A transfer learning SHM strategy for bridges enriched by the use of speaker recognition x-vectors

Tronci, Eleonora M.; Beigi, Homayoon; Feng, Maria Q.; Betti, Raimondo

doi:10.1007/s13349-022-00591-3

Cited by 18 publications

(8 citation statements)

References 22 publications

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“…In comparison to traditional artificial neural networks, deep learning methods are a sort of neural network that have the most layers and parameters and can learn from databases with a lot of data. In recent years, we have seen the use of deep learning methods in various parts of the SHM process [171][172][173][174][175]. But even though deep learning techniques have many benefits, there are limitations.…”

Section: Observation and Discussionmentioning

confidence: 99%

Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art

et al. 2022

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In the age of the smart city, things like the Internet of Things (IoT) and big data analytics are making big changes to the way traditional structural health monitoring (SHM) is done. Also, the capacity, flexibility, and robustness of artificial intelligence (AI) techniques for solving complex real-world problems have led to an increasing interest in applying these methods to SHM systems of infrastructures in recent years. Therefore, an analytical evaluation of recent advancements in SHM for infrastructures appears to be important. The bridge is one of the significant transportation infrastructures where existing environmental and destructive variables can have a negative impact on the structure's life and health. The SHM system for bridges in different stages of their life cycle, such as construction, development, management, and maintenance, is seen as a complementary part of intelligent transportation systems (ITS). The main goal of this study is to look at how AI can be used to improve the current state of the art in data-driven SHM systems for bridges, including conceptual frameworks, advantages, and challenges, as well as existing approaches. This article presents an overview of the role of AI in data-driven SHM systems for bridges in the future. Finally, some potential research possibilities in AI-assisted SHM are also emphasized and detailed.

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Section: Observation and Discussionmentioning

confidence: 99%

Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art

et al. 2022

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“…After training the model, new data was introduced by plugging in the values of the predictor variables into the logistic function and allowing the model to predict the probability of the event occurring for new observations because the datasets were labelled and the values of the predictors and the corresponding outcomes were known. A standard sigmoid function [35] is given by:…”

Section: Logistic Regression Classifiermentioning

confidence: 99%

Evaluation of Machine Learning Algorithms using Combined Feature Extraction Techniques for Speaker Identification

Iwok,

Udofia,

Obot

et al. 2023

JERR

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The aim of this study is to evaluate and compare machine learning algorithms when various feature extraction techniques are employed together and determine the optimal feature combinations for the models studied. The TIMIT online database was used where 5 male and 5 female non-native English speakers from five American locations were selected. Each speaker had ten 3-second utterances, totaling 500. Mel frequency cepstral coefficients (MFCC), linear predictive cepstral coefficients (LPCC), gammatone frequency cepstral coefficients (GFCC), discrete wavelet transforms (DWT) and pitch features were extracted using MATLAB and concatenated. The concatenated features were used to train and evaluate three classifier models—Random Forest (RF), Linear Discriminant Analysis (LDA), and Logistic Regression (LR)—using Python software. The results obtained showed that as the number of features combinations increased, the models’ performances improved as well. This improved performance was observed when all the cepstral features were part of the combinations. This implies that cepstral features are more robust and improve speaker identification systems. The best average score of accurate predictions of ≈ 76% for the LR model was obtained for the MGL (39 features) features combination and dropped to 70% for the highest number of feature combinations MGDLP (53 features). This indicates that more training data improves system performance, however, too much data does not translate to even better performance because the system will eventually achieve its peak performance. This information is useful for applications where limited data can present a problem.

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“…In [20], a framework is proposed to transfer knowledge obtained through synthetic data creation from earthquake simulations for various damage classes to real data with exposure limited to a single health state via a domain adversarial neural network (DANN) architecture. Again, to overcome the limitations imposed by knowledge of the goodness-of-fit class data set alone, the authors of [21] developed a vibrationbased SHM framework for damage classification in structural systems to overcome this limitation. The model is trained to acquire richness and knowledge in the learning task from a source domain with extensive and exhaustive datasets and transfer that knowledge to a target domain with much less information.…”

Section: Introductionmentioning

confidence: 99%

Framework for Identification and Prediction of Corrosion Degradation in a Steel Column through Machine Learning and Bayesian Updating

Castelli

Belleri

2023

Applied Sciences

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In recent years, structural health monitoring, starting from accelerometric data, is a method which has become widely adopted. Among the available techniques, machine learning is one of the most innovative and promising, supported by the continuously increasing computational capacity of current computers. The present work investigates the potential benefits of a framework based on supervised learning suitable for quantifying the corroded thickness of a structural system, herein uniformly applied to a reference steel column. The envisaged framework follows a hybrid approach where the training data are generated from a parametric and stochastic finite element model. The learning activity is performed by a support vector machine with Bayesian optimization of the hyperparameters, in which a penalty matrix is introduced to minimize the probability of missed alarms. Then, the estimated structural health conditions are used to update an exponential degradation model with random coefficients suitable for providing a prediction of the remaining useful life of the simulated corroded column. The results obtained show the potentiality of the proposed framework and its possible future extension for different types of damage and structural types.

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A transfer learning SHM strategy for bridges enriched by the use of speaker recognition x-vectors

Cited by 18 publications

References 22 publications

Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art

Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art

Evaluation of Machine Learning Algorithms using Combined Feature Extraction Techniques for Speaker Identification

Framework for Identification and Prediction of Corrosion Degradation in a Steel Column through Machine Learning and Bayesian Updating

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