Details of a Researcher - OGINO Toshihiro

Peat is composed of organic and inorganic phases, and the mixing ratio of the two phases greatly affects the mechanical and physical properties of the soil. However, plant remains, a component of the organic phase, are diverse and cause wide variations in the natural water content ratio. In this study, we proposed a method for accurately estimating the two-dimensional spatial distribution of water content in peat soils from the limited natural water content data obtained by ground investigation. For the estimation, a tensor analysis based on CP (Canonical Polyadic) decomposition with a radial basis function and a generalized model with a non-negative probability density function as an error function were used. The results show that the proposed method reproduces the observed data well and can adequately represent the variation in the water content ratio of peat.

DOI CiNii Research

Snowmelt and runoff analysis in landslide areas using a time-series statistical model based on monitoring and meteorological data

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

Artificial Intelligence and Data Science ( Japan Society of Civil Engineers ) 6 ( 3 ) 949 - 956 2025 [Refereed]

Research paper (journal)

In this study, to analyze the hydrological behavior of landslide areas in snowy cold regions, we developed a state-space model by integrating Sugawara's snowmelt analysis model with a groundwater tank model. The target area was the Obuchi district of Ani, Akita Prefecture, and the analysis utilized landslide moni- toring data from June 2018 to May 2022, along with meteorological data from the Ani AMeDAS station. The integrated model enabled the estimation of daily snowmelt-equivalent rainfall, taking into account both snowfall and snowmelt, which was then input into the groundwater tank model to analyze the time series of runoff and groundwater storage. Model parameters were estimated using Bayesian inference with the Markov Chain Monte Carlo (MCMC) method.As a result of verifying the consistency of the estimated values, the variations in snow depth and runoff during the snowfall and snowmelt periods were generally reproduced, indicating successful modeling of hydrological processes in snowy cold regions. The mean squared error between the observed and estimated (median) values was 10.4 cm for snow depth and 203 m3/s for runoff.

DOI CiNii Research

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

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