Fusion and Visualization of Bridge Deck Nondestructive Evaluation Data via Machine Learning

Mohamadi, Sara; Lattanzi, David; Azari, Hoda

doi:10.3389/fmats.2020.576918

Cited by 15 publications

(5 citation statements)

References 75 publications

(80 reference statements)

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“…Machine learning was utilized to enhance the interpretation of the ER and HCP results by using data from the three NDE methods. Machine learning has been extensively utilized in various applications of structural assessments by NDEs, as reported in several research studies such as [22][23][24][25]. Artificial intelligence (AI) has also been employed for concrete evaluations in NDEs for various technologies, including impact echo [22,25], half-cell potential [26], and electrical resistivity [27].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Integrating Data from Multiple Nondestructive Evaluation Technologies Using Machine Learning Algorithms for the Enhanced Assessment of a Concrete Bridge Deck

Khudhair,

Gucunski

2023

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Several factors impact the durability of concrete bridge decks, including traffic loads, fatigue, temperature changes, environmental stress, and maintenance activities. Detecting problems such as corrosion, delamination, or concrete degradation early on can lower maintenance costs. Nondestructive evaluation (NDE) techniques can detect these issues at early stages. Each NDE method, meanwhile, has limitations that reduce the accuracy of the assessment. In this study, multiple NDE technologies were combined with machine learning algorithms to improve the interpretation of half-cell potential (HCP) and electrical resistivity (ER) measurements. A parametric study was performed to analyze the influence of five parameters on HCP and ER measurements, such as the degree of saturation, corrosion length, delamination depth, concrete cover, and moisture condition of delamination. The results were obtained through finite element simulations and used to build two machine learning algorithms, a classification algorithm and a regression algorithm, based on Random Forest methodology. The algorithms were tested using data collected from a bridge deck in the BEAST® facility. Both machine learning algorithms were effective in improving the interpretation of the ER and HCP measurements using data from multiple NDE technologies.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Integrating Data from Multiple Nondestructive Evaluation Technologies Using Machine Learning Algorithms for the Enhanced Assessment of a Concrete Bridge Deck

Khudhair,

Gucunski

2023

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“…The method achieved an optimal recognition accuracy of 88%, and experimental results demonstrated that the CNN model outperformed the traditional peak frequency method in imaging internal defects in concrete slabs. Mohamadi et al 10 utilized support vector machine (SVM) to randomly select salient features of the signal, which were then used to identify unlabeled signals. While these methods significantly improve the accuracy of defect recognition, the designed network structures need a large amount of labeled training data, which are not necessarily available in practice.…”

Section: Introductionmentioning

confidence: 99%

Visualization of concrete internal defects based on unsupervised domain adaptation algorithm for transfer learning of experiment-simulation hybrid dataset of impact echo signals

Shang,

Chen

2023

Structural Health Monitoring

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Detecting concrete internal defects through deep learning analysis of impact echo signals faces two challenges: (1) the traditional signal processing method such as wavelet transform (WT) fails to reflect data-sensitive damage characteristics due to the uncertainty principle and (2) the limited labeled data acquired from real structures impedes network training. To address the first challenge, this paper proposes the WT-based synchrosqueezing transform (WT-SST) for the conversion of time-series data to the time-frequency spectrogram, which can provide effective features for the network in time and frequency domains simultaneously. To overcome the second challenge, numerical simulation data are supplemented for the augment of labeled data. To minimize the effect of data variance between experiments and simulations, this paper uses an unsupervised domain adaptation (DA) network for the transfer training of labeled simulation data (original domain) and unlabeled experimental data (target domain). The DA network extracts domain-invariant features by maximizing the domain recognition error and minimizing the probability distribution distance. The damage probability is calculated by the trained model to produce a 2D defect contour image of concrete specimens, and the three-dimensional visualization of internal defects by estimating the defect depth based on the defect area of contour image. Finally, the recognition precision, recall, F1-score, and accuracy of the model of unsupervised DA network trained by a hybrid dataset reaches 89.4%, 88.4%, 88.9%, and 94.7%, respectively.

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“…ML has been adopted in NDT over the past decade, and progress has been achieved and demonstrated in particular studies [17], [18], [19] The applications target the holistic assessment of structures in terms of data fusion concepts [20] and the analysis of specific inspection techniques [21] With a wide range of studies conducted, there is still an urgent need for development regarding applying ML to NDT of concrete structures and, more specifically, NDT methods using elastic waves. With powerful algorithms available, ML models can be tailored to specific applications.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applications in Nondestructive Testing of Concrete Structures

Algernon,

Münch,

Muller

et al. 2023

ce papers

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Machine Learning bears great potential for data‐driven solutions in the field of nondestructive testing (NDT) of concrete structures. The analysis of the data collected with NDT methods, such as ultrasonics, impact‐echo and ground penetrating radar, can be complex and requires experience. The expected benefit of Machine Learning applications in this context goes beyond the increase of efficiency obtained by automating the analysis process. While traditional analysis approaches are usually solely based on key features according to the basic principles, Machine Learning algorithms can consider the full data content and reveal hidden correlations. For an organized approach to Machine Learning on NDT data, an analysis tool has been developed. The tool provides a graphical user interface to manage and label training/test data and interactively define the Deep Neural Network architecture. In particular, Convolutional Neural Networks, as proven successful in various image recognition tasks, are implemented. The Machine Learning concepts are demonstrated in show cases, comprising ultrasonic as well as impact‐echo applications. In particular, the relevance of a targeted preprocessing is addressed, comparing the effectiveness of time‐, frequency‐ and joint‐time‐frequency‐representations.

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Fusion and Visualization of Bridge Deck Nondestructive Evaluation Data via Machine Learning

Cited by 15 publications

References 75 publications

Integrating Data from Multiple Nondestructive Evaluation Technologies Using Machine Learning Algorithms for the Enhanced Assessment of a Concrete Bridge Deck

Integrating Data from Multiple Nondestructive Evaluation Technologies Using Machine Learning Algorithms for the Enhanced Assessment of a Concrete Bridge Deck

Visualization of concrete internal defects based on unsupervised domain adaptation algorithm for transfer learning of experiment-simulation hybrid dataset of impact echo signals

Machine Learning Applications in Nondestructive Testing of Concrete Structures

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