Identification For Structures Research Articles

Stochastic force identification for uncertain structures based on matrix equilibration and improved Tikhonov regularization method

Accurate identification and estimation of stochastic forces applied to in-service engineering structures play a vital role in structural safety assessments. This study devised an effective force power spectral density (PSD) identification method to address the challenge of identifying multipoint stationary stochastic forces in uncertain structures. Initially, a probability model was employed to characterize structural uncertainties. Subsequently, an integral relationship was established between the probability density function (PDF) of the random structural parameters and that of the stochastic force PSD. By employing a point-selection technique based on the generalized F-discrepancy and a smoothing method, the uncertainty problem was transformed into a finite number of stochastic force PSD identification problems for deterministic structures. Simultaneously, based on the inverse pseudo-excitation method, a matrix equilibration approach and an improved Tikhonov regularization method were used to address the problem of large identification errors near structural natural frequencies. In comparison to the traditional weighting matrix method, the proposed method further reduces the condition number of frequency response function matrices, thereby enhancing the accuracy of force PSD identification. Finally, numerical examples were presented to validate the effectiveness of the proposed method in solving the stochastic force identification problem.

Impact force identification for composite structures using adaptive wavelet-regularised deconvolution

Impact force identification (IFI) through deconvolution methods from measured structural responses poses a challenge due to the ill-posed nature of the inversion problem. Regularisation techniques, such as Tikhonov, sparse, and wavelet regularisation, offer potential solutions to mitigate the ill-posedness. However, determining suitable regularisation parameters is time-consuming. This study presents an adaptive wavelet-regularised time-domain deconvolution method for efficient IFI, based on wavelet transform and multi-resolution analysis. By analysing impact sensor signals using wavelets, adaptive impact windows covering the entire impact duration are generated, reducing signal length for deconvolution. Multi-resolution analysis of sensor signals enables identification of wavelet bases for the multi-resolution representation of impact force history. Regularisation is achieved by filtering out insignificant wavelet bases based on their energy contributions. Validation is performed through experiments involving small-mass hammer and large-mass drop tower impacts, comparing against various deconvolution methods. Results demonstrate the superior computational efficiency and comparable accuracy of the proposed adaptive method in IFI across different time windows. Notably, accurate force deconvolution for large-mass impacts confirms the effectiveness of the deconvolution methods for IFI, particularly when the structure undergoes significant local deformation.

Improved acoustic source localization method for crack identification in structures

Acoustic Emission (AE) monitoring is considered one of the popular non-destructive testing (NDT) methodologies that have been used to predict and identify the location of damage in critical civil infrastructure. In this paper, an improved AE crack localization method is proposed by integrating the empirical mode decomposition (EMD)-based signal decomposition method with a source localization model. Unlike the conventional AE method, the proposed method is free of the use of bulk AE parameters such as counts, rise time, signal strength, and energy. First, EMD is used to minimize the presence of noise in the recorded AE waveforms and extract the key AE components. Then, key AE events are located using the source localization model to localize the crack in concrete structures. The performance of the proposed method is validated experimentally on small and large-scale concrete beams, where the damage is induced using progressive static load testing. In particular, the large-scale beams are designed for flexural and shear mode failure to evaluate the performance of the proposed method under various types of damage. Finally, the results of the proposed method are compared with the traditional method that uses raw AE waveforms and the method that uses bandpass-filtered AE waveforms. The results show higher crack location accuracy of the proposed method than the other methods, which makes it a suitable approach as a crack localization technology.

A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

Mathematical Modelling for Failure Analysis of Composite Drive Shaft Using Modal Flexibility and Curvature Method

Early damage identification in composite structures has attracted a lot of attention in post-failure examinations in recent years. In the research, torsional fatigue test of composite drive shafts has been done and numerical methodologies have been employed for the validation for premature failure using modal flexibility and modal curvature methods. Initially, the eigenvalue and eigenvectors of healthy and fractured drive shafts are evaluated using a numerical model and verified by fatigue experimental approach. Flexibility variance and the absolute curvature difference between the drive shafts that are fractured and those that are healthy are evaluated after mass normalization. Variations in absolute modal curvatures and variations in local flexibilities that are utilized to identify the existence, location, and severity of a drive shaft fracture. The position and severity of the fracture are predicted more precisely by the modal curvature approach than by the modal flexibility approach.

Autonomous surface crack identification for concrete structures based on the you only look once version 5 algorithm

Failure to repair roads in a timely manner may shorten their life and even cause traffic accidents. Thus, accurate crack detection and reasonable classification are crucial for road safety evaluation. In this study, an improved network model based on the You Only Look Once version 5 algorithm is presented, with three additional modules: The first module improves the data processing speed by replacing the C3 module in the original network with a lightweight network model. The second module was used to lighten network weight by reusing a simple convolution structure to equivalently represent the calculation of a convolution layer as a weighted sum of several small convolution blocks. And the third module is used to improve the detection accuracy by removing the upsampling and performing three-way splicing. The proposed model can detect different types of cracks, and an extensive ablation study is reported based on various combinations of the proposed modules. Based on training on a database of 5484 images, the results show that the improved network proposed in this study can effectively identify pavement cracks. Compared with the original network, mean Average Precision is increased by 5.98%, the inference time is reduced by 4.82%, and the model weight is decreased by 17.36%. Additionally, to comply with engineering practice, comparative experiments were conducted on the pre-rotated dataset. The results showed that compared with You Only Look Once version 8, the improved algorithm improved accuracy, recall, average accuracy, and F1 score by 3.28%, 8.46%, 3.79% and 5.89%, respectively. This study can serve as an important reference for the development of crack detection methods.

Crack Recognition in Masonry Structures: CNN Models with Limited Data Sets

This research focuses on the task of Masonry Wall Crack Identification using limited data, employing state-of-the-art Convolutional Neural Network (CNN) models. The models investigated include VGG16, MobileNetV2, Xception, and DenseNet121. The dataset, consisting of 946 masonry wall images containing cracks, is used to evaluate the effectiveness of each model in this specific domain. The training set comprises 642 images, the validation set consists of 90 images, and the test set includes 214 images. The models are pretrained on large-scale datasets to extract robust features and are then fine-tuned on the masonry wall crack dataset. Among the models, DenseNet121 stands out, achieving a commendable accuracy of 85.98% in accurately identifying masonry wall cracks. This result underscores the efficacy of DenseNet121 for the challenging task of crack identification in masonry structures using limited data. This study not only contributes to the field of structural health monitoring but also emphasizes the practicality of employing CNN models for real-world applications, particularly in the critical domain of masonry crack identification.

A Markov data-based approach to system identification and output error covariance analysis for tensegrity structures

A Markov data-based approach to system identification and output error covariance analysis for tensegrity structures

Application of Machine Learning (ML)-based multi-classifications to identify corrosion fatigue cracking phenomena in Naval steel weldments

Corrosion fatigue (CF) crack detection in welded ship hull structures is quite crucial as well as challenging for ensuring their structural integrity. This is conventionally done by offline ship hull survey and inspection methods, which have large time and cost implications. Data-driven approaches of ship hull crack detection are promising in this regard. Corrosion fatigue crack growth rate (CFCGR) behaviour of shipbuilding steel was studied for its base metal (BM), fatigue-prone heat affected zones (HAZs) of Submerged Arc Welding (SAW), and shielded metal arc welding (SMAW) joints of Naval-grade steel. The results indicated the highest CFCGR for BM, intermediate for SAW, and smallest for SMAW welds. This was attributed to 44% lath martensite observed in the SMAW HAZ microstructure as compared to 33% in SAW HAZ. A novel ML-based method has been proposed for CF crack location identification by a multi-classification approach using CFCGR data for structural health monitoring (SHM) applications. Prediction of crack location (either BM, SMAW HAZ, or SAW HAZ) was investigated using K-nearest neighbour (KNN), linear support vector machine classifier (LSVC), Naïve Bayes (NB), decision tree (DT), random forest (RF), and artificial neural network (ANN) models. DT and RF models were found to perform very well even without scaling and hyper-parameter tuning operations and exhibited 98–99% classification accuracy. Scaling and tuning operations resulted in significant improvements in accuracy of KNN models from 43% to 84% (minmax scaling) and 98% (standard scaling). Significant improvement in the accuracy from 37% to 98% ANN model was achieved by standard scaling. The proposed method shall be useful in data-driven real-time crack location identification in ship hull structures.

A comparative analysis of damage identification methods based on multi-channel data fusion and convolutional neural network for frame structures

Vibration-based damage identification methods are crucial for structural health monitoring. In recent years, deep learning methods, such as convolutional neural networks (CNNs), have been widely used for damage identification due to their superior performance. To enhance the accuracy of damage identification for complex frame structures and effectively utilize data measured from multiple sensors, this study proposes and compares four different damage identification methods based on CNN and four different kinds of multi-channel data fusion approaches, i.e., data splicing, matrix reconstruction, data dimension elevation, and sub-model integration. Firstly, the original one-dimensional vibration signals of the frame structures are measured. Then, multi-channel data fusion is performed on the signals to construct datasets used as input to train the CNN model. Finally, the sensitive damage features are automatically extracted using the trained CNN models after parameter tuning, and the structural damage patterns are identified. The effectiveness of these damage identification methods is evaluated using the numerical model of IASC-ASCE Benchmark structure, the Qatar University Grandstand Simulator test, and a three-layer stainless steel frame structure test. The efficiency of damage identification methods using multi-channel data is compared to those using only single-channel data. Additionally, the impact of noise on damage identification accuracy is also analyzed. The results demonstrate that the proposed four different damage identification methods based on CNN and multi-channel data fusion, outperform the methods that solely rely on single-channel data. Furthermore, these methods significantly enhance the accuracy of damage identification.

Real-time damage identification in composite structures based on pseudo excitation (PE) approach and fiber Bragg grating (FBG) sensor arrays

At present, most of the damage detection techniques based on global vibration and local guided wave have obvious limitations, which brings difficulties to the safety assessment of structures. To solve this problem, a damage location identification method based on micro-dynamic balance and inverse finite element is proposed. Firstly, the vibration frequency, measuring point density and damage magnitude were determined by using finite simulation composite plate structure. Secondly, a fiber grating sensor is attached to the surface of the laminate to receive the strain response signal of the structure under steady-state vibration, and the global displacement of the structure is constructed by inverse finite element method. Finally, the inverse finite element listing is introduced into the theoretical framework of the dynamic response of the micro-element, and the structural damage identification experiment based on the measured strain data is realized. The experimental results show that this method can effectively identify the structural damage area and has a special sensitivity to structural damage, and the identification accuracy and efficiency are high.

A Scoping Review on Fungus and Mycotoxin Studies in the Building's Environment: Mycotoxin Analysis by Mass Spectrometry.

It has been well-established that mycotoxins are poisonous chemical metabolites secreted by certain molds. Some of them significantly affect the health of humans and livestock. Increasing attention is now being paid to uncovering and identifying mycotoxins' presence in the building's environment. However, the main challenge remains in suitable and reliable analytical methods for their identification and detection in infected structures. GC-MS and LC-MS/MS techniques have been used extensively for mycotoxin analysis, and advancement in these techniques enabled a more comprehensive range of mycotoxins to be detected. As such, this study aimed to address a brief overview of various phenomena of existing sample collection, preparation, and analysis to detect mycotoxins in the building's environment. This scoping review includes articles from 2010 to 2020 available from PubMed, Scopus, Cochrane, Wiley, Google Scholar, and ScienceDirect. Duplicate articles were removed, and exclusion criteria were applied to eliminate unrelated studies, resulting in 14 eligible articles. The present study provides an overview of mycotoxin analysis by GC-MS and LC-MS/MS in buildings. Many techniques are available for analyzing and detecting multiple mycotoxins using these methods. Future efforts would focus on rapid assays and tools enabling measuring a broader range of mycotoxins in a single matrix and lower detection limits. In addition, it would assist future findings on new techniques and mycotoxins that existed in the building's environment.

Deep learning enhanced metamodel design based on reduced mode shapes for delamination identification in composite structures

Structures requiring robust maintenance and accessibility pose challenges for structural integrity monitoring. This monitoring, crucial in various engineering applications, enables corrective actions to ensure machinery and equipment safety. Laminated composite materials, prevalent in aeronautical structures, often exhibit imperceptible delaminations or cracks, demanding the use of artificial intelligence for damage characterization. This study employs a numerical-experimental methodology, integrating artificial neural networks, to identify delaminations. Delamination, a structural damage form, was intentionally induced in epoxy-fiberglass composite structures. Modal tests captured characteristics in damaged and undamaged structures, revealing artificial intelligence effectiveness in pinpointing damage location and size. Damage significantly altered beam mode shapes, generating complex modes. Induced delaminations led to a measurable 15% reduction in natural frequencies, and damping ratios increased by approximately 10% in damaged structures. Artificial neural networks achieved an impressive 92% accuracy in predicting delamination location and size. Quantitative analysis of mode shape alterations validated the proposed methodology’s efficacy. This study provides valuable quantitative insights into damage detection in laminated composite structures, highlighting AI's potential for enhanced structural health monitoring. The numerical–experimental approach, supported by quantitative results, emphasizes the importance of advanced techniques for precise and efficient damage characterization in critical engineering applications.

A Substructure-based Model Updating Method to Damage Detection for Large-scale Structures

The health monitoring and damage detection of structures are major scientific issues that the civil engineering discipline, which made great achievements in the 20th century, has bequeathed to the 21st century. Despite the installation of health monitoring systems in many large structures, implementing damage detection through finite element model updating is often time-consuming or even infeasible due to the size of the model and the presence of numerous uncertain parameters. To address this issue, this paper proposes a substructure-based model updating method. The entire structure is divided into substructures with reduced degrees of freedom, allowing for the simplification of the motion equation by employing only a small number of low-order modes from each substructure. Consequently, the analysis scale of the structure is effectively reduced. The discarded higher modes are compensated by residual modes to ensure the accuracy of structural response and sensitivity. Subsequently, a damage identification program substructure-based model updating is developed, which is applied to component mode synthesis and damage identification for large structures. By precisely detecting damage in critical areas, accurate diagnosis and evaluation of the global structural safety are achieved. The results validate the implementation, computational efficiency, and accuracy of the proposed substructure-based model updating method. This approach shifts the focus from model updating of the entire structure to model updating of substructures, with the aim of fundamentally resolving the technical challenge of accurate damage detection of large and complex structures. Furthermore, it provides theoretical support for the practical application of damage detection in large civil structures.

Fault Identification for Shear-Type Structures Using Low-Frequency Vibration Modes

Fault Identification for Shear-Type Structures Using Low-Frequency Vibration Modes

An integrated surrogate model-driven and improved termite life cycle optimizer for damage identification in dams

Structural Damage Identification (SDI) is a crucial branch in the field of structural health monitoring, providing an essential support for the safe operation of structures. In this paper, a novel structural damage identification method based on a surrogate-assisted evolutionary optimization algorithm is proposed. This method incorporates cost-effective surrogate modelling techniques and swarm intelligence optimization algorithms with powerful global optimization capabilities. Three popular surrogate models are fused based on a weighted average strategy to construct an integrated surrogate model with stronger generalization ability and higher accuracy. In addition, the self-designed movement strategy is more suitable for the characteristics of Termite Life Cycle Optimizer (TLCO), allowing the improved TLCO to have stronger robustness and the capability to escape from local optimum. The effectiveness of the proposed method for solving the SDI problem is confirmed in a damaged dam model with different complexities. Some important findings are as follows: (i) Compared with the conventional SDI methods, which directly combines optimization algorithms and finite element models, the proposed method maintains reliable accuracy, while improving computational efficiency by a factor of more than 100. (ii) There is no direct relationship between the accuracy of the surrogate models and the effectiveness of the SDI method based on their combination with ITLCO. The proposed method can serve as a promising tool for damage identification in large-scale structures.

On using the DFOS for damage identification in civil engineering structures

AbstractThe application of distributed fiber optic sensing (DFOS) technologies to the monitoring of civil engineering structures has been widely developed in recent years. In particular, fiber optic sensing (FOS) using the Rayleigh backscattering phenomenon of the laser frequency spectrum has been used to continuously measure strain along the fiber optic sensor with high spatial resolution. However, the practical implementation of fiber optic sensorsin the field is still a challenge. These include, but are not limited to, the bonding between fiber sensor cables and the surrounding medium, the practical way in which fiber optic sensors can be installed in concrete structures, and the analysis of crack width using the strain distribution along the fiber. Compared to classical discrete sensors, the DFOS has a great potential to detect, localize and quantify crack formation even in the early stages of microcracking. This paper discusses the potential and challenges of using distributed fiber optic sensors for crack detection in concrete structures. In this sense, different types of mechanical tests on concrete structures using different fiber optic sensors, and different bonding techniques are presented to explore the possibilities of crack detection in concrete structures and its potential for practical structural health monitoring (SHM) systems of civil structures.

Damage Identification in Cement-Based Structures: A Method Based on Modal Curvatures and Continuous Wavelet Transform.

Modal analysis is an effective tool in the context of Structural Health Monitoring (SHM) since the dynamic characteristics of cement-based structures reflect the structural health status of the material itself. The authors consider increasing level load tests on concrete beams and propose a methodology for damage identification relying on the computation of modal curvatures combined with continuous wavelet transform (CWT) to highlight damage-related changes. Unlike most literature studies, in the present work, no numerical models of the undamaged structure were exploited. Moreover, the authors defined synthetic damage indices depicting the status of a structure. The results show that the I mode shape is the most sensitive to damages; indeed, considering this mode, damages cause a decrease of natural vibration frequency (up to approximately -67%), an increase of loss factor (up to approximately fivefold), and changes in the mode shapes morphology (a cuspid appears). The proposed damage indices are promising, even if the level of damage is not clearly distinguishable, probably because tests were performed after the load removal. Further investigations are needed to scale the methodology to in-field applications.

A novel criterion for crack identification in beam-like structures using distributed piezoelectric sensor and controlled moving load

The present study addresses the problem of crack identification in beam-like structures using a distributed piezoelectric sensor and controlled moving load. First, governing equations for the vibration of a cracked Timoshenko beam integrated with a piezoelectric layer and subjected to a moving load are established and their solution in the frequency domain is derived and acknowledged as electro-mechanical response of the beam. Then, variation of the electro-mechanical frequency response is examined versus crack and moving load parameters with the aim to propose a novel indicator, called herein spectral assurance criterion, for crack detection in beam-like structures. Finally, a procedure is developed for crack identification from moving load-induced frequency response measured by the distributed piezoelectric sensor based on the proposed spectral assurance criterion. The numerical analysis demonstrates that (a) the piezoelectric layer can be used as a distributed sensor for reliably measuring the mechanical response of beam subjected to moving load; (b) the speed-controlled moving harmonic force is a useful tool for exciting a beam-like structure for its health monitoring purpose and (c) the proposed spectral assurance criterion is actually a novel and efficient indicator for crack identification in beam-like structures.

Damage identification in concrete structures using a hybrid time–frequency decomposition of acoustic emission responses

Damage identification in concrete structures using a hybrid time–frequency decomposition of acoustic emission responses