Communicated to IWSHM, 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.
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)
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.