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Reseacher

Name YOSHIDA Ikumasa
Official Title Professor
Affiliation Urban and Civil Engineering
E-mail iyoshida@tcu.ac.jp
Web
  1. http://www.risys.gl.tcu.ac.jp/Main.php?action=profile&type=detail&tchCd=5001644
Profile My specialty is application of Bayesian inference, random field theory, Gaussian process regression, theories of inverse problem and data science technologies to geotechnical and structural engineering problems. These research is useful for health monitoring, risk management and reliability engineering of infra-structures.
Research Field(Keyword & Summary)
  1. 1. Spatial variability of geotechnical properties

    The risk of failure for a geotechnical structure significantly depends on the properties of the surrounding soil. Understanding spatial variability of geotechnical properties is thus important for geotechnical applications. We propose a method for estimating the spatial variability at arbitrary locations using Gaussian process regression with the superposition of multiple Gaussian random fields.

  2. 2. Bayesian Bridge Weigh-In-Motion (BWIM)

    BWIM systems use the bridge as a scale to measure the weight of vehicles passing over it. BWIM technology has attracted attention as a promising tool for bridge design optimization, overweight enforcement, fatigue prediction and maintenance planning. We proposed Bayesian bridge weigh-in-motion (BBWIM), which combines static BWIM and the probabilistic inverse problem, which includes a broad class of inverse problems such as the Kalman filter.

  3. 3. Reliability Estimation of Existing Structures

    The main research topics in this item are to develop the methodology to estimate limit state probability updated by inspection or test data. Inspection or test data of specific site as well as latest knowledge of degrading mechanism should be considered in order to perform accurate reliability estimation of existing structures. The formulation with sequential Monte Carlo simulation (SMCS) or Particle Filter is introduced for updating of model parameters and limit state probability. This is applied for RC structure with chloride deterioration and settlement of ground.
Representative Papers
  1. 1) Yoshida, I., Nakamura, T., Au, S-K: Bayesian updating of model parameters using adaptive Gaussian process regression and particle filter, Structural Safety 102, 102328, 2023.2. DOI: https://doi.org/10.1016/j.strusafe.2023.102328
  2. 2)Yoshida, I, Tomizawa, Y. and Ching, J., Dealing with nonlattice spatially variable data contaminated by white noise using Kronecker-product formulation, Computers and Geotechnics, Vol.154, 2023, February, 105130.
  3. 3)Ching, J., Yoshida, I., Data-Drive Site Characterization for Benchmark Examples: Sparse Bayesian Learning versus Gaussian Process Regression, ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., 2023, 9(1): 04022064
  4. 4)Tomizawa, Y and Yoshida, I, Benchmarking of Gaussian Process Regression with Multiple Random Fields for Spatial Variability Estimation, ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng, 8(4), December, 2022. DOI: 10.1061/AJRUA6.0001277.
  5. 5)Ching, J., Yoshida, I., and Phoon, K. K., Comparison of trend models for geotechnical spatial variability: Sparse Bayesian Learning vs. Gaussian Process Regression, Gondwana Research, 2022.8.
  6. 6)Phoon K. K., Shuku T., Ching J., Yoshida, I. Benchmark examples for data-driven site characterisation, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 16(4), 2022.1, DOI: 10.1080/17499518.2022.2025541
  7. 7)Yoshida, I., Tasaki, Y., and Tomizawa, Y., Optimal placement of sampling locations for identification of a two-dimensional space.，Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2021.9，http://dx.doi.org/10.1080/17499518.2021.1971255.
  8. 8)Yoshida, I, Tomizawa, Y. and Otake, Y., Estimation of trend and random components of conditional random field using Gaussian process regression, Computers and Geotechnics, vol.136, 2021.8 https://doi.org/10.1016/j.compgeo.2021.104179
  9. 9)Yoshida, I. and Shuku, T. 2021. Soil Stratification and Spatial Variability Estimated Using Sparse Modeling and Gaussian Random Field Theory, ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ.Eng. 7(3). 2021.9
  10. 10)Yoshida, I., Sekiya, H. and Mustafa, S., Bayesian Bridge Weigh-In-Motion and Uncertainty Estimation, ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., 7(1) , 2021.1: 04021001
Grant-in-Aid for Scientific Research Support: Japan Society for Promotion of Science (JSPS) https://nrid.nii.ac.jp/en/nrid/1000060409373/
Recruitment of research assistant(s) Yes (1)
Affiliated academic society (Membership type) (1)JSCE, (2)JGS, (3)JAEE
Education Field (Undergraduate level) Earthquake Engineering, Engineering and Ethics, Mathematical Statistics,
Education Field (Graduate level) Advanced Applied Mathematical Statistics, Adv

Affiliation