Smriti Sharma

@i4siitmandi.com

Research Scholar
Indian Institute of Technology Mandi



                    

https://researchid.co/smritid17007

EDUCATION

Indian Institute of Technology Mandi

RESEARCH INTERESTS

Structural Health Monitoring(SHM), Artificial Intelligence, Machine Learning, Deep Learning, FE updating, Environmental effects, Damage detection and localization

10

Scopus Publications

168

Scholar Citations

5

Scholar h-index

5

Scholar i10-index

Scopus Publications


  • Evaluation of CNN Models for Alzheimer's Classification Using MRI Images
    Smriti Sharma and Himanshu Mittal
    IEEE
    Alzheimer's Disease poses a significant global health burden as pervasive neurodegenerative affliction, underscoring the critical need for timely and accurate diagnosis to optimize patient care and implement effective therapeutic interventions. This research delves into the application of deep learning, a subset of artificial intelligence, as a potent tool for automating AD classification using medical imaging data, particularly MRI. Utilizing convolutional neural networks (CNNs), Inception Net, ResNet50, and VGG16, our study focuses on classifying distinct AD stages. By training these models on extensive medical imaging datasets, intricate patterns and features are extracted from MRI images, enabling robust discrimination between individuals at varying AD stages. Beyond technical exploration, this research underscores the significance of integrating cutting-edge technology, offering a promising avenue to improve early-stage AD detection precision, contributing to enhanced patient outcomes and more effective therapeutic strategies.

  • MONITORING MOORING (MONIMOOR) LINES OF FLOATING STRUCTURES USING DEEP LEARNING-BASED APPROACHES


  • Real-time structural damage assessment using LSTM networks: regression and classification approaches
    Smriti Sharma and Subhamoy Sen
    Springer Science and Business Media LLC

  • Comparative study on sensitivity of acceleration and strain responses for bridge health monitoring
    Smriti Sharma, Sunil Kumar Dangi, Shivam Kumar Bairwa, and Subhamoy Sen
    Informa UK Limited
    ABSTRACT 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.

  • Bridge Damage Detection in Presence of Varying Temperature Using Two-Step Neural Network Approach
    Smriti Sharma and Subhamoy Sen

    Abstract The dynamic properties of bridges can be affected not only through damage but also from ambient uncertainty. False-positive or negative alarms may be raised if environmental effects are no...



  • One-dimensional convolutional neural network-based damage detection in structural joints
    Smriti Sharma and Subhamoy Sen
    Springer Science and Business Media LLC
    Structural health monitoring research traditionally focuses on detecting damage in members excluding the possibility of weakened joint conditions. Efficient model-based joint damage detection algorithms demand computationally expensive model that may affect the promptness of detection. Deep learning techniques have recently come up as efficient alternative to this cause. These techniques help in predicting occurrence and location of damage in structures based on some automatically identified features embedded in the measured structural response. This article proposes an output-only approach for joint damage detection in which a 1D-convolutional neural network (CNN) has been introduced to locate weakened joints in semi-rigid frames. CNN architecture merges feature extraction and classification simultaneously within a single learning block to automatically extract abstract features from typically 2D/3D signals. Proposed approach further modifies the usual CNN architecture to enable it to handle 1D response signals. Numerical validation is performed on a 2D-steel frame under different damage locations and severities followed by experimental validation on a steel frame structure. The method is observed to be very precise and prompt in detecting single as well as multiple damage scenarios. False alarm sensitivity of the proposed algorithm is also tested and found to be well within acceptable limits.

  • Damage detection in presence of varying temperature through residual error modelling approach with dual neural network


RECENT SCHOLAR PUBLICATIONS

  • 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

  • MONITORING MOORING (MONIMOOR) LINES OF FLOATING STRUCTURES USING DEEP LEARNING-BASED APPROACHES
    S Sharma, V Nava, N Gorostidi
     2023

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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 2021

  • 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

  • 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

  • Damage detection in presence of varying temperature through residual error modelling approach with dual neural network
    S Sharma, S Sen
     2018

  • 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

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
    Citations: 72

  • 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
    Citations: 33

  • 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
    Citations: 29

  • 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
    Citations: 14

  • 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
    Citations: 10

  • 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 2021
    Citations: 5

  • 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
    Citations: 3

  • Damage detection in presence of varying temperature through residual error modelling approach with dual neural network
    S Sharma, S Sen
     2018
    Citations: 2