scholarly journals Damage Identification of Unreinforced Masonry Panels Using Vibration-Based Techniques

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Claudio Oyarzo-Vera ◽  
Nawawi Chouw
Keyword(s):  
Spatial Distribution ◽  
Damage Detection ◽  
Numerical Simulations ◽  
Damage Identification ◽  
Unreinforced Masonry ◽  
Masonry Structures ◽  
Damage Distribution ◽  
Modal Response ◽  
Damage States ◽  
Damage Indicators

Several damage indicators based on changes in modal properties validated for homogeneous materials were applied to detect damage in an unreinforced masonry cantilever panel. Damage was created by a “clean diagonal cut” at the center of the specimen which length was progressively extended towards the specimen’s corners. Numerical simulations were employed to determine the modal response at several damage states and this data was used to calculate the damage indicators. Those indicators presenting a good performance were then applied to identify damage on a physical specimen tested in the laboratory. The outcomes of this study demonstrated that vibration-based damage detection in unreinforced masonry structures can be satisfactorily performed. However, the identification of the damage spatial distribution using vibration-based methods in unreinforced masonry structures is still difficult. To improve the prediction of damage distribution, a large number of measurement points need to be considered to obtain an acceptable level of resolution.

scholarly journals A Neural-Wavelet Technique for Damage Identification in the ASCE Benchmark Structure Using Phase II Experimental Data

2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
Mahmoud M. Reda Taha
Keyword(s):  
Damage Detection ◽  
Damage Identification ◽  
Damage Classification ◽  
Integrated Method ◽  
Damage Recognition ◽  
Wavelet Energy ◽  
Challenging Tasks ◽  
Damage States ◽  
Artificial Neural Network Ann ◽  
American Society

Damage pattern recognition research represents one of the most challenging tasks in structural health monitoring (SHM). The vagueness in defining damage and the significant overlap between damage states contribute to the challenges associated with proper damage classification. Uncertainties in the damage features and how they propagate during the damage detection process also contribute to uncertainties in SHM. This paper introduces an integrated method for damage feature extraction and damage recognition. We describe a robust damage detection method that is based on using artificial neural network (ANN) to compute the wavelet energy of acceleration signals acquired from the structure. We suggest using the wavelet energy as a damage feature to classify damage states in structures. A case study is presented that shows the ability of the proposed method to detect and pattern damage using the American Society of Civil Engineers (ASCEs) benchmark structure. It is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy to varying levels of damage.

Application of Extreme Learning Machine to Damage Detection of Plate-Like Structures

2017 ◽  
Vol 17 (07) ◽  
pp. 1750068 ◽  
Author(s):  
S. S. Kourehli
Keyword(s):  
Damage Detection ◽  
Extreme Learning Machine ◽  
Damage Identification ◽  
Mode Shapes ◽  
Effective Technique ◽  
Plate Structures ◽  
Modal Data ◽  
Plate Elements ◽  
Damage States ◽  
Learning Machine

An effective method for damage detection of plate structures using the extreme learning machine (ELM) is proposed in this study. With the ELM, the mode shapes and natural frequencies of a damaged plate are treated as the input and the damage states in the plate elements as the output. The proposed method was applied to two numerical examples, namely, a cantilever and a plate with four-fixed supports containing one or several damages with and without noise in the modal data. The results obtained reveal that the methodology can be used as an effective technique for the damage identification of plate structures using the modal data and ELM.

Vibration-based damage identification of an unreinforced masonry house model

2016 ◽  
Vol 20 (3) ◽  
pp. 331-351 ◽  
Author(s):  
Claudio Oyarzo-Vera ◽  
Jason Ingham ◽  
Nawawi Chouw
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Unreinforced Masonry ◽  
Homogeneous Material ◽  
Material Degradation ◽  
Damage Distribution ◽  
Damage State ◽  
Slender Structure ◽  
Identification Techniques ◽  
Non Destructive

Non-destructive vibration-based damage identification techniques are especially attractive for assessing damage in structures of high historical and architectural value. So far, most studies have focused on slender structures built using relatively homogeneous materials. In this study, global damage identification methods based on vibration response parameters were applied for identifying damage in an unreinforced masonry full-scale house model (non-homogeneous material and non-slender structure). The house model was dynamically loaded using an eccentric-mass shaker. Structural damage to the walls was initiated by increasing the amplitude of the applied load. At each damage state, a modal test was performed by impacting the walls with a calibrated hammer. Statistically significant variations of modal frequencies and the modal assurance criteria were considered as suitable parameters to identify damage. It was concluded that different sets of modes can be found for different states of damage because of material degradation, change in the support and connectivity conditions, and breaks in the members continuity generated by damage. All these changes are reflected in variations of modal frequencies and modal assurance criteria. It was also established that prior to identifying the damage distribution on the entire building, it was necessary to determine how the modal frequencies were related to each wall.

Sparse damage detection via the elastic net method using modal data

2021 ◽  
pp. 147592172110219
Author(s):  
Rongrong Hou ◽  
Xiaoyou Wang ◽  
Yong Xia
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Measurement Data ◽  
Elastic Net ◽  
Sensitivity Matrix ◽  
Regularization Technique ◽  
Modal Data ◽  
Regularization Techniques ◽  
Net Method

The l1 regularization technique has been developed for damage detection by utilizing the sparsity feature of structural damage. However, the sensitivity matrix in the damage identification exhibits a strong correlation structure, which does not suffice the independency criteria of the l1 regularization technique. This study employs the elastic net method to solve the problem by combining the l1 and l2 regularization techniques. Moreover, the proposed method enables the grouped structural damage being identified simultaneously, whereas the l1 regularization cannot. A numerical cantilever beam and an experimental three-story frame are utilized to demonstrate the effectiveness of the proposed method. The results showed that the proposed method is able to accurately locate and quantify the single and multiple damages, even when the number of measurement data is much less than the number of elements. In particular, the present elastic net technique can detect the grouped damaged elements accurately, whilst the l1 regularization method cannot.

scholarly journals Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain Response

Vibration ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 422-445
Author(s):  
Md Riasat Azim ◽  
Mustafa Gül
Keyword(s):  
Damage Detection ◽  
Damage Identification ◽  
Numerical Study ◽  
Damage Index ◽  
Strain Analysis ◽  
Time History ◽  
Principal Component ◽  
Element Model ◽  
Railway Bridges ◽  
Truss Bridge

Railway bridges are an integral part of any railway communication network. As more and more railway bridges are showing signs of deterioration due to various natural and artificial causes, it is becoming increasingly imperative to develop effective health monitoring strategies specifically tailored to railway bridges. This paper presents a new damage detection framework for element level damage identification, for railway truss bridges, that combines the analysis of acceleration and strain responses. For this research, operational acceleration and strain time-history responses are obtained in response to the passage of trains. The acceleration response is analyzed through a sensor-clustering-based time-series analysis method and damage features are investigated in terms of structural nodes from the truss bridge. The strain data is analyzed through principal component analysis and provides information on damage from instrumented truss elements. A new damage index is developed by formulating a strategy to combine the damage features obtained individually from both acceleration and strain analysis. The proposed method is validated through a numerical study by utilizing a finite element model of a railway truss bridge. It is shown that while both methods individually can provide information on damage location, and severity, the new framework helps to provide substantially improved damage localization and can overcome the limitations of individual analysis.

Structural Damage Assessment Using Output-Only Measurement: Localization and Quantification

2013 ◽  
Author(s):  
Chin-Hsiung Loh ◽  
Min-Hsuan Tseng ◽  
Shu-Hsien Chao
Keyword(s):  
Damage Detection ◽  
Damage Assessment ◽  
Structural Damage ◽  
Damage Identification ◽  
Model Updating ◽  
Null Space ◽  
Singular Spectrum Analysis ◽  
Damage Index ◽  
Computation Efficiency ◽  
Output Only

One of the important issues to conduct the damage detection of a structure using vibration-based damage detection (VBDD) is not only to detect the damage but also to locate and quantify the damage. In this paper a systematic way of damage assessment, including identification of damage location and damage quantification, is proposed by using output-only measurement. Four level of damage identification algorithms are proposed. First, to identify the damage occurrence, null-space and subspace damage index are used. The eigenvalue difference ratio is also discussed for detecting the damage. Second, to locate the damage, the change of mode shape slope ratio and the prediction error from response using singular spectrum analysis are used. Finally, to quantify the damage the RSSI-COV algorithm is used to identify the change of dynamic characteristics together with the model updating technique, the loss of stiffness can be identified. Experimental data collected from the bridge foundation scouring in hydraulic lab was used to demonstrate the applicability of the proposed methods. The computation efficiency of each method is also discussed so as to accommodate the online damage detection.

scholarly journals Modelling imperfections in unreinforced masonry structures: Discrete element simulations and scale model experiments of a pavilion vault

2021 ◽  
Vol 228 ◽  
pp. 111499
Author(s):  
Alessandro Dell'Endice ◽  
Antonino Iannuzzo ◽  
Matthew J. DeJong ◽  
Tom Van Mele ◽  
Philippe Block
Keyword(s):  
Discrete Element ◽  
Unreinforced Masonry ◽  
Scale Model ◽  
Masonry Structures ◽  
Model Experiments ◽  
Discrete Element Simulations

scholarly journals Use of Time- and Frequency-Domain Approaches for Damage Detection in Civil Engineering Structures

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
V. H. Nguyen ◽  
J. Mahowald ◽  
S. Maas ◽  
J.-C. Golinval
Keyword(s):  
Damage Detection ◽  
Frequency Domain ◽  
Prestressed Concrete ◽  
Civil Engineering ◽  
Damage Identification ◽  
Real Life ◽  
Hankel Matrix ◽  
Principal Component ◽  
Engineering Structures ◽  
Civil Engineering Structures

The aim of this paper is to apply both time- and frequency-domain-based approaches on real-life civil engineering structures and to assess their capability for damage detection. The methodology is based on Principal Component Analysis of the Hankel matrix built from output-only measurements and of Frequency Response Functions. Damage detection is performed using the concept of subspace angles between a current (possibly damaged state) and a reference (undamaged) state. The first structure is the Champangshiehl Bridge located in Luxembourg. Several damage levels were intentionally created by cutting a growing number of prestressed tendons and vibration data were acquired by the University of Luxembourg for each damaged state. The second example consists in reinforced and prestressed concrete panels. Successive damages were introduced in the panels by loading heavy weights and by cutting steel wires. The illustrations show different consequences in damage identification by the considered techniques.

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