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

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.

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A Damage Detection Method Using Neural Network Optimized by Multiple Particle Collision Algorithm

Journal of Sensors ◽

10.1155/2021/9998187 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Sergio V. Farias ◽

Osamu Saotome ◽

Haroldo F. Campos Velho ◽

Elcio H. Shiguemori

Keyword(s):

Neural Network ◽

Damage Detection ◽

Lamb Wave ◽

Learning Strategy ◽

Damage Identification ◽

Particle Collision ◽

Learning Approaches ◽

Propagation Methods ◽

Artificial Neural Network Ann ◽

Multiple Particle

A critical task of structural health monitoring is damage detection and localization. Lamb wave propagation methods have been successfully applied for damage identification in plate-like structures. However, Lamb wave processing is still a challenging task due to its multimodal and dispersive characteristics. To address this issue, data-driven machine learning approaches as artificial neural network (ANN) have been proposed. However, the effectiveness of ANN can be improved based on its architecture and the learning strategy employed to train it. The present paper proposes a Multiple Particle Collision Algorithm (MPCA) to design an optimum ANN architecture to detect and locate damages in plate-like structures. For the first time in the literature, the MPCA is applied to find damages in plate-like structures. The present work uses one piezoelectric transducer to generate Lamb wave signals on an aluminum plate structure and a linear array of four transducers to capture the scattered signals. The continuous wavelet transform (CWT) processes the captured signals to estimate the time-of-flight (ToF) that is the ANN inputs. The ANN output is the damage spatial coordinates. In addition to MPCA optimization, this paper uses a quantitative entropy-based criterion to find the best mother wavelet and the scale values. The presented experimental results show that MPCA is capable of finding a simple ANN architecture with good generalization performance in the proposed damage localization application. The proposed method is compared with the 1-dimensional convolutional neural network (1D-CNN). A discussion about the advantages and limitations of the proposed method is presented.

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Multi-class Damage Detection and Localization Using Long Short-Term Memory (LSTM) Networks

10.31224/osf.io/ftb4d ◽

2021 ◽

Author(s):

Sandeep Sony

Keyword(s):

Damage Detection ◽

Structural Damage ◽

Short Term Memory ◽

Damage Identification ◽

Binary Classification ◽

Short Term ◽

Damage Classification ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network

In this paper, a novel method is proposed for detecting and localizing structural damage by classifying acceleration responses of a structure using a long short-term memory (LSTM) network. Windows of samples are extracted from acceleration responses in a novel data pre-processing pipeline, and an LSTM network is developed to classify the signals into multiple classes. A predicted classification of a signal by the LSTM network into one of the damage levels indicates a damage detection. Furthermore, multiple signals obtained from the vibration sensors placed on a structure are provided as input to the LSTM model, and the resulting predicted class probabilities are used to identify the locations with high probability of damage. The proposed method is validated on the experimental setup of the Qatar University Grandstand Simulator (QUGS) for binary classification, as well as, full-scale study of the Z24 bridge benchmark data for multi-class damage classification. Experiments show that the proposed LSTM-based method performs on par with 1D convolutional neural networks (1D CNN) on the QUGS dataset, and outperforms the 1D CNN on the Z24 dataset. The novelty of this study lies in the use of recurrent neural network based LSTM for vibration data for multi-class damage identification and localization.

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

International Journal of Structural Stability and Dynamics ◽

10.1142/s0219455417500687 ◽

2017 ◽

Vol 17 (07) ◽

pp. 1750068 ◽

Cited By ~ 8

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.

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Damage Identification of Unreinforced Masonry Panels Using Vibration-Based Techniques

Shock and Vibration ◽

10.1155/2017/9161025 ◽

2017 ◽

Vol 2017 ◽

pp. 1-14 ◽

Cited By ~ 2

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.

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Automatic Concrete Damage Recognition Using Multi-Level Attention Convolutional Neural Network

Materials ◽

10.3390/ma13235549 ◽

2020 ◽

Vol 13 (23) ◽

pp. 5549

Author(s):

Hyun Kyu Shin ◽

Yong Han Ahn ◽

Sang Hyo Lee ◽

Ha Young Kim

Keyword(s):

Neural Network ◽

Damage Detection ◽

Convolutional Neural Network ◽

Damage Identification ◽

Concrete Surface ◽

Superior Performance ◽

Detection Accuracy ◽

Damage Recognition ◽

Proposed Model ◽

Damage Types

There has been an increase in the deterioration of buildings and infrastructure in dense urban regions, and several defects in the structures are being exposed. To ensure the effective diagnosis of building conditions, vision-based automatic damage recognition techniques have been developed. However, conventional image processing techniques have some limitations in real-world situations owing to their manual feature extraction approach. To overcome these limitations, a convolutional neural network-based image recognition technique was adopted in this study, and a convolution-based concrete multi-damage recognition neural network (CMDnet) was developed. The image datasets consisted of 1981 types of concrete surface damages, including surface cracks, rebar exposure and delamination, as well as intact. Furthermore, it was experimentally demonstrated that the proposed model could accurately classify the damage types. The results obtained in this study reveal that the proposed model can recognize the different damage types from digital images of the surfaces of concrete structures. The trained CMDnet demonstrated a damage-detection accuracy of 98.9%. Moreover, the proposed model could be applied in automatic damage detection networks to achieve superior performance with regard to concrete surface damage detection and recognition, as well as accelerating efficient damage identification during the diagnosis of deteriorating structures used in civil engineering applications.

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A SCALABLE NEURAL-NETWORK MODULAR-ARRAY ARCHITECTURE FOR REAL-TIME MULTI-PARAMETER DAMAGE DETECTION IN PLATE STRUCTURES USING SINGLE SENSOR OUTPUT

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026812500241 ◽

2012 ◽

Vol 11 (04) ◽

pp. 1250024 ◽

Cited By ~ 4

Author(s):

SANJAY GOSWAMI ◽

PARTHA BHATTACHARYA

Keyword(s):

Neural Network ◽

Damage Detection ◽

Real Time ◽

Young’S Modulus ◽

Young's Modulus ◽

Principal Component ◽

Damage Classification ◽

Damage Scenarios ◽

Array Architecture ◽

Damage States

A scalable modular neural network array architecture has been proposed for real time damage detection in plate like structures for structural health monitoring applications. Damages in a plate like structure are simulated using finite element method of numeric system simulation. Various damage states are numerically simulated by varying Young's modulus of the material at various locations of the structure. Transient vibratory loads are applied at one end of the beam and picked at the other end by means of point sensors. The vibration signals thus obtained are then filtered and subjected to wavelet transform (WT) based multi resolution analysis (MRA) to extract features and identify them. The redundant features are removed and only the principal features are retained using principal component analysis (PCA). A large database of principal features (the feature base) corresponding to different damage scenarios is created. This feature base is used to train individual multi layer perceptron (MLP) networks to identify different parameters of the damage such as location and extent (Young's modulus). Individually trained MLP units are then organized and connected in parallel so that different damage parameters can be identified almost simultaneously, on being fed with new signal feature vectors. For a given case, damage classification success rate has been found to be encouraging. The main feature of this implementation is that it is scalable. That is, any number of trained MLP units capable of identifying a certain parameter of damages can be integrated into the architecture and theoretically it will take almost the same time to identify various damage parameters irrespective of their numbers.

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Sparse damage detection via the elastic net method using modal data

Structural Health Monitoring ◽

10.1177/14759217211021938 ◽

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.

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Wavelet Energy Accumulation Method Applied on the Rio Papaloapan Bridge for Damage Identification

Mathematics ◽

10.3390/math9040422 ◽

2021 ◽

Vol 9 (4) ◽

pp. 422

Author(s):

Jose M. Machorro-Lopez ◽

Juan P. Amezquita-Sanchez ◽

Martin Valtierra-Rodriguez ◽

Francisco J. Carrion-Viramontes ◽

Juan A. Quintana-Rodriguez ◽

...

Keyword(s):

Bridge Deck ◽

Damage Identification ◽

Area Under The Curve ◽

Economic Losses ◽

Civil Structures ◽

Vibration Signals ◽

Energy Accumulation ◽

Damage Location ◽

Wavelet Energy ◽

Accumulation Method

Large civil structures such as bridges must be permanently monitored to ensure integrity and avoid collapses due to damage resulting in devastating human fatalities and economic losses. In this article, a wavelet-based method called the Wavelet Energy Accumulation Method (WEAM) is developed in order to detect, locate and quantify damage in vehicular bridges. The WEAM consists of measuring the vibration signals on different points along the bridge while a vehicle crosses it, then those signals and the corresponding ones of the healthy bridge are subtracted and the Continuous Wavelet Transform (CWT) is applied on both, the healthy and the subtracted signals, to obtain the corresponding diagrams, which provide a clue about where the damage is located; then, the border effects must be eliminated. Finally, the Wavelet Energy (WE) is obtained by calculating the area under the curve along the selected range of scale for each point of the bridge deck. The energy of a healthy bridge is low and flat, whereas for a damaged bridge there is a WE accumulation at the damage location. The Rio Papaloapan Bridge (RPB) is considered for this research and the results obtained numerically and experimentally are very promissory to apply this method and avoid accidents.

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

Vibration ◽

10.3390/vibration4020028 ◽

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.

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

Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting ◽

10.1115/smasis2013-3008 ◽

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.

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