• K LAKSHMI

      Articles written in Sadhana

    • Structural damage detection using ARMAX time series models and cepstral distances

      K LAKSHMI A RAMA MOHAN RAO

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      A novel damage detection algorithm for structural health monitoring using time series model is presented. The proposed algorithm uses output-only acceleration time series obtained from sensors on the structure which are fitted using Auto-regressive moving-average with exogenous inputs (ARMAX) model. The algorithm uses Cepstral distances between the ARMAX models of decorrelated data obtained from healthy and any other current condition of the structure as the damage indicator. A numerical model of a simply supported beam with variations due to temperature and operating conditions along with measurement noise is used to demonstrate the effectiveness of the proposed damage diagnostic technique using the ARMAX time series models and their Cepstral distances with novelty indices. The effectiveness of the proposed method is validatedusing the benchmark data of the 8-DOF system made available to public by the Engineering Institute of LANL and the simulated vibration data obtained from the FEM model of IASC-ASCE 12-DOF steel frame. The results of the studies indicate that the proposed algorithm is robust in identifying the damage from the acceleration datacontaminated with noise under varied environmental and operational conditions.

    • Output-only damage localization technique using time series model

      K LAKSHMI A RAMA MOHAN RAO

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      In this paper, we present a technique to detect the time instant and location of damage in civil structures using scalar time series models, by handling operational variability and measurement noise. The scalar Autoregressive (AR) and Autoregressive with exogenous inputs (ARX) models are used to obtain the timeinstant of damage and its spatial location. The spatial damage feature to locate the damage is obtained using a metric constructed from the probability density values of the prediction errors of AR–ARX model. The proposed method does not resort to any computationally expensive vector time series models to locate the damage and so highly preferable in smart wireless online continuous SHM schemes. Numerical simulation studies are carried out by using a simply supported beam model. The results of the studies indicate that the proposed technique iscapable of identifying both the time instant and location of damage accurately using the proposed PDF based damage index. In order to validate the proposed technique with experimental results, the time-history data from the three-story bookshelf benchmark structure of EI-LANL is used. Finally, the laboratory experimental studies carried out on an RCC simply supported beam with inflicted damage are also presented. The experimental studies clearly indicate the effectiveness of the proposed damage index to detect the location of damage, byhandling operational variability and measurement noise.

    • Data clustering using K-Means based on Crow Search Algorithm

      K LAKSHMI N KARTHIKEYANI VISALAKSHI S SHANTHI

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      Cluster analysis is one of the popular data mining techniques and it is defined as the process of grouping similar data. K-Means is one of the clustering algorithms to cluster the numerical data. The features of K-Means clustering algorithm are easy to implement and it is efficient to handle large amounts of data. The major problem with K-Means is the selection of initial centroids. It selects the initial centroids randomly and it leads to a local optimum solution. Recently, nature-inspired optimization algorithms are combined with clustering algorithms to obtain the global optimum solution. Crow Search Algorithm (CSA) is a new populationbasedmetaheuristic optimization algorithm. This algorithm is based on the intelligent behaviour of the crows. In this paper, CSA is combined with the K-Means clustering algorithm to obtain the global optimum solution. Experiments are conducted on benchmark datasets and the results are compared to those from various clustering algorithms and optimization-based clustering algorithms. Also the results are evaluated with internal, external and statistical experiments to prove the efficiency of the proposed algorithm.

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