Structural Health Monitoring(SHM), Artificial Intelligence, Machine Learning, Deep Learning, FE updating, Environmental effects, Damage detection and localization
12
Scopus Publications
285
Scholar Citations
7
Scholar h-index
5
Scholar i10-index
Scopus Publications
Integrated damage detection and time-series data augmentation for floating offshore mooring systems via variational semi-supervised learning Pranjal Tamuly, Smriti Sharma, Vincenzo Nava Ocean Engineering, 2025 • A semi-supervised approach is proposed for detecting damage in mooring systems with limited labelled data. • A well-structured latent representation enhances both damage detection and data generation ability. • The performance surpasses that of other deep learning models when working with limited labelled data. • A rejection sampling technique is proposed for the generation of artificial data. The dynamics and stability of the semi-submersible offshore platforms are significantly impacted by the degradation of the mooring system. Identifying structural integrity issues in mooring systems through a data-driven approach is challenging due to the infrequency of damage events and the difficulties in recording them. To address these challenges, this study proposes the Time-Series Variational Semi-Supervised Learning (TSVSSL) framework, which effectively bridges the gap between supervised and unsupervised learning by leveraging unlabelled data for damage detection. The proposed framework features a distinctive training procedure in which the encoder-decoder and classifier components are trained concurrently. This process produces a well-clustered latent representation that enhances damage detection and supports class-specific artificial data generation. A numerical study using simulated responses of a 5 MW semi-submersible FOWT under varying metocean conditions demonstrated that the proposed framework outperformed existing deep learning methods in damage detection, achieving superior accuracy, precision, recall, and F1 score. Further, a rejection sampling technique is also introduced to effectively generate artificial data that closely aligns with actual time series displacement response. The novelty of the proposed framework lies in its dual focus on damage detection and artificial data generation marking a significant advancement in the data-driven assessment of mooring systems.
Monitoring Mooring Lines of Floating Offshore Wind Turbines: Autoregressive Coefficients and Stacked Auto-Associative-Deep Neural Networks Smriti Sharma, Vincenzo Nava 20th International Conference on Condition Monitoring and Asset Management CM 2024, 2024 This study introduces a pioneering monitoring system designed to mitigate operational costs and enhance the sustainability of Floating Offshore Wind Turbines (FOWT). The proposed framework combines Autoregressive models with a Stacked Auto-Associativebased Deep Neural Network (AANN-DNN) to detect and classify damages in mooring systems of FOWTs. By extracting damage-sensitive features (DSFs) using the AR models from time-series data and employing unsupervised learning in the auto-associative neural network, followed by supervised training with DNN, the approach demonstrates exceptional accuracy in damage identification and classification. Numerical simulations conducted using NREL's OpenFAST software under diverse metocean conditions validate the method's efficacy, offering a promising solution for efficient FOWT mooring line monitoring.
MONITORING MOORING (MONIMOOR) LINES OF FLOATING STRUCTURES USING DEEP LEARNING-BASED APPROACHES Compdyn Proceedings, 2023
Comparative study on sensitivity of acceleration and strain responses for bridge health monitoring Smriti Sharma, Sunil Kumar Dangi, Shivam Kumar Bairwa, Subhamoy Sen Journal of Structural Integrity and Maintenance, 2022 Bridge health monitoring has been attempted to ensure the safety of the bridges in their operations, employing various measurement options like acceleration, strain, displacement, etc. The relative efficacy of these measurements as a damage-sensitive response has remained a topic of research. While acceleration has traditionally been used in abundance, dynamic strain, being relatively cheaper to record, also holds the potential to replace acceleration. This study undertakes a comparative investigation weighing the relative benefits of both the measurement options for prompt and reliable damage detection in both the time and frequency domain. The comparison is drawn in the light of damage sensitivity, intensity and consistency of the damage signature of the adopted measurement type while keeping the damage and loading specifications unaltered. A multi-span concrete box girder has been replicated with a high-fidelity numerical model as a proxy for the real structure followed by an experimental validation on a propped cantilever beam. Acceleration and strain responses are measured and analyzed for different damage conditions. A rigorous sensitivity analysis is undertaken to compare explicitly the performance of both the measurement options. The results demonstrated superior performance with the strain response in time and frequency domains from consistency and intensity perspectives.
Damage detection in presence of varying temperature through residual error modelling approach with dual neural network 9th European Workshop on Structural Health Monitoring Ewshm 2018, 2018
RECENT SCHOLAR PUBLICATIONS
Integrating DL-based surrogate within an Interacting Particle Ensemble Kalman Filtering framework for computationally efficient condition monitoring of FOWT moorings SS Ananay Thakur a , Rohit Kumar a , O.A. Shereena a , Smriti Sharma b ... Ocean Engineering 330 (121223) , 2025 2025.0 Citations: 4
Integrated damage detection and time-series data augmentation for floating offshore mooring systems via variational semi-supervised learning P Tamuly, S Smriti, N Vincenzo Ocean Engineering 330 (121199) , 2025 2025.0 Citations: 7
Condition monitoring of mooring systems for Floating Offshore Wind Turbines using Convolutional Neural Network framework coupled with Autoregressive coefficients S Smriti, N Vincenzo Ocean Engineering 302 (117650) , 2024 2024.0 Citations: 40
Monitoring Mooring Lines of Floating Offshore Wind Turbines: Autoregressive Coefficients and Stacked Auto-Associative-Deep Neural Networks S Sharma, V Nava 20th International Conference on Condition Monitoring and Asset Management … , 2024 2024.0
Monitoring mooring (monimoor) lines of floating structures using deep learning-based approaches S Sharma, V Nava, N Gorostidi 2023.0 Citations: 1
MONITORING MOORING (MONIMOOR) LINES OF FLOATING STRUCTURES USING DEEP LEARNING-BASED APPROACHES S Sharma, V Nava, N Gorostidi 9th ECCOMAS Thematic Conference on Computational Methods in Structural … , 2023 2023.0
Real-time structural damage assessment using LSTM networks: regression and classification approaches S Sharma, S Sen Neural Computing and Applications 35 (1), 557-572 , 2023 2023.0 Citations: 77
Comparative study on sensitivity of acceleration and strain responses for bridge health monitoring S Sharma, SK Dangi, SK Bairwa, S Sen Journal of Structural Integrity and Maintenance 7 (4), 238-251 , 2022 2022.0 Citations: 18
Bridge health monitoring using data-driven algorithms: LSTM regression and classification approaches S Sharma, S Sen European Workshop on Structural Health Monitoring(EWSHM) 2022 , 2022 2022.0
Structural damage detection in presence of temperature variability using 2D CNN integrated with EMD S Sharma, S Sen Structural Monitoring and Maintenance 8 (4), 379-402 , 2021 2021.0 Citations: 4
Bridge damage detection in presence of varying temperature using two-step neural network approach S Sharma, S Sen Journal of Bridge Engineering 26 (6), 04021027 , 2021 2021.0 Citations: 38
Damage detection in presence of varying temperature using mode shape and a two-step neural network S Sharma, S Sen Recent Advances in Computational Mechanics and Simulations: Volume-I … , 2020 2020.0 Citations: 7
One-dimensional convolutional neural network-based damage detection in structural joints S Sharma, S Sen Journal of Civil Structural Health Monitoring 10 (5), 1057-1072 , 2020 2020.0 Citations: 87
Dynamic strain measurements based structural joint damage estimation using 1D Convolution Neural Network S Sharma, S Sen The Sixteenth International Conference on Civil, Structural & Environmental … , 2019 2019.0
Damage detection in presence of varying temperature through residual error modelling approach with dual neural network S Sharma, S Sen 9th European Workshop on Structural Health Monitoring, EWSHM 2018, December , 2018 2018.0 Citations: 2
PLATE DAMAGE DETECTION UNDER VARYING TEMPERATURE USING DUAL NEURAL NETWORK S Sharma, S Sen
Dynamic strain measurements based structural joint damage S Sharma, S Sen neural networks 4 (3), 93-101 , 0
MOST CITED SCHOLAR PUBLICATIONS
One-dimensional convolutional neural network-based damage detection in structural joints S Sharma, S Sen Journal of Civil Structural Health Monitoring 10 (5), 1057-1072 , 2020 2020.0 Citations: 87
Real-time structural damage assessment using LSTM networks: regression and classification approaches S Sharma, S Sen Neural Computing and Applications 35 (1), 557-572 , 2023 2023.0 Citations: 77
Condition monitoring of mooring systems for Floating Offshore Wind Turbines using Convolutional Neural Network framework coupled with Autoregressive coefficients S Smriti, N Vincenzo Ocean Engineering 302 (117650) , 2024 2024.0 Citations: 40
Bridge damage detection in presence of varying temperature using two-step neural network approach S Sharma, S Sen Journal of Bridge Engineering 26 (6), 04021027 , 2021 2021.0 Citations: 38
Comparative study on sensitivity of acceleration and strain responses for bridge health monitoring S Sharma, SK Dangi, SK Bairwa, S Sen Journal of Structural Integrity and Maintenance 7 (4), 238-251 , 2022 2022.0 Citations: 18
Integrated damage detection and time-series data augmentation for floating offshore mooring systems via variational semi-supervised learning P Tamuly, S Smriti, N Vincenzo Ocean Engineering 330 (121199) , 2025 2025.0 Citations: 7
Damage detection in presence of varying temperature using mode shape and a two-step neural network S Sharma, S Sen Recent Advances in Computational Mechanics and Simulations: Volume-I … , 2020 2020.0 Citations: 7
Integrating DL-based surrogate within an Interacting Particle Ensemble Kalman Filtering framework for computationally efficient condition monitoring of FOWT moorings SS Ananay Thakur a , Rohit Kumar a , O.A. Shereena a , Smriti Sharma b ... Ocean Engineering 330 (121223) , 2025 2025.0 Citations: 4
Structural damage detection in presence of temperature variability using 2D CNN integrated with EMD S Sharma, S Sen Structural Monitoring and Maintenance 8 (4), 379-402 , 2021 2021.0 Citations: 4
Damage detection in presence of varying temperature through residual error modelling approach with dual neural network S Sharma, S Sen 9th European Workshop on Structural Health Monitoring, EWSHM 2018, December , 2018 2018.0 Citations: 2
Monitoring mooring (monimoor) lines of floating structures using deep learning-based approaches S Sharma, V Nava, N Gorostidi 2023.0 Citations: 1
Monitoring Mooring Lines of Floating Offshore Wind Turbines: Autoregressive Coefficients and Stacked Auto-Associative-Deep Neural Networks S Sharma, V Nava 20th International Conference on Condition Monitoring and Asset Management … , 2024 2024.0
MONITORING MOORING (MONIMOOR) LINES OF FLOATING STRUCTURES USING DEEP LEARNING-BASED APPROACHES S Sharma, V Nava, N Gorostidi 9th ECCOMAS Thematic Conference on Computational Methods in Structural … , 2023 2023.0
Bridge health monitoring using data-driven algorithms: LSTM regression and classification approaches S Sharma, S Sen European Workshop on Structural Health Monitoring(EWSHM) 2022 , 2022 2022.0
Dynamic strain measurements based structural joint damage estimation using 1D Convolution Neural Network S Sharma, S Sen The Sixteenth International Conference on Civil, Structural & Environmental … , 2019 2019.0
PLATE DAMAGE DETECTION UNDER VARYING TEMPERATURE USING DUAL NEURAL NETWORK S Sharma, S Sen
Dynamic strain measurements based structural joint damage S Sharma, S Sen neural networks 4 (3), 93-101 , 0
