Connected successfully! Publications | i4S Lab IIT Mandi

Research Publications

Our laboratory has published numerous research papers in prestigious international journals and conferences. Below is a list of our recent publications categorized by year.

Journal Papers

2025

Mitigating High Dimensionality in Damage Identification for Plate-like Structures through Substructuring with Interacting Filtering-Based Approaches
Journal

Authors: Shereena, O.A., Kuncham, E., Jain, P.C., Mevel, L., & Sen, S.

Journal: Engineering Structures, Vol. 323, Article 119190

View Paper
Abstract: This study addresses the challenge of high-dimensional damage identification in plate-like structures by employing a substructuring strategy combined with Interacting Particle Ensemble Kalman Filtering (IPEnKF). The proposed approach effectively reduces computational complexity and enhances damage detection accuracy, as demonstrated through numerical simulations and experimental validations.

2024

Fatigue Assessment of Bridges Using Interacting Filtering Approach with Sub-Structured Predictor Model Based on Current Health
Journal

Authors: Kuncham, E., & Sen, S.

Journal: Structural Health Monitoring, Available Online

View Paper
Abstract: The paper proposes a sub-structured predictor model integrated with an interacting filtering approach to assess fatigue in bridges, leveraging real-time health indicators.
Characterizing Damage in Wind Turbine Mooring Using a Data-Driven Predictor Model Within a Particle Filtering Estimation Framework
Journal

Authors: Kumar, R., Thakur, A., Shereena, O. A., Keprate, A., & Sen, S.

Journal: PHM Society Journal, Vol. 8, No. 1, pp. 8–8

View Paper
Abstract: This work demonstrates a data-driven prediction framework integrated with particle filtering to assess and monitor damage progression in offshore wind turbine moorings.
Enhanced High-Resolution Structural Crack Detection Using Hybrid Interacting Particle-Kalman Filter
Journal

Authors: Hoda, Md A., Kuncham, E., & Sen, S.

Journal: Structures, Vol. 62, Article 106227

View Paper
Abstract: This paper presents a hybrid Interacting Particle-Kalman Filter framework for detecting fine structural cracks with improved resolution and accuracy using image-based monitoring techniques.
Force Estimation in Bridge Substructure Boundary Under Vehicle Loading Using Interacting Filtering Approach
Journal

Authors: Kuncham, E., Hoda, Md A., & Sen, S.

Journal: Journal of Civil Structural Health Monitoring, Available Online

View Paper
Abstract: The paper introduces an interacting filtering-based method for estimating forces in bridge substructures under vehicular loads, enhancing accuracy in health monitoring of critical infrastructure.
Improved Force Density Optimization-Based Form-Finding Algorithm Mitigating the Local Instabilities
Journal

Authors: Aswal, N., & Sen, S.

Journal: Structures, Vol. 61, Article 106046

View Paper
Abstract: This study proposes an enhanced force density method for form-finding in tensile structures, addressing local instabilities in geometry under varying loading conditions.
Toward Improving Water-Energy-Food Nexus Through Dynamic Energy Management of Solar Powered Automated Irrigation System
Journal

Authors: Yadav, N., Pattabiraman, B., Tummuru, N. R., Soundharajan, B. S., Kasiviswanathan, K. S., Adeloye, A. J., Sen, S., Maurya, M., & Vijayalakshmanan, S.

Journal: Heliyon, Vol. 10, No. 4, e25359

View Paper
Abstract: This paper presents a novel dynamic energy management framework for solar-powered irrigation systems, aimed at optimizing the water-energy-food nexus in agricultural settings.
Early Age Cracking Relevant to Mass Concrete Dam Structures During the Construction Schedule
Journal

Authors: Singh, M. P., Sen, S., Pathak, H., & Dogra, A. B.

Journal: Construction and Building Materials, Vol. 411, Article 134739

View Paper
Abstract: This research investigates the causes and mitigation of early-age cracking in mass concrete used in dam structures, highlighting the influence of thermal gradients and hydration kinetics during the construction timeline.
A Probabilistic Integration of LSTM and Gaussian Process Regression for Uncertainty-Aware Reservoir Water Level Predictions
Journal

Authors: Tandon, K., & Sen, S.

Journal: Hydrological Sciences Journal, Vol. 69, No. 1, pp. 1–18

View Paper
Abstract: This paper combines Long Short-Term Memory (LSTM) networks with Gaussian Process Regression to forecast reservoir water levels, accounting for uncertainties in input data and improving reliability in hydrological applications.
Real-time Fatigue Assessment of Floating Offshore Wind Turbine Mooring Employing Sequence-to-Sequence-Based Deep Learning on Indirect Fatigue Response
Journal

Authors: Kumar, R., Sen, S., & Keprate, A.

Journal: Ocean Engineering, Vol. 315, Article 119741

View Paper
Abstract: This study presents a sequence-to-sequence deep learning model for real-time fatigue estimation of floating offshore wind turbine moorings using indirect response signals, providing a robust and low-cost monitoring alternative to conventional sensor-based approaches.

2023

Reliability Analysis of 15MW Horizontal Axis Wind Turbine Rotor Blades Using Fluid-Structure Interaction Simulation and Adaptive Kriging Model
Journal

Authors: Keprate, A., Bagalkot, N., Siddiqui, M. S., & Sen, S.

Journal: Ocean Engineering, Vol. 288, Article 116138

View Paper
Abstract: This research performs reliability analysis of a 15MW wind turbine rotor blade using coupled fluid-structure interaction simulation and adaptive kriging modeling, enabling better design decisions under uncertain loading conditions.
A Novel Genetic Algorithm Based Form-Finding Approach Towards the Improved Design of Tensegrity Utility Bridge
Journal

Authors: Kumar, S., Aswal, N., & Sen, S.

Journal: Structures, Vol. 58, Article 105401

View Paper
Abstract: This study introduces a genetic algorithm-based method for optimizing the form-finding process of tensegrity bridges, resulting in enhanced structural performance and material efficiency.
Bayesian Monitoring of Substructures Under Unknown Interface Assumption
Journal

Authors: Kuncham, E., Aswal, N., Sen, S., & Mevel, L.

Journal: Mechanical Systems and Signal Processing, Vol. 193, Article 110269

View Paper
Abstract: The paper proposes a Bayesian framework for structural health monitoring of substructures with unknown boundary conditions, enhancing accuracy in real-world applications with limited prior knowledge.
Real-time Structural Damage Assessment Using LSTM Networks: Regression and Classification Approaches
Journal

Authors: Sharma, S., & Sen, S.

Journal: Neural Computing and Applications, Vol. 35, No. 1, pp. 557–572

View Paper
Abstract: This study investigates the application of Long Short-Term Memory (LSTM) networks for real-time structural damage assessment using both regression and classification techniques. Results show that LSTM-based models outperform traditional approaches in predicting damage severity with higher accuracy and robustness.

2022

Integration of Machine Learning and Particle Filter Approaches for Forecasting Soil Moisture
Journal

Authors: Tandon, K., Sen, S., Kasiviswanathan, K. S., Soundharajan, B. S., Tummuru, N. R., & Das, A.

Journal: Stochastic Environmental Research and Risk Assessment, Vol. 36, No. 12, pp. 4235–4253

View Paper
Abstract: This study integrates machine learning with particle filtering techniques for enhanced forecasting of soil moisture dynamics.
Comparative Study on Sensitivity of Acceleration and Strain Responses for Bridge Health Monitoring
Journal

Authors: Sharma, S., Dangi, S. K., Bairwa, S. K., & Sen, S.

Journal: Journal of Structural Integrity and Maintenance, Vol. 7, No. 4, pp. 238–251

View Paper
Abstract: This paper provides a comparative analysis of acceleration and strain responses used in structural health monitoring of bridges.
Strain-based Joint Damage Estimation Approach Robust to Unknown Nonstationary Input Force
Journal

Authors: Aswal, N., Sen, S., & Mevel, L.

Journal: Structural Control and Health Monitoring, Vol. 29, No. 10, Article 2999

View Paper
Abstract: A strain-based method for joint damage estimation that remains effective despite unknown and nonstationary input forces is proposed, enhancing structural diagnostics.
Switching Kalman Filter for Damage Estimation in the Presence of Sensor Faults
Journal

Authors: Aswal, N., Sen, S., & Mevel, L.

Journal: Mechanical Systems and Signal Processing, Vol. 175, Article 109116

View Paper
Abstract: The paper develops a switching Kalman filter framework for reliable damage estimation even when sensor faults occur, improving robustness in structural health monitoring.
Estimation of Local Failure in Tensegrity Using Interacting Particle-Ensemble Kalman Filter
Journal

Authors: Aswal, N., Sen, S., & Mevel, L.

Journal: Mechanical Systems and Signal Processing, Vol. 160, Article 107824

View Paper
Abstract: This study presents a novel interacting particle-ensemble Kalman filter approach for detecting local failures in tensegrity structures, enhancing diagnostic precision.
An Online Model-Based Fatigue Life Prediction Approach Using Extended Kalman Filter
Journal

Authors: Kuncham, E., Sen, S., Kumar, P., & Pathak, H.

Journal: Theoretical and Applied Fracture Mechanics, Vol. 117, Article 103143

View Paper
Abstract: This paper proposes an online fatigue life prediction method based on extended Kalman filtering, providing improved real-time monitoring of structural components under cyclic loading.

2020

Seismic Induced Damage Detection through Parallel Estimation of Force and Parameter Estimation Using an Improved Interacting Particle-Kalman Filter
Journal

Authors: Sen, S.

Journal: IEEE Transactions on Automatic Control, Vol. 45, No. 12, pp. 2374-2378

Abstract: This paper presents an improved interacting Particle-Kalman filter for simultaneous force and parameter estimation to detect seismic damage.
Structural Health Monitoring with Non-Linear Sensor Measurements Robust to Unknown Non-Stationary Input Forcing
Journal

Authors: Sen, S., Aswal, N., Zhang, Q., & Mevel, L.

Journal: Mechanical Systems and Signal Processing, Vol. 152, Article 107472

View Paper
Abstract: This study proposes robust structural health monitoring techniques for non-linear sensor data under unknown non-stationary input conditions.
Bridge Damage Detection in Presence of Varying Temperature Using Two-Step Neural Network Approach
Journal

Authors: Sharma, S., & Sen, S.

Journal: Journal of Bridge Engineering, Vol. 26, No. 6, Article 4021027

View Paper
Abstract: This paper presents a two-step neural network approach for bridge damage detection accounting for temperature variations, improving detection reliability.
One-Dimensional Convolutional Neural Network-Based Damage Detection in Structural Joints
Journal

Authors: Sharma, S., & Sen, S.

Journal: Journal of Civil Structural Health Monitoring, Vol. 10, No. 5, pp. 1057-1072

View Paper
Abstract: This paper proposes a 1D CNN model for damage detection in structural joints, showing improved sensitivity and accuracy compared to conventional methods.

2018

Stationary Hydrological Frequency Analysis Coupled with Uncertainty Assessment under Nonstationary Scenarios
Journal

Authors: Vidrio-Sahagun, C.T., He, J., Kasivishnathan, K.S., & Sen, S.

Journal: Journal of Hydrology, Vol. 598, Article 125725

View Paper
Abstract: This paper develops a hydrological frequency analysis method integrating uncertainty quantification under nonstationary environmental conditions.
Uncertainty Qualification Using the Particle Filter for Non-Stationary Hydrological Frequency Analysis
Journal

Authors: Sen, S., He, J., & Kasivishwanathan, K.S.

Journal: Journal of Hydrology, Vol. 584, Article 124666

View Paper
Abstract: The study applies particle filtering techniques to quantify uncertainty in hydrological frequency analysis under non-stationary conditions.
Seismic-Induced Damage Detection through Parallel Force and Parameter Estimation Using an Improved Interacting Particle-Kalman Filter
Journal

Authors: Sen, S., Criniere, A., Mevel, L., Cerou, F., & Dumoulin, J.

Journal: Mechanical Systems and Signal Processing, Vol. 110, pp. 231-247

View Paper
Abstract: This paper presents a novel IPKF method for simultaneous estimation of seismic forces and structural parameters for damage detection.

2017

Non-Gaussian Parameter Estimating Using Generalized Polynomial Chaos Expansion with Extended Kalman Filtering
Journal

Authors: Sen, S., & Bhattacharya, B.

Journal: Structural Safety, Vol. 70, pp. 104-114

View Paper
Abstract: This paper presents a novel parameter estimation approach combining polynomial chaos expansion with EKF for non-Gaussian systems.
Online Structural Damage Identification Technique Using Constrained Dual Extended Kalman Filter
Journal

Authors: Sen, S., & Bhattacharya, B.

Journal: Structural Control and Health Monitoring, Vol. 24, No. 9, e1961

View Paper
Abstract: A method using constrained dual extended Kalman filtering for real-time structural damage identification.

2016

A Non-Iterative Structural Damage Identification Methodology Using State Space Eigenstructure Assignment
Journal

Authors: Sen, S., & Bhattacharya, B.

Journal: Structural and Infrastructure Engineering, Vol. 13, No. 2, pp. 211-222

View Paper
Abstract: A non-iterative damage identification method based on eigenstructure assignment in state space.
Progressive Damage Identification Using Dual Extended Kalman Filter
Journal

Authors: Sen, S., & Bhattacharya, B.

Journal: Acta Mechanica, Vol. 227, No. 8, pp. 2099-2109

View Paper
Abstract: This paper proposes a dual EKF approach to progressively identify structural damage with improved accuracy.

2015

Non-Iterative Eigenstructure Assignment Technique for Finite Element Model Updating
Journal

Authors: Sen, S., & Bhattacharya, B.

Journal: Journal of Civil Structural Health Monitoring, Vol. 5, No. 4, pp. 365-375

View Paper
Abstract: This study presents a non-iterative method for finite element model updating using eigenstructure assignment.

Conference Papers

2023

Subdomain Fault Isolation for Linear Parameter Varying Systems Through Coupled Marginalized Particle and Kitanidis Filters
Conference

Authors: Aswal, N., Kuncham, E., Sen, S., & Mevel, L.

Conference: IFAC-PapersOnLine, Vol. 56, No. 0, pp. 126–131

View Paper
Abstract: This paper presents a novel approach for subdomain fault isolation in linear parameter-varying systems using a combination of Marginalized Particle Filters and Kitanidis Filters, improving diagnostic accuracy in complex dynamic environments.

2018

Correntropy Based IPKF Filter for Parameter Estimation in Presence of Non-Stationary Noise Process
Conference

Authors: Sen, S., Criniere, A., Mevel, L., Cerou, F., & Dumoulin, J.

Conference: IFAC-PapersOnLine, Vol. 51, No. 24, pp. 420-427

View Paper
Abstract: This work introduces a correntropy-based IPKF for parameter estimation robust to non-stationary noise in dynamic systems.

2017

Estimation of Time Varying System Parameters from Ambient Response Using Improved Particle-Kalman Filter with Correlated Noise
Conference

Authors: Sen, S., Criniere, A., Mevel, L., Cerou, F., & Dumoulin, J.

Conference: EGU General Assembly Conference Abstracts, Abstract 15527

View Paper
Abstract: Improved Particle-Kalman filter technique to estimate time-varying parameters from ambient vibration data with correlated noise.

Book Chapters & Data Papers

2020

Design and Health Monitoring of Tensegrity Structures: An Overview
Book chapter

Authors: Aswal, N., & Sen, S.

Source: Reliability, Safety and Hazard Assessment for Risk-Based Technologies, pp. 523-533

DOI: 10.1007/978-981-13-9008-1_43

View Paper
Abstract: This overview reviews design and health monitoring techniques for tensegrity structures, highlighting challenges and recent advancements.

2022

Response and Input Time History Dataset and Numerical Models for a Miniaturized 3D Shear Frame Under Damaged and Undamaged Conditions
Data

Authors: Hoda, M. A., Kuncham, E., & Sen, S.

Source: Data in Brief, Vol. 45, Article 108692

DOI: 10.1016/j.dib.2022.108692

View Paper
Abstract: This dataset paper presents experimental response and input time histories along with numerical models for a miniaturized 3D shear frame structure under different damage states.