Ultrasound classification of interacting flaws using finite element simulations and convolutional neural network

Niu, Sijun; Srivastava, Vipin

doi:10.1007/s00366-022-01681-y

Cited by 11 publications

(2 citation statements)

References 47 publications

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“…ML can also be used with non-destructive measurement methods to characterize internal cracks without deforming the material. For example, Niu and Srivastava [113,114] used FEMtrained CNN to accurately identify the internal crack information from ultrasound measurement (Fig. 2 (d)).…”

Section: For Fracture Mechanicsmentioning

confidence: 99%

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Jin¹,

Zhang²,

Espinosa³

2023

Preprint

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For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantification, and inverse problems. As the number of papers published in recent years in this emerging field is exploding, it is timely to conduct a comprehensive and up-to-date review of recent ML applications in experimental solid mechanics. Here, we first provide an overview of common ML algorithms and terminologies that are pertinent to this review, with emphasis placed on physics-informed and physics-based ML methods. Then, we provide thorough coverage of recent ML applications in traditional and emerging areas of experimental mechanics, including fracture mechanics, biomechanics, nano-and micro-mechanics, architected materials, and 2D material. Finally, we highlight some current challenges of applying ML to multi-modality and multi-fidelity experimental datasets and propose several future research directions. This review aims to provide valuable insights into the use of ML methods as well as a variety of examples for researchers in solid mechanics to integrate into their experiments.

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Section: For Fracture Mechanicsmentioning

confidence: 99%

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Jin¹,

Zhang²,

Espinosa³

2023

Preprint

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“…Additional memory cells in LSTMs are used to store memories from long-distance phrases. Because LSTMs may store information from past sequence inputs in the current input state, they have proven a natural option for data applications such as speech recognition, language modeling, and trial option (Niu and Srivastava, 2022). An LSTM has a hidden layer, an input layer, and an output layer (Endalie et al, 2022).…”

mentioning

confidence: 99%

Wastewater pipe defect rating model for pipe maintenance using natural language processing

Betgeri,

Vadyala,

Matthews

et al. 2023

Front. Water

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IntroductionClosed-circuit video (CCTV) inspection has been the most popular technique for visually evaluating the interior status of pipelines in recent decades. Certified inspectors prepare the pipe repair document based on the CCTV inspection. The traditional manual method of assessing structural wastewater conditions from pipe repair documents takes a long time and is prone to human mistakes. The automatic identification of necessary texts has received little attention. Computer Vision based Machine Learning models failed to estimate structural damage because they are not entirely understood and have difficulty providing high data needs. Hence, they have problems providing physically consistent findings due to their high data needs. Currently, a very small curated annotated image and video data set with well-defined, precisely labeled categories to test Computer Vision based Machine Learning models.MethodsThis study provides a valuable method to determine the pipe defect rating of the pipe repair documents by developing an automated framework using Natural Language Processing (NLP) on very small, curated annotated images, video data, and more text data. The text used in this study is broken into grammatical units using NLP technologies. The next step in the analysis entails using words to find the frequency of pipe defects and then classify them into respective defect ratings for pipe maintenance.Results and discussionsThe proposed model achieved 95.0% accuracy, 94.9% recall, 95% specificity, 95.9% precision score, and 95.7% F1 score, showing the potential of the proposed model to be used in large-scale pipe repair documents for accurate and efficient pipeline failure detection to improve the quality of the pipeline.

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A novel robust approach of 3D CNN and SAE-based near-field acoustical holography relying on self-identity constraint data for Kalman gain

Wang,

Huang,

et al. 2023

Engineering with Computers

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Ultrasound classification of interacting flaws using finite element simulations and convolutional neural network

Cited by 11 publications

References 47 publications

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Wastewater pipe defect rating model for pipe maintenance using natural language processing

A novel robust approach of 3D CNN and SAE-based near-field acoustical holography relying on self-identity constraint data for Kalman gain

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