Evaluation of Infrastructure Facilities Using Machine Learning
| Project/Area Number |
23710185
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Social systems engineering/Safety system
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| Research Institution | Kansai University |
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Principal Investigator |
YUN Yeboon 関西大学, 環境都市工学部, 准教授 (10325326)
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| Project Period (FY) |
2011 – 2012
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| Project Status |
Completed (Fiscal Year 2012)
|
| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2012: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2011: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
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| Keywords | 社会システム / 維持管理 / 維持管理計画 / 社会的インフラシステム / 機械学習 |
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| Research Abstract |
In this research，we suggest a method using support vector machines (SVM) to evaluate objectively and effectively sewerage systems．Utilizing the characteristics of SVM，we elicit relevant attributes (factors) to collapse of roads from basic data and inspection for the sewerage systems in which road subsidence has occurred due to damaged sewer pipes．Calculating latent risk degree of each sewerage system，we decide a priority for maintaining preventively，properly and efficiently．Finally，the effectiveness of the proposed method will be investigated through some real inspection data．
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Report
(3 results)
Research Products
(27 results)
