Paper Accepted for Publication in Entropy V. Meruane, P. Véliz, E. López Droguett, A. Ortiz-Bernardin, “Impact location and quantification on an aluminum sandwich panel using principal component analysis and linear approximation with maximum entropy.” ABSTRACT To avoid structural failures it is of critical importance to detect, locate and quantify impact damage as soon as it
Read More » Read More Entropy Vol. 19, No. 4, pp. 137, 2017 V. Meruane, P. Véliz, E. López Droguett, A. Ortiz-Bernardin Abstract To avoid structural failures it is of critical importance to detect, locate and quantify impact damage as soon as it occurs. This can be achieved by impact identification methodologies, which continuously monitor the structure, detecting, locating, and quantifying
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Paper Accepted for Publication in Smart Materials and Structures N. Sanchez, V. Meruane, A. Ortiz-Bernardin, “A novel impact identification algorithm based on a linear approximation with maximum entropy.” ABSTRACT This article presents a novel impact identification algorithm that uses a linear approximation handled by a statistical inference model based on the maximum-entropy principle, termed linear
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Smart Materials and Structures Vol. 25, No. 9, pp. 095050, 2016 N. Sanchez, V. Meruane, A. Ortiz-Bernardin Abstract This article presents a novel impact identification algorithm that uses a linear approximation handled by a statistical inference model based on the maximum-entropy principle, termed linear approximation with maximum entropy (LME). Unlike other regression algorithms as Artificial
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Mechanical Systems and Signal Processing Vol. 54-55, pp. 210-223, 2015 V. Meruane, A. Ortiz-Bernardin Abstract Supervised learning algorithms have been proposed as a suitable alternative to model updating methods in structural damage assessment, being Artificial Neural Networks the most frequently used. Notwithstanding, the slow learning speed and the large number of parameters that need to
Read More » Read More Paper Accepted for Publication in Mechanical Systems and Signal Processing V. Meruane, A. Ortiz-Bernardin, “Structural damage assessment using linear approximation with maximum entropy and transmissibility data.” ABSTRACT Supervised learning algorithms have been proposed as a suitable alternative to model updating methods in structural damage assessment, being Artificial Neural Networks the most frequently used. Notwithstanding, the
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14th Pan-American Congress of Applied Mechanics – PACAM XIV March 24-28, 2014, Santiago, Chile Proceedings of PACAM XIV V. Meruane and A. Ortiz-Bernardin Abstract Supervised learning algorithms have been proposed as a suitable alternative to model updating methods in vibration-based damage assessment, being Artificial Neural Networks the most frequently used. Notwithstanding, the slow learning speed and
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Entropy Vol. 16, No. 5, pp. 2869-2889, 2014 V. Meruane, V. del Fierro, A. Ortiz-Bernardin Abstract Honeycomb sandwich structures are used in a wide variety of applications. Nevertheless, due to manufacturing defects or impact loads, these structures can be subject to imperfect bonding or debonding between the skin and the honeycomb core. The presence of
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V. Meruane, A. Ortiz-Bernardin, “Damage identification using linear approximation with maximum-entropy and transmissibility data,” submitted. ABSTRACT Supervised learning algorithms have been proposed as a suitable alternative to model updating methods in damage assessment, being Artificial Neural Networks the most frequently used. Notwithstanding, the slow learning speed and the large number of parameters that need to
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