Communicated to EWSHM, UK, July 2018

DAMAGE DETECTION IN PRESENCE OF VARYING TEMPERATURE THROUGH RESIDUAL ERROR MODELLING APPROACH WITH DUAL NEURAL NETWORK

Modal property of structural system gets affected not only due to the presence of damage but also due to a variation of environmental agents like temperature, humidity etc. Detection of damage through modal parameter comparison thus may lead to false predictions. This article presents a two-stage data-driven approach in which damage detection and localization are performed in consequence by combining Auto-Associative Neural Network and RBF environment for classification.

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Presented in ICRESH, IIT Madras, January 2019

DESIGN AND HEALTH MONITORING OF TENSEGRITY STRUCTURES: AN OVERVIEW

The very first step to do, when you're eager to work in a new field is to go through its available literature. When the field is relatively newer, it is difficult to gather and skim through the important stuff. To ease this ache, this paper earnestly attempts to compile most of the works that are relevant to the topic at hand.
This work has been published as a book chapter in Reliability, Safety and Hazard Assessment for Risk-Based Technologies, Springer, Singapore.

Published in IFAC Conference

CORRENTROPY BASED IPKF FILTER FOR PARAMETER ESTIMATION IN PRESENCE OF NON-STATIONARY NOISE PROCESS

Existing filtering based structural health monitoring (SHM) algorithms assume a constant noise environment which does not always conform to the reality as noise is hardly stationary. Thus to ensure optimal solution even with non-stationary noise processes, the assumed statistical noise models have to be updated periodically. This work incorporates a modification in the existing Interacting Particle-Kalman Filter (IPKF) to enhance its detection capability in the presence of non-stationary noise processes.  Further, this algorithm also attempts to mitigate the ill effects of abrupt change in noise statistics which most often deteriorates/ diverges the estimation. For this, the Kalman filters (KF) within the IPKF have been replaced with a maximum Correntropy criterion (MCC) based KF that, unlike regular KF, takes moments beyond second order into consideration.

Presented in EC 2018, JU

PLATE DAMAGE DETECTION UNDER VARYING TEMPERATURE USING DUAL NEURAL NETWORK

The structural modal property gets affected not only due to damage but also variation in ambient temperature, humidity etc. Detection of damage through modal comparison thus may lead to false predictions. This article presents a two-stage data-driven approach in which damage detection and localization are performed in consequence.  The proposed algorithm is further validated using numerical experiments.

 

Published in Journal of Civil Structural Health Monitoring (JCSHM)

DOI: ​10.1007/s13349-020-00434-z

ONE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK-BASED DAMAGE DETECTION IN STRUCTURAL JOINTS

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 an efficient alternative to this cause.  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. 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.

Published in Journal of Mechanical Systems and Signal Processing

DOI: https://doi.org/10.1016/j.ymssp.2020.107472

STRUCTURAL HEALTH MONITORING WITH NON-LINEAR SENSOR MEASUREMENTS ROBUST TO UNKNOWN NON_STATIONARY INPUT FORCING

This paper extends an existing interacting filtering algorithm to efficiently estimate structural damages while being robust to unknown non-stationary non-Gaussian input forcing. Furthermore, this approach is generalized beyond linear measurements to encompass the case of non-linear measurements such as strains. The joint estimation of state and parameters is performed by combining Ensemble Kalman filtering, for non-linear system state estimation, and Particle filtering to estimate changes in the structural parameters. The robustness against input force is achieved through an output injection approach embedded in the state filter equation. Numerical simulations for two kinds of response measurements (acceleration and strain) are performed on a 3D frame structure under different damage location and severity scenarios. The sensitivity with respect to noise and the impact of different sensor combinations have also been investigated.

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Published in Recent Advances in Computational Mechanics and Simulations 

DOI: https://doi.org/10.1007/978-981-15-8138-0_23

DAMAGE DETECTION IN PRESENCE OF VARYING TEMPERATURE USING MODE SHAPE AND A TWO-STEP NEURAL NETWORK

The dynamic characteristics of any structural system get affected not only due to damage but also from variations in ambient uncertainty. Thus, false positive or negative alarm may be signalled if temperature effects are not taken care off. The difficulty lies in correlating response measurements to corresponding damage patterns in the presence of varying temperature. This study employs machine learning algorithm to filter out the temperature effect from the measured mode shapes. A two-stage data-driven approach has been developed in which damage detection and localization are performed in consequence. For detection, a model to correlate mode shapes and temperature is formulated using an Auto-Associative Neural Network (AANN) and a temperature-invariant prediction error is defined as Novelty Index (NI). NIs are further classified to corresponding damage cases employing a fully connected layer network. With numerical experiments, the algorithm presented excellent efficiency and robustness against varying temperature in detecting damage.