PSI - Issue 64

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ScienceDirect Structural Integrity Procedia 00 (2023) 000 – 000 Structural Integrity Procedia 00 (2023) 000 – 000 Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Available online at www.sciencedirect.com ScienceDirect

www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia

Procedia Structural Integrity 64 (2024) 580–587

SMAR 2024 – 7th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures Damage Classification Using CNN-Based Model for Multi-Part Strengthening System Nikhil Holsamudrkar a,* , Sauvik Banerjee a a Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai – 400076, Bharat Abstract Strengthening and retrofitting systems, including FRCM (Fiber-Reinforced Cementitious Matrix), play a vital role in improving the durability and safety of structures facing challenges such as aging, increased loads, or seismic risks. To ensure the long-term integrity and performance of these structures, it's crucial to monitor the health of repaired structural components. Acoustic emission (AE) is a non-destructive and passive technique that involves the detection and analysis of stress waves emitted by various material components undergoing damage. In this particular research, the study focuses on the classification of damage in different material components of RC (Reinforced Concrete) beams strengthened with FRCM. This classification is achieved using a convolutional neural network (CNN) model that utilizes image-based waveform data from the AE testing. The study collected waveforms from four distinct failure modes: fabric-matrix debonding, fabric rupture, tensile cracking in the cementitious matrix, and yielding of steel. These waveforms were used to train the CNN model. Each waveform was converted into a Discrete Wavelet Transform (DWT) scalogram as an input into the model. The model demonstrated prediction accuracy of approximately 95% during training. The pre-trained model was further employed to classify failure mechanisms with a separate test dataset with an accuracy of approximately 87%, which is considered satisfactory considering added noise to the test dataset. © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers Keywords: TRM; FRCM; AE; CNN; DWT SMAR 2024 – 7th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures Damage Classification Using CNN-Based Model for Multi-Part Strengthening System Nikhil Holsamudrkar a,* , Sauvik Banerjee a a Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai – 400076, Bharat Abstract Strengthening and retrofitting systems, including FRCM (Fiber-Reinforced Cementitious Matrix), play a vital role in improving the durability and safety of structures facing challenges such as aging, increased loads, or seismic risks. To ensure the long-term integrity and performance of these structures, it's crucial to monitor the health of repaired structural components. Acoustic emission (AE) is a non-destructive and passive technique that involves the detection and analysis of stress waves emitted by various material components undergoing damage. In this particular research, the study focuses on the classification of damage in different material components of RC (Reinforced Concrete) beams strengthened with FRCM. This classification is achieved using a convolutional neural network (CNN) model that utilizes image-based waveform data from the AE testing. The study collected waveforms from four distinct failure modes: fabric-matrix debonding, fabric rupture, tensile cracking in the cementitious matrix, and yielding of steel. These waveforms were used to train the CNN model. Each waveform was converted into a Discrete Wavelet Transform (DWT) scalogram as an input into the model. The model demonstrated prediction accuracy of approximately 95% during training. The pre-trained model was further employed to classify failure mechanisms with a separate test dataset with an accuracy of approximately 87%, which is considered satisfactory considering added noise to the test dataset. © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers Keywords: TRM; FRCM; AE; CNN; DWT © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers

Nomenclature ML Nomenclature ML TPR

Machine Learning True Positive Ratio Machine Learning True Positive Ratio

TPR TNR TNR True Negative Ratio MCC Matthews Correlation Coefficient DOR Diagnostic Odds Ratio True Negative Ratio MCC Matthews Correlation Coefficient DOR Diagnostic Odds Ratio

2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers 2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers

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