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Entropy 2016, 18(5), 167; doi:10.3390/e18050167

Estimation of Tsunami Bore Forces on a Coastal Bridge Using an Extreme Learning Machine

1
Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
2
Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
3
Department of Civil and Environmental Engineering, Faculty of Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Malaysia
4
Department of Mechanical Convergence Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Korea
*
Authors to whom correspondence should be addressed.
Academic Editors: Andreas Holzinger and Kevin H. Knuth
Received: 7 September 2015 / Revised: 26 November 2015 / Accepted: 9 December 2015 / Published: 28 April 2016
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Abstract

This paper proposes a procedure to estimate tsunami wave forces on coastal bridges through a novel method based on Extreme Learning Machine (ELM) and laboratory experiments. This research included three water depths, ten wave heights, and four bridge models with a variety of girders providing a total of 120 cases. The research was designed and adapted to estimate tsunami bore forces including horizontal force, vertical uplift and overturning moment on a coastal bridge. The experiments were carried out on 1:40 scaled concrete bridge models in a wave flume with dimensions of 24 m × 1.5 m × 2 m. Two six-axis load cells and four pressure sensors were installed to the base plate to measure forces. In the numerical procedure, estimation and prediction results of the ELM model were compared with Genetic Programming (GP) and Artificial Neural Networks (ANNs) models. The experimental results showed an improvement in predictive accuracy, and capability of generalization could be achieved by the ELM approach in comparison with GP and ANN. Moreover, results indicated that the ELM models developed could be used with confidence for further work on formulating novel model predictive strategy for tsunami bore forces on a coastal bridge. The experimental results indicated that the new algorithm could produce good generalization performance in most cases and could learn thousands of times faster than conventional popular learning algorithms. Therefore, it can be conclusively obtained that utilization of ELM is certainly developing as an alternative approach to estimate the tsunami bore forces on a coastal bridge. View Full-Text
Keywords: tsunami bore forces; wave height; estimation; extreme learning machine (ELM); six-axis load cell tsunami bore forces; wave height; estimation; extreme learning machine (ELM); six-axis load cell
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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. (CC BY 4.0).

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MDPI and ACS Style

Mazinani, I.; Ismail, Z.B.; Shamshirband, S.; Hashim, A.M.; Mansourvar, M.; Zalnezhad, E. Estimation of Tsunami Bore Forces on a Coastal Bridge Using an Extreme Learning Machine. Entropy 2016, 18, 167.

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