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Authors: Takaya Kawakatsu 1 ; 2 ; Atsuhiro Takasu 2 and Jun Adachi 2

Affiliations: 1 The University of Tokyo, 2-1-2 Hitotsubashi, Chiyoda, Tokyo and Japan ; 2 National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda, Tokyo and Japan

ISBN: 978-989-758-351-3

Keyword(s): Generative Adversarial Network (GAN), Civil Engineering, Structural Health Monitoring, Multimodal.

Related Ontology Subjects/Areas/Topics: Applications ; Cardiovascular Imaging and Cardiography ; Cardiovascular Technologies ; Health Engineering and Technology Applications ; Pattern Recognition ; Sensors and Early Vision ; Signal Processing ; Software Engineering

Abstract: This paper contributes to the wide acceptance of autonomous health monitoring for real bridges. Our approach involves dynamic simulation, whereby damage may be identified by detecting abnormal mechanical behavior in the bridge components in response to passing vehicles. Conventionally, dynamic simulation requires expert knowledge of mechanics, components, materials, and structures, in addition to accurate modeling. Moreover, it requires detailed specification of the external forces applied, such as vehicle speeds, loci, and axle weights. This paper introduces a novel media-fusion framework to obtain a bridge dynamic model in a fully data-driven fashion. The proposed generative model also successfully simulated strain responses for a real road bridge by using a camera and strain sensors on the bridge. The generative network was trained by an adversarial learning algorithm customized for media-fusion analysis.

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Paper citation in several formats:
Kawakatsu, T.; Aihara, K.; Takasu, A. and Adachi, J. (2019). Adversarial Media-fusion Approach to Strain Prediction for Bridges.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 736-743. DOI: 10.5220/0007555007360743

@conference{icpram19,
author={Takaya Kawakatsu. and Kenro Aihara. and Atsuhiro Takasu. and Jun Adachi.},
title={Adversarial Media-fusion Approach to Strain Prediction for Bridges},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={736-743},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007555007360743},
isbn={978-989-758-351-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Adversarial Media-fusion Approach to Strain Prediction for Bridges
SN - 978-989-758-351-3
AU - Kawakatsu, T.
AU - Aihara, K.
AU - Takasu, A.
AU - Adachi, J.
PY - 2019
SP - 736
EP - 743
DO - 10.5220/0007555007360743

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