Seismic Damage Identification for Bridges Based on Transmissibility Function and Support Vector Machine

Research on sampling rate selection of sensors in offshore platform shm based on vibration

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.

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.

Background: Structural damage identification is a very important subject in the field of civil, mechanical and aerospace engineering according to recent patents. Optimal sensor placement is one of the key problems to be solved in structural damage identification. Methods: This paper presents a simple and convenient algorithm for optimizing sensor locations for structural damage identification. Unlike other algorithms found in the published papers, the optimization procedure of sensor placement is divided into two stages. The first stage is to determine the key parts in the whole structure by their contribution to the global flexibility perturbation. The second stage is to place sensors on the nodes associated with those key parts for monitoring possible damage more efficiently. With the sensor locations determined by the proposed optimization process, structural damage can be readily identified by using the incomplete modes yielded from these optimized sensor measurements. In addition, an Improved Ridge Estimate (IRE) technique is proposed in this study to effectively resist the data errors due to modal truncation and measurement noise. Two truss structures and a frame structure are used as examples to demonstrate the feasibility and efficiency of the presented algorithm. Results: From the numerical results, structural damages can be successfully detected by the proposed method using the partial modes yielded by the optimal measurement with 5% noise level. Conclusion: It has been shown that the proposed method is simple to implement and effective for structural damage identification.

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.

According to statistics from the Global Burden of Cancer Study (Globocon) of the World Health Organization (WHO), cancer, particularly breast cancer, is a severe health issue in Indonesia with 68,858 new cases and 22,000 deaths recorded in 2020. Ultrasonography (USG) technology is acknowledged as one of the potentials to support early detection, which is vital in reducing mortality from breast cancer. This study focuses on classifying ultrasound images using the Support Vector Machine (SVM) algorithm, GLCM feature extraction, Min-Max normalization, and Mutual Information with SelectKBest Feature Selection. From several experiments using the SVM algorithm with various combinations of parameter values that have been set and different Tests, namely using a Train/Test Split with a proportion of 80/20 and K-Fold Cross Validation, it shows that the SVM algorithm is capable of classifying ultrasound images of breast cancer. into two categories (Benign Tumor and Malignant Tumor) with the same maximum accuracy of 79% after applying the SMOTE Balancing Data technique or without using the Balancing Data technique. As a result, the Support Vector Machine (SVM) algorithm has the potential to be an effective model for identifying breast cancer ultrasound images, both on data from the original set that has not been balanced and data from the set that has been balanced.

In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality of the obtained experimental data. Finally, the fault diagnosis algorithms were designed by support vector machine (SVM) and BP neural network to identify and prevent the reformer carbon deposition and heat exchanger rupture faults, respectively. The research results show that both SVM and BP fault diagnosis algorithms can achieve online fault identification. The PCA + SVM algorithm was compared with the SVM algorithm, BP algorithm, and PCA + BP algorithm, and the results show that the PCA + SVM algorithm is superior in terms of running time and accuracy, the diagnosis accuracy reached more than 99%, and the running time was within 20 s. The corresponding system optimization scheme is also proposed.

Dilihat dari sisi konsumen, ada berbagai aspek yang mempengaruhi konsumen untuk membeli suatu kendaraan bermotor. Hal ini karena masing-masing orang memiliki minat dan kebutuhan yang berbeda. Oleh karena itu, tujuan dari penelitian ini adalah untuk mengetahui kelompok umur dan profesi apa saja yang menjadi pelanggan potensial di SPS Motor Honda Lombok Timur. Metode penelitian yang digunakan adalah metode analitik deskriptif, dimana peneliti mengambil data-data penjualan sepeda motor dari SPS Motor Honda Lombok Timur, yang selanjutnya diolah dan dianalisis menggunakan software Rapidminer 5. Hasil analisis data tersebut selanjutnya dideskripsikan dengan detail mengenai kelompok pelanggan potensial. Berdasarkan analisis yang telah dilakukan, dapat ditarik kesimpulan bahwa kelompok yang termasuk dalam pelanggan potensial dari SPS Motor Honda Lombok Timur adalah kelompok dengan kriteria umur berkisar 33-46 tahun yang berprofesi sebagai wiraswasta dan petani, Hal ini disebabkan karena, pelanggan tersebut memiliki penghasilan dengan usia produktif. Sedangkan kelompok pelanggan yang tidak potensial ada 2 irisan yaitu yang pertama apabila kategori usia produktif tapi tidak memiliki penghasilan dengan rentang umur 17-32 tahun dengan profesi pelajar dan wiraswasta. Kedua yaitu, apabila kategori usia tidak produktif dengan rentang usia 47-73 tahun dan tidak memiliki penghasilan dengan profesi petani dan wiraswata.DOI : 10.29408/jit.v2i2.1447

Structural damage identification using strain mode differences by the iFEM based on the convolutional neural network (CNN)

In order to improve the accuracy of the prediction model of lung tuberculosis, a prediction model of lung tuberculosis based on grayscale mixed nucleus AR-SVM model is proposed in the Thesis. First, early warning model of the incidence trend of lung tuberculosis was constructed by using support vector machine (SVM) algorithm which includes two circumstances. One is that there is no extreme risk preference; the other is that there is extreme risk preference. And the searching was conducted in the optimum sorting process in training set based on SVM algorithm; Secondly, aimed at the problem that early warning of extreme risk preference of SVM model is easily out of order in the prediction of incidence trend of lung tuberculosis, data record of incidence trend of lung tuberculosis was preprocessed by using improved SVM model and SVM algorithm was improved by polynomial kernel SVM, which achieved improvement of prediction performance of autoregression model of sample data; finally, the effectiveness of algorithm is verified by positive analysis.

In this day and age, Structural Health Monitoring (SHM) has become an indispensable tool to determine the safety and normal operation of structures in service. Among numerous SHM methods, piezoceramic transducers have been widely utilized (Liao et al. 2011, Yan et al. 2009). The focus of recent researches has generally been on damage identification and evaluation of concrete structures (Song et al. 2007, Moslehy et al. 2010, Howser et al. 2011). Some piezoelectric-based monitoring methods have also been proposed to monitor early age concrete strength, water seepage depth (Liu et al. 2013, Zou et al. 2014), and damage caused by cyclic combined loadings or seismic excitations (Gu et al. 2010). However, it should be concerned that there are a great deal of influencing factors existing in practical monitoring, like temperature, boundary, and loading conditions, which may bring in significant effects during the monitoring process (Zou et al. 2015). Specifically, the construction load has not been taken into consideration during monitoring process. Especially for the important columns downstairs, there are a lot of load increments in the construction process of a high-rise building (Du et al. 2015). The question whether the construction load brings in negligible impact on the stability of smart aggregate is not fully discussed.

This paper proposes a novel method for structural damage identification that integrates the power spectral density (PSD) of structural acceleration responses with densely connected convolutional networks (DenseNet). The method transforms the training object of the DenseNet into a numerical matrix (PSD matrix) for structural damage identification. Leveraging transfer learning, the DenseNet models are initially trained on simulated data and further fine‐tuned using experimental data to enhance robustness and generalization. Results demonstrate that frequency‐domain signals processed by PSD significantly enhance model performance, achieving lower mean squared error (MSE), higher Pearson’s correlation coefficient ( R value), and reduced mean absolute error (MAE) compared to time‐domain signals. The effectiveness of this method was verified on a six‐story frame structure. This study underscores the efficacy of transfer learning in bridging the gap between simulated and real‐world data, thereby facilitating effective structural health monitoring and damage identification.

Structural nonlinear damage identification based on Bayesian optimization GNAR/GARCH model and its experimental study

As newer and more reliable ways of construction were developed, civilization began to spread out further and retain functional infrastructure for longer periods of time.[...]

One of the challenges in structural health monitoring (SHM) is the power required for sensors to collect and communicate data. Self-powered sensors are able to harvest power by from their environment, i.e., strain and vibration of the host structure. However, the harvested power with current technology is still limited and improving the system’s efficiency requires reducing the power budget. A way to minimize the communication power demand is to transmit the minimum amount of information, namely one bit. The binary signal can be generated at a sensor node according to a local rule based on physical measurements, but interpretation at the global level requires dealing with discrete binary (1 or 0) data, which implies system information with reduced resolution. This study presents an investigation on approaches for the interpretation of such kind of binary data for use in structural assessment and damage identification. Pattern recognition (PR) methods based on image data analysis were adapted for the study. The methods used were classifiers based on deviation of patterns with respect to each other, two-dimensional principal component analysis, and two-dimensional linear discriminant analysis. The PR methods and the performance of the interpretation algorithms were evaluated by using virtual data from finite element simulations on an aluminum plate. The ability for each of the PR methods to identify service demands, load variations and localized material degradation was evaluated. Results indicate that PR methods can be used as damage identification algorithms for binary data sets in novel wireless self-powered sensor networks.