient detection accuracy and lower robustness to noise. As an exploring attempt, a novel structural damage detection method is proposed to tackle the above deficiency via combining weighted strategy with trace least absolute shrinkage and selection operator (Lasso). First, an objective function is defined for the structural damage detection optimization problem by using structural modal parameters; a weighted strategy and the trace Lasso are also involved into the objection function. A novel antlion optimizer algorithm is then employed as a solution solver to the structural damage detection optimization problem. To assess the capability of the proposed structural damage detection method, two numerical simulations and a series of laboratory experiments are performed, and a comparative study on effects of different parameters, such as weighted coefficients, regularization parameters and damage patterns, on the proposed structural damage detection methods are also carried out. Illustrated results show that the proposed structural damage detection method via combining weighted strategy with trace Lasso is able to accurately locate structural damages and quantify damage severities of structures.

Structural Damage Identification Based on Information Fusion Techniques

2010 ◽  
Author(s):  
Hui Li ◽  
Yuequan Bao
Keyword(s):  
Damage Detection ◽  
Information Fusion ◽  
Structural Damage ◽  
Damage Identification ◽  
Yellow River ◽  
Numerical Study ◽  
Detection Methods ◽  
Data Sets ◽  
Structural Damage Detection ◽  
Selective Fusion

With the aim to decrease the uncertainties of structural damage detection, two fusion models are presented in this paper. The first one is a weighted and selective fusion method for combing the multi-damage detection methods based on the integration of artificial neural network, Shannon entropy and Dempster-Shafer (D-S) theory. The second one is a D-S based approach for combing the damage detection results from multi-sensors data sets. Numerical study on the Binzhou Yellow River Highway Bridge and an experimental of a 20-bay rigid truss structure were carried out to validate the uncertainties decreasing ability of the proposed methods for structural damage detection. The results show that both of the methods proposed are useful to decrease the uncertainties of damage detection results.

Effect of Attribute Reduction on Rough-Probabilistic Neural Network for Structural Damage Detection

2010 ◽  
Vol 163-167 ◽  
pp. 2482-2487
Author(s):  
Shao Fei Jiang ◽  
Zhao Qi Wu
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Data Storage ◽  
Structural Damage ◽  
Probabilistic Neural Network ◽  
Attribute Reduction ◽  
Measurement Data ◽  
Noise Tolerance ◽  
Structural Damage Detection ◽  
Proposed Model

In this paper, a new rough-probabilistic neural network (RSPNN) model, whereby rough set data and a probabilistic neural network (PNN) are integrated, is proposed. This model is used for structural damage detection, particularly for cases where the measurement data has many uncertainties. To verify the proposed method, an example is presented to identify both single and multi-damage case patterns. The effects of measurement noise and attribute reduction on the damage detection results are also discussed. The results show that the proposed model not only has good damage detection capability and noise tolerance, but also reduces data storage memory requirements.

Structural Damage Detection Accounting for Loss of Data in Wireless Network Sensors

2009 ◽  
Vol 413-414 ◽  
pp. 125-132 ◽  
Author(s):  
Ser Tong Quek ◽  
Viet Anh Tran ◽  
Xiao Yan Hou
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Wireless Sensors ◽  
Detection Method ◽  
Truss Structures ◽  
Monitoring Systems ◽  
Structural Damage Detection ◽  
Cumulative Energy ◽  
Health Monitoring Systems ◽  
Identification Parameter

For detection of damage in frame and truss structures, the normalized cumulative energy is proposed as the identification parameter within the framework of the damage locating vector (DLV) method. Due to the limited number of sensors used, it is necessary to filter out the actual damaged elements from the identified set of potential damaged elements. An intersection scheme using only the measured signals is proposed for the filtering and verified using a warehouse frame comprising truss, beam and column elements. As wireless sensors are introduced into structural health monitoring systems, loss of data during transmission is one significant issue which needs to be addressed. An algorithm to patch the loss data is proposed and when integrated with the proposed damage detection method is experimentally shown to be feasible using a cantilever truss.

scholarly journals A Real-Time Structural Damage Detection method for High-Pile Wharf Foundations

2021 ◽  
Vol 283 ◽  
pp. 01022
Author(s):  
Yongcheng Liu ◽  
Yonglai Zheng ◽  
Yujue Zhou
Keyword(s):  
Damage Detection ◽  
Real Time ◽  
Finite Element Modelling ◽  
Structural Damage ◽  
Detection Method ◽  
Lateral Displacement ◽  
Detection Methods ◽  
Economic Damage ◽  
Structural Damage Detection ◽  
Port Engineering

As one of the most common structural forms in port engineering, the operation environment of high-pile wharf is quite harsh and complex, and its pile foundation often produces structural damage of different degrees. Until now, there is a lack of efficient, safe and economic damage detection methods. A novel and precise real-time structural damage detection (SDD) method using both finite element modelling (FEM) and 1D convolutional neural networks (CNNs) is established in this study. The results indicate that the proposed method could accurately identify the presence and location of damage in real time. The results also demonstrated that the proposed 1D CNNs based model are more sensitive to the longitudinal and lateral displacement responses of the high-pile wharf structure.

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