Details of a Researcher - OGINO Toshihiro

OGINO Toshihiro

写真a

Affiliation

Graduate School of Engineering Science Department of Systems Design Engineering Civil and Environmental Engineering Course

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Research Interests 【 display / non-display 】

室内試験
泥炭
ベンダーエレメント
地盤工学
土質工学

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Graduating School 【 display / non-display 】

　

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1996.03

Hokkaido University Graduated

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Graduate School 【 display / non-display 】

　

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1999.03

Hokkaido University Doctor's Degree Program Unfinished Course

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Campus Career 【 display / non-display 】

2010.06

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Now

Akita University Associate Professor
2005.04

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2010.05

Akita University Lecturer
1999.04

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2005.03

Akita University Research Assistant

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Research Areas 【 display / non-display 】

Social Infrastructure (Civil Engineering, Architecture, Disaster Prevention) / Geotechnical engineering

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Research Achievements 【 display / non-display 】

◆Original paper【 display / non-display 】

Mechanism of sampling disturbance for peat ground and its influence on mechanical properties

Yamazoe N.

Soils and Foundations ( Soils and Foundations ) 63 ( 5 ) 101361 - 101361 2023.10 [Refereed]

Research paper (journal) Domestic Co-author

DOI

VIBRATION CHARACTERISTICS OF SELF-MONITORING BENDER ELEMENTS WITH VARIOUS DIMENSIONS AND GEOMETRIES

Toshihiro OGINO, Shinya NISHIO

79 ( 15 ) 2023.01 [Refereed]

Research paper (journal) Single author

Comparison of prediction performance for S-wave arrival point in bender element test using various machine learning models

MOMIYAMA Shoya, OGINO Toshihiro

Artificial Intelligence and Data Science ( Japan Society of Civil Engineers ) 4 ( 3 ) 60 - 69 2023 [Refereed]

Research paper (journal)

In Bender Element tests, determining the arrival time of the S-wave is often challenging from received waveforms. To improve the accuracy of S-wave arrival time prediction using machine learning as decision support for experimenters, we created three machine learning models based on support vector regression, Gaussian process regression, and neural network. We compared their prediction accuracies. We obtained 7240 artificial received waveforms with true S-wave arrival times from the linear system theory and trained the models using 11-dimensional features reflected from waveforms and test condition. The prediction performance of three models were compared using the errors between predicted arrival times and the values determined by an expert. The comparison revealed characteristics of each model in prediction and that Gaussian process regression model demonstrated the closest approximation to the values determined by the expert.

DOI CiNii Research

Estimation of pore water pressure in aquifers using a time series model based on field observation in landslide site

AIHARA Wataru, OGINO Toshihiro, FUJII Noboru, KURIYAMA Daisuke, OGITA Shigeru

Artificial Intelligence and Data Science ( Japan Society of Civil Engineers ) 4 ( 3 ) 328 - 336 2023 [Refereed]

Research paper (journal)

In this study, a state-space model based on a tank model was constructed, and field observation data obtained from a landslide site in Kobuchi area, Akita Prefecture, from 2018 to May 2022, were fitted. The estimation was conducted for outflow volume from the landslide site and the water levels in five representative boreholes. The estimated results obtained through Markov chain Monte Carlo method fairly captured the behaviors of water levels and outflow volume. Furthermore, based on the estimated water levels of the tanks, the changes in pore water pressure acting on the aquifers of the landslide site were estimated.

DOI CiNii Research

Evaluation of slope soundness using state space models based on expressway inspection data

MARUYAMA Kouki, OGINO Toshihiro, HOSHINO Katsuyuki, MIYAKOSHI Makoto

Artificial Intelligence and Data Science ( Japan Society of Civil Engineers ) 4 ( 3 ) 320 - 327 2023 [Refereed]

Research paper (journal)

In this study, based on number of damaged places and their damage degree data which are obtained from slope inspection of expressway, two time-series models were developed using state space model to estimate soundness index of slopes for each expressway route. The soundness index of slopes was defined as three latent states: occurrence rate of the damaged places, the number of occurred damaged place, and the occurrence probabilities of damage degrees. The developed models were fitted using inspection data from three expressway routes in Akita Prefecture. The estimation of soundness index was performed using the Markov chain Monte Carlo method. Based on the estimation results, a discussion on the temporal variations of these latent states is presented.

DOI CiNii Research

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