Gibbs Sampler-Based Probabilistic Damage Detection of Structures Using Reduced Order Model Ayan Das, Nirmalendu Debnath International Journal of Structural Stability and Dynamics, 2023 Vibration-based global damage detection based on updating of finite element (FE) model by targeting the modal measurements is a significant area of interest in structural health monitoring (SHM). In a typical modal testing setup, the measured mode shapes have missing components against various degrees of freedom (DOFs) due to the limitation in the number of sensors available. In this context, a novel Gibbs sampling approach is proposed for updating of FE model incorporating model reduction (MR) to facilitate the global-level detection of structural damages from incomplete modal measurements. In addition to the ease with similar sizes of analytical and experimental mode shapes, the proposed Gibbs sampling approach (for updating the reduced order FE model in the Bayesian framework) has some important advantages like: (A) no need for consideration of system mode shapes as parameters (unlike needed in the typical Gibbs sampling approach) thereby having a significant reduction in the number of parameters, (B) non-requirement of mode matching with consequent reduction in computation time to a significant extent. A generalized formulation is presented in this work providing the scope for incorporating measurements from multiple sensor setups. Moreover, formulations are adapted to incorporate multiple sets of data/measurements from each setup targeting the epistemic uncertainty. Finally, validation is carried out with both numerical (truss structure and building structure) and experimental (laboratory building structure) exercises in comparison with the typical Gibbs sampling approach having a full-sized model. The proposed approach is observed to be evolved as a computationally efficient technique with satisfactory performance in FE model updating and global damage detection.
A multi-objective framework for finite element model updating using incomplete modal measurements Nirmalendu Debnath, Ayan Das Structural Control and Health Monitoring, 2021 Finite element (FE) model updating in multi-objective framework helps for better understanding of overall performance in updating (under various variations of weightages assigned to basic components of the objective function) along with providing scope for better judgmental selection. A FE model updating in multi-objective framework is proposed with no requirement of repeated eigen-solution along with avoiding repeated possibilities of incurring mode-pairing error (by adopting an existing framework of system mode shape). Two multi-objective optimization techniques are adopted: (a) weighted sum and (b) adaptive weighted sum methods. Moreover, a possible single best solution out of the Pareto front is identified based on minimum modal distance value and compared with Gibbs sampling technique (without mode-matching). Two examples with multiple damage cases utilized in validating the proposed approach are as follows: (a) simulated example (ASCE benchmark structure) and (b) experimental example (four storied shear frame laboratory structure). It is observed that the proposed multi-objective framework has performed well in FE model updating in case of both simulated and experimental cases. Additionally, a connection (directly relating the multi-objective weights and error variances) is established between the proposed updating methodology and an existing Bayesian updating methodology to facilitate the probabilistic damage detection in Bayesian framework. Moreover, selection of an appropriate solution (out of the Pareto front) having suitable values of multi-objective weights facilitates to estimate the suitable values of error variances (based on the proposed connection), consequently enabling an efficient Bayesian FE model updating without requirement of any assumption of error variances.
Gibbs Sampling for Damage Detection Using Complex Modal Data from Multiple Setups Ayan Das, Nirmalendu Debnath ASCE ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering, 2021 This paper presents a novel Gibbs sampling approach for structural health monitoring (SHM) with detection of structural changes/damages using incomplete complex modal data measured with a limited number of sensors. The usual difficulty with the availability of sensors in SHM practices and enforcing data acquisition in multiple setups is thoroughly addressed. Structural modeling incorporated with damping is considered in this proposed inverse problem exercise to calibrate damping parameters along with the stiffness and mass parameters facilitating SHM. Both proportional and nonproportional viscous damping are adopted in structural modeling. Detailed formulations on the probabilistic detection of changes/damages are presented in detail. Moreover, a Gibbs sampling technique is introduced to quantify uncertainties of the various sets of uncertain parameters, where samples of the conditional probability density function of a parameter set are obtained iteratively. The proposed approach retains the typical advantage of the nonrequirement of mode-matching. A validation exercise is performed using a three-dimensional building structure (attached with supplementary viscous dampers) and a laboratory steel structure considering multiple damage cases and different sensor placements. The proposed methodology is observed to be efficient for SHM using incomplete complex modal data measured with a limited number of sensors.
A state-of-the-art review of Bayesian finite element model updating techniques for structural systems RP Kiran, A Das, S Bansal Probabilistic Engineering Mechanics 80, 103761 , 2025 2025 Citations: 16
Inception Time Model for Structural Damage Detection Using Vibration V Singh, K Bharali, I Kalita, M Roy, N Debnath, M Saharia, A Das Fourth Congress on Intelligent Systems: CIS 2023, Volume 2 2, 103 , 2024 2024
Hierarchical Bayesian Model Updating Using Modal Data Based on Dynamic Condensation A Das, S Bansal Journal of Vibration Engineering & Technologies , 2023 2023 Citations: 1
Inception time model for structural damage detection using vibration measurements V Singh, K Bharali, I Kalita, M Roy, N Debnath, M Saharia, A Das Congress on Intelligent Systems, 103-122 , 2023 2023 Citations: 1
On the Bayesian model updating based on model reduction using complex modal data for damage detection EK Henikish, A Das, S Bansal Journal of Sound and Vibration 556, 117712 , 2023 2023 Citations: 11
Gibbs sampler-based probabilistic damage detection of structures using reduced order model A Das, N Debnath International Journal of Structural Stability and Dynamics 23 (03), 2350075 , 2023 2023 Citations: 7
A novel Metropolis-within-Gibbs sampler for Bayesian model updating using modal data based on dynamic reduction A Das, RP Kiran, S Bansal Structural Engineering and Mechanics 87 (1), 1-18 , 2023 2023 Citations: 3
Experimental Evaluation of Bayesian Finite Element Model Updating Using Combined A Das, N Debnath Advances in Structural Mechanics and Applications: Proceedings of ASMA-2021, 447 , 2022 2022
Gibbs Sampling for Damage Detection Using Complex Modal Data from Multiple Setups A Das, N Debnath ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A … , 2021 2021 Citations: 16
A multi-objective framework for finite element model updating using incomplete modal measurements N Debnath, A Das Structural Control and Health Monitoring, 1-31 , 2021 2021 Citations: 17
Limited sensor-based probabilistic damage detection using combined normal–lognormal distributions A Das, N Debnath Arabian Journal for Science and Engineering 46 (5), 4639-4663 , 2021 2021 Citations: 5
Bayesian Finite Element Model Updating Without Requirement of Mode-Matching and Sub-structuring of System Matrices A Das, N Debnath Recent Advances in Structural Engineering 135, 73-82 , 2021 2021
Experimental Evaluation of Bayesian Finite Element Model Updating Using Combined Normal and Lognormal Distributions A Das, N Debnath International Conference on Advances in Structural Mechanics and … , 2021 2021
Sampling-based techniques for finite element model updating in bayesian framework using commercial software A Das, N Debnath Advances in Structural Technologies: Select Proceedings of CoAST 2019, 363-379 , 2020 2020 Citations: 2
A Bayesian model updating with incomplete complex modal data A Das, N Debnath Mechanical Systems and Signal Processing 136, 106524 , 2020 2020 Citations: 38
A Bayesian finite element model updating with combined normal and lognormal probability distributions using modal measurements A Das, N Debnath Applied Mathematical Modelling 61, 457-483 , 2018 2018 Citations: 44
Bayesian probabilistic finite element model updating of the UCF (University of Central Florida) benchmark structure A Das, N Debnath Journal of Civil Engineering and Environmental Technology 3 (1), 1-7 , 2016 2016 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
A Bayesian finite element model updating with combined normal and lognormal probability distributions using modal measurements A Das, N Debnath Applied Mathematical Modelling 61, 457-483 , 2018 2018 Citations: 44
A Bayesian model updating with incomplete complex modal data A Das, N Debnath Mechanical Systems and Signal Processing 136, 106524 , 2020 2020 Citations: 38
A multi-objective framework for finite element model updating using incomplete modal measurements N Debnath, A Das Structural Control and Health Monitoring, 1-31 , 2021 2021 Citations: 17
A state-of-the-art review of Bayesian finite element model updating techniques for structural systems RP Kiran, A Das, S Bansal Probabilistic Engineering Mechanics 80, 103761 , 2025 2025 Citations: 16
Gibbs Sampling for Damage Detection Using Complex Modal Data from Multiple Setups A Das, N Debnath ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A … , 2021 2021 Citations: 16
On the Bayesian model updating based on model reduction using complex modal data for damage detection EK Henikish, A Das, S Bansal Journal of Sound and Vibration 556, 117712 , 2023 2023 Citations: 11
Gibbs sampler-based probabilistic damage detection of structures using reduced order model A Das, N Debnath International Journal of Structural Stability and Dynamics 23 (03), 2350075 , 2023 2023 Citations: 7
Limited sensor-based probabilistic damage detection using combined normal–lognormal distributions A Das, N Debnath Arabian Journal for Science and Engineering 46 (5), 4639-4663 , 2021 2021 Citations: 5
A novel Metropolis-within-Gibbs sampler for Bayesian model updating using modal data based on dynamic reduction A Das, RP Kiran, S Bansal Structural Engineering and Mechanics 87 (1), 1-18 , 2023 2023 Citations: 3
Sampling-based techniques for finite element model updating in bayesian framework using commercial software A Das, N Debnath Advances in Structural Technologies: Select Proceedings of CoAST 2019, 363-379 , 2020 2020 Citations: 2
Hierarchical Bayesian Model Updating Using Modal Data Based on Dynamic Condensation A Das, S Bansal Journal of Vibration Engineering & Technologies , 2023 2023 Citations: 1
Inception time model for structural damage detection using vibration measurements V Singh, K Bharali, I Kalita, M Roy, N Debnath, M Saharia, A Das Congress on Intelligent Systems, 103-122 , 2023 2023 Citations: 1
Bayesian probabilistic finite element model updating of the UCF (University of Central Florida) benchmark structure A Das, N Debnath Journal of Civil Engineering and Environmental Technology 3 (1), 1-7 , 2016 2016 Citations: 1
Inception Time Model for Structural Damage Detection Using Vibration V Singh, K Bharali, I Kalita, M Roy, N Debnath, M Saharia, A Das Fourth Congress on Intelligent Systems: CIS 2023, Volume 2 2, 103 , 2024 2024
Experimental Evaluation of Bayesian Finite Element Model Updating Using Combined A Das, N Debnath Advances in Structural Mechanics and Applications: Proceedings of ASMA-2021, 447 , 2022 2022
Bayesian Finite Element Model Updating Without Requirement of Mode-Matching and Sub-structuring of System Matrices A Das, N Debnath Recent Advances in Structural Engineering 135, 73-82 , 2021 2021
Experimental Evaluation of Bayesian Finite Element Model Updating Using Combined Normal and Lognormal Distributions A Das, N Debnath International Conference on Advances in Structural Mechanics and … , 2021 2021
