| Project/Area Number |
22K20601
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Multi-year Fund |
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| Review Section |
0604:Agricultural economics and rural sociology, agricultural engineering, and related fields
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| Research Institution | Kyoto University |
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Principal Investigator |
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| Project Period (FY) |
2022-08-31 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2023: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2022: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | inverse problems / transdimensional / interface detection / RJMCMC / HMC / layer detection / 逆解析 / 機能診断 |
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| Outline of Research at the Start |
自然斜面の亀裂や水みち，地中のパイプラインといった地中や構造物の内部に存在する空洞形状の確率的なイメージング（可視化）を可能にすることで，自然斜面や地盤の安定性や浸透性能の評価，構造物の機能診断やその後の維持管理等に貢献する。現在の空洞探査においては，空洞形状を明確に検出できない問題があり，また内部の可視化精度も高くない。本研究は上記課題を克服するものであり，空洞の数が不明な場合であっても，空洞（固体と空洞の界面）を正確に検出できる一般的な手法を開発する。
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| Outline of Annual Research Achievements |
The research focuses on visualizing subsurface accurately via stochastic inversion, identifying parameters like elastic modulus and interface geometry. The 1-D transdimensional MCMC algorithm, developed initially, now handles correlated and uncorrelated data. Tested on synthetic and real geotechnical data, it precisely detects subsurface layers, depths, and spatial properties, outperforming competitors computationally. Its standout feature is capturing uncertainty in subsurface stratification. A manuscript detailing these findings is under journal review.
Experimental results were mixed despite using a new apparatus. While hydraulic head measurements were precise, outflow rate measurements still show significant errors, albeit less than phase 1. We're reassessing our experimental approach.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
The 1-D transdimensional algorithm developed in phase 1 of this project did not work well on real data (especially for correlated data). This algorithm was improved in the last year and is now able to quantify the uncertainty in number of layers, depth of layer interfaces and random field properties of each layer. This novel algorithm developed has given a valuable insight that accounting for correlations in observation data is of vital importance. This knowledge is now being used in the development of the 2-D transdimensional algorithm.
The new experimental apparatus helped solve some issues in the experimentation program. However, some complications still exist in measuring the outflow rate and we are working on methods to improve the fidelity of these measurements.
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| Strategy for Future Research Activity |
After the successful development and validation of the 1-D transdimensional algorithm on different types of cone penetration test data, work is ongoing to develop the transdimensional HMCID algorithm for void detection.
During the development of the 1-D algorithm, the PI has learnt of a method to integrate cone penetration as well as geophysical data (such as electromagnetic data) to develop higher quality 2-D images of the subsurface by repeated use of 1-D transdimensional algorithms. To this end, I am currently collaborating with geophysicists at Geoscience Australia to integrate the 1-D transdimensional algorithm for cone penetration data (already developed in this project) with their 1-D transdimensional algorithm (open source program known as HiQGA) for electromagnetic data.
A few more trials will be made to obtain reliable measurement results. Learnings from past trials have helped us make improvements in obtaining reliable data. We will continue to modify the device till suitable observations are obtained.
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