A Compressed Sensing Approach in Structural Damage Identification

Damage localization for composite structure using guided wave signals with Gramian angular field image coding and convolutional neural networks

An updating kernel density estimation method for guided wave-based quantitative damage diagnosis under vibration conditions.

This paper first describes the importance of structural damage identification and diagnosis in civil engineering, and introduces domestic and foreign status of damage identification and diagnosis methods, and on the basis of this, it also introduces all kinds of methods for damage identification and diagnosis of civil engineering structures, and finally puts forward the development direction of civil engineering structure damage identification and diagnosis.

Composite structures are generally more susceptible to impact damage than non-composite structures, and early identification of damage is the primary goal of structural health monitoring (SHM). If such damage remains undetected or reaches a critical size, it can lead to sudden collapse and catastrophic failure. Modern SHM methods aim to preserve the integrity of composite structures through continuous inspection, monitoring, and damage assessment, including detection, localization, quantification, classification, and prognosis. These methods use sensor-based technologies to assess vibration, extension, and acoustic and thermal emission. This paper provides a review of various computational methods including physics-based methods (signal processing techniques, modal analysis, and finite element model updating) and optimization methods (inverse problems, particle swarm optimization, topology optimization, genetic algorithms, time series analysis, and hybrid techniques), alongside machine learning methodologies employing neural networks as well as deep learning for damage identification in composite structures. These computational and learning-based techniques are widely applied in the development of algorithms, optimization strategies, and hybrid frameworks for SHM. The review further summarizes the applications, advantages, and limitations of each method according to structure type and damage characteristics. The key emphasis of this review is on integrating computational approaches, as well as machine learning, to enhance the efficiency of damage identification. The conclusion is drawn based on an overview of the literature, focusing on the contributions of different computational methods and machine learning for damage identification in composites.

The in situ structural assessment by means of structural health monitoring (SHM) has received a great attention in all sorts of civil engineering applications. However, SHM implementations especially damage detections for real-world infrastructures are always overwhelmed with uncertainties of high dimensionality. A nonprobabilistic uncertainty-quantification-enhanced damage diagnosis method is proposed in this study with respect to interval analysis on SHM features. The diagonal elements of the vector auto-regressive model, constructed from the data measurements, are firstly extracted to form a vector, and this vector's Mahalanobis distance between pristine and unknown conditions is used as a damage-sensitive feature. Subsequently, the uncertainty sources, such as measurement inaccuracy and physical variability, are considered as influencing variables. A differential evolution algorithm is thereby introduced to convert the fluctuating interval of those variables into the uncertainty interval of Mahalanobis distance estimation. Finally, inspired by the idea of receiver operating characteristics when probability of detection is available, a modified mathematic metric is defined suited for interval analysis, and area under the modified receiver operating characteristics curve is employed to detect and localize damages. A contrived numerical mass-spring system and a laboratory-scale frame structure are used to validate the proposed framework; and in addition, the damage severity is able to be quantified via a proposed interval distance between pristine and inspection conditions.

Microwave Imaging offers safe, low-cost, and portable method for medical imaging applications. These advantages make the microwave imaging convenient for early detection of tumor or cancer. The transmission method is one of the methods in microwave imaging which provides fast measurement and simple image reconstruction. However, this method requires a great number of measurements to obtain a well-reconstructed image. In order to reduce the number of measurements, this research proposes a Compressive Sensing (CS) approach for image reconstruction on microwave imaging. Compressive Sensing allows reconstruction of a signal with fewer measurements than the conventional approach. In this research, the scanning process is conducted on Computer Simulation Technology (CST) Microwave Studio software. Two dipole antennas with 3 GHz frequency are utilized as microwave transmitter and receiver. A two-layer cube phantom acts as the scanned object. Each layer has different relative permittivity which illustrates the healthy cell and abnormal cell. To meet the framework of Compressive Sensing, a weighted matrix of Discrete Radon Transform (DRT) is created as a projection matrix which delineates the data acquisition scheme in the scanning process. Discrete Cosine Transform (DCT) is selected as sparse dictionary matrix to represent the sparse basis while Basis Pursuit is selected as sparse reconstruction algorithm to reconstruct the sparse signal from measurement data. The measured $S_{21}$ data are successfully reconstructed into an image using the Compressive Sensing approach. The reconstructed image is analyzed both qualitatively and quantitatively using image quality parameters such as Structural Similarity Index (SSIM) and Mean Squared Error (MSE).

Structural health monitoring (SHM) is essential for ensuring the durability and safety of steel bridges, which are considered as critical part of modern infrastructure. Ensuring the integrity of bridges is vital for maintaining their functionality and safety. Considerable research has been conducted on predicting damage location in multi-span steel bridges, while relatively limited attention has been given to the identification of damage in single-span steel bridges, and there exists a notable gap in the literature regarding the quantification of damage based on severity. This study presents a novel approach combining Modal Strain Energy-based damage indices with extensively trained data-driven Artificial Neural Networks (ANNs) to achieve highly accurate damage location and quantify this damage in terms of severity in single-span steel bridges. The research integrates a finite element (FE) model as the case study of the Alamosa Canyon Bridge in Sierra County, New Mexico. The validated FE model was used to compute damage indices based on the first two bending mode shapes. The damage index (DI) of each mode for different damage scenarios simulated on the FE model is then combined using the absolute value method to pinpoint damage locations. ANNs are employed to predict severity based on these damage indices, leveraging a comprehensive dataset generated through cubic spline interpolations and FE simulations. The DI was used as an input parameter for the ANNs to estimate damage. This methodology highlights the reduced computational effort using a streamlined method for structural health monitoring that maintains high accuracy in severity prediction and damage detection. This study enhances the infrastructure safety and maintenance strategies by offering a practical framework for steel bridges.

Purpose Aiming at the problems of high cost, high risk and traffic impact of in situ load testing of Bridges after fire, this paper proposes a fire bridge structural damage identification method based on a fire dynamics simulator (FDS)-Kriging model modification and a unit modal strain energy damage index, so as to effectively assess the damage of bridge structures after fire. Determine whether it can continue to be used and whether it affects driving safety. Design/methodology/approach Propose a fire bridge structural damage identification method to establish the FDS-Kriging model correction. Findings The finite element model of the fire bridge modified using FDS-Kriging approach can reflect the actual situation of the bridge structure more accurately, and the damage location and degree identified by the modal strain energy method are consistent with the actual bridge. Therefore, the effectiveness of the proposed method has been verified, and a new strategy for fire bridge damage identification has been provided. Research demonstrates that the finite element model of the fire-damaged bridge, modified using the FDS-Kriging method, can more accurately reflect the actual structural state. Moreover, the damage location and severity identified via the modal strain energy method align well with the actual bridge conditions. Thus, the effectiveness and engineering applicability of the proposed method are validated, offering a comprehensive new strategy and technical support for the integrated performance evaluation, damage identification and reliability analysis of fire-damaged bridges. Originality/value By determining the finite element correction interval and combining it with the Kriging model, the method realizes the fine correction of the finite element model of the fire bridge. Structural damage identification is carried out by using the unit modal strain energy damage index. This verifies the effectiveness and engineering applicability of the proposed method, which offers a novel approach and technical support for the comprehensive performance evaluation, damage identification and reliability analysis of fire-damaged bridges.

Transmissibility of non-linear output frequency response functions with application in detection and location of damage in MDOF structural systems

This paper demonstrates the merits of nested Compressive Sensing (CS) approach for Electrocardiogram (ECG) signals using discrete wavelet transform (DWT) and discrete cosine transform (DCT) as sensing matrix and sparsifying matrix. The implementation of CS framework is done using Gradient Projection for Sparse Reconstruction (GPSR). It is tested on 9 ECG signals of different arrhythmia categories obtained from MITBIH and BIDMC dataset. It is analysed for 3 different combinations, case (1) DCT sensing and DWT sparsification, case (2) DWT sensing and DCT sparsification and case (3) DWT sensing and DWT sparsification. A novel hybrid nested CS approach is proposed as case (4) which is a combination of case (2) in higher level and case (1) in lower level. This nested method yields the best PRD of 1.39 for CR = 0.2 and performs better than all proposed cases. This proposed approach involves a fair method of discarding the measurements in all frequency band and performs better than the state of the art work when the sampling rate is reduced by 4 times for ECG signal 100.dat from MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) database for CR = 0.2. It is shown that the reconstruction time of the proposed nested CS approach is lesser than non-nested CS approach.

Structural health monitoring (SHM) methods provide damage metrics and localisation, but not a means of answering subsequent questions concerning immediate or long-term damage mitigation, risk, or safety in re-occupancy. Models based on the SHM results would provide a means to test these issues, but typically require extensive human input, which is not available immediately after an event to enhance and optimise immediate decision-making. This work presents a simple, readily automated modelling approach to translate SHM results from the proven hysteresis loop analysis (HLA) method into foundation models for immediate use. Experimental data from a 3-storey structure tested at the E-Defense facility in Japan are used to assess model performance. The model’s ability to capture the essential dynamics is assessed by comparing peak dynamic displacement and cross correlation coefficient (Rcoeff). For all 6 events, 3 storeys, and 2 directions, median (5-95% Range) of peak displacement error was 0.82 (0.17, 4.09) mm, and average Rcoeff = 0.82, all of which were significantly improved if the worst event was excluded. Overall, accurate nonlinear, time-varying baseline models were created using data from SHM damage identification and localisation methods using relatively quite simple model structures. The method is readily automated via algorithm, and the models were suitable for initial investigation and analysis on safety, damage mitigation, and thus re-occupancy. Such models could take SHM from being a tool for damage identification and extend it into further decision-making, creating far greater utility for engineers and owners, which could further spur impetus for investment in monitoring.

As sensor monitoring technology continues to evolve, structural online monitoring and health management have found numerous applications across various fields. However, challenges remain concerning the real-time diagnosis of structural damage and the accuracy of dynamic reliability predictions. In this paper, a structural online damage identification and dynamic reliability prediction method based on Unscented Kalman Filter (UKF) is presented. Specifically, in the Wiener degradation process with random effects on structural performance, the structural damage identification is initially realized using UKF. Following that, the EM algorithm is employed for estimating the performance model parameters. Eventually, dynamic reliability prediction is realized based on conditional probability. The simulation results indicate that the method effectively estimates the damage state during the structure’s use while providing accurate, real-time, and dynamic reliability predictions for the system.

Steel jacket-type offshore platforms are operating in extreme environmental conditions and are prone to various damages. Thus, using structural health monitoring techniques prevents irreparable damages, long-term out of services, and losses. Several methods, such as physical and data-driven models-based, are presented for damage detection. In this paper, the experimental response of a prototype model with various damage scenarios has been measured. The prototype was excited using an eccentric-mass actuator, and dynamic responses were applied to identify damage locations using the decomposition of the signal energy approach. This approach could overcome the weaknesses of other energy methods. These methods don't demonstrate the location and intensity of damages when a wide range of damages exists in the structure. However, the DSE approach can predict all damages in a prototype with reasonable accuracy. Therefore, the DSE method can be proposed as an acceptable method for structural health monitoring for steel jacket-type offshore platforms.

This paper presents an experimental study on the detection and localization of damage in a steel plate using vibration-based methods. Dynamic impulse tests were conducted on both undamaged and damaged plate to determine changes in modal parameters, such as eigenfrequencies and mode shapes. Several damage detection methods, including the Modal Assurance Criterion (MAC), Coordinate Modal Assurance Criterion (COMAC), Mode Shape Curvature (MSC), Mode Shape Curvature Square (MSCS), and Damage Index (DI), were applied. The results showed that while MAC and COMAC effectively detected damage, they did not provide information about the location. MSC and MSCS indices demonstrated improved damage localization, with the MSCS being particularly effective. The DI index provided the clearest and most accurate representation of the damage location. These findings highlight the potential of these methods for non-destructive testing and structural health monitoring.

This paper presents an overview of current research and development in the field of sensor placement methods in structural health monitoring. Due to the limitations of equipment facilities and cost, the number of sensors to be installed in a structure is relatively few. In many cases, three are always degrees of freedoms in a structure are not easily accessible, or eventually inaccessible. Moreover, actual structures are all continuous and have essentially infinite degrees of freedoms, therefore, it is impossible to instrument on all the degrees of freedoms. The methods of sensor placement are mainly dealing with accelerometers for global dynamic testing, or structural health monitoring. Three major concerns are discussed, the necessary number of sensors to be installed, where to deploy these sensors, and how to evaluate the effectiveness of these deployed sensors. Finally, a new loading dependent sensor placement method is proposed, and verified by the experimental data of a simple cantilever beam structure.