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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 212369, 15 pages
Research Article

Structural Health Monitoring of Tall Buildings with Numerical Integrator and Convex-Concave Hull Classification

1Departamento de Control Automatico, CINVESTAV-IPN, 07360 México, DF, Mexico
2Departamento de Computacion, CINVESTAV-IPN, 07360 México, DF, Mexico

Received 1 September 2012; Accepted 15 November 2012

Academic Editor: Huaguang Zhang

Copyright © 2012 Suresh Thenozhi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An important objective of health monitoring systems for tall buildings is to diagnose the state of the building and to evaluate its possible damage. In this paper, we use our prototype to evaluate our data-mining approach for the fault monitoring. The offset cancellation and high-pass filtering techniques are combined effectively to solve common problems in numerical integration of acceleration signals in real-time applications. The integration accuracy is improved compared with other numerical integrators. Then we introduce a novel method for support vector machine (SVM) classification, called convex-concave hull. We use the Jarvis march method to decide the concave (nonconvex) hull for the inseparable points. Finally the vertices of the convex-concave hull are applied for SVM training.