Development of information platform for maintenance and management of Social Infrastructure utilizing information technology
| Project/Area Number |
15K06165
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Civil engineering materials/Construction/Construction management
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| Research Institution | Yamaguchi University |
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Principal Investigator |
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| Project Period (FY) |
2015-10-21 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2019: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2018: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2017: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2015: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | 維持管理 / 社会インフラ / 橋梁点検 / 人工知能 / 深層学習 / 画像認識 / 損傷 / 画像処理 / データ拡張 / 異常検知 / AnoGAN / DOC / 点検支援 / Deep Learning / Semantic Segmentation / 機械学習 / 目視点検 / 点検画像 / 変状図 / 施工管理記録 / 維持管理記録 / データベース / GPS / 準天頂衛星システム / 識別 / 個別ID |
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| Outline of Final Research Achievements |
The purpose of this study is to develop an information platform to support the maintenance of social infrastructure, and to contribute to the efficiency and rationalization of maintenance.
Visual inspection is very important for maintenance of infrastructure. Therefore, in order to support visual inspection, we applied image recognition using deep learning to images acquired by digital cameras and developed a system that automatically assesses classifications related to ranking by detecting deformations. Furthermore, we have developed a system that not only ranks damages but also uses Semantic Segmentation by deep learning to extract damage areas in pixel units for each damage. As a result, it became possible to quantitatively judge whether the damaged area has expanded by periodic inspection every five years, and the accuracy of deterioration prediction was improved.
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| Academic Significance and Societal Importance of the Research Achievements |
建設された構造物は、必ず維持管理が必要であり、これら構造物は年を重ねるごとに増えるため、これら構造物の維持管理を効率的に行うことが重要である。また、熟練技術者の退職や生産年齢人口の減少により、専門技術者の不足が懸念されている。さらに、現在政府は「働き方改革」で労働時間の短縮を目指しており、維持管理の分野においても労働生産性の向上が喫緊の課題となっている。
そこで、本研究は、人工知能などの情報技術を活用し、社会インフラの維持管理を支援するための情報プラットフォームを構築し、維持管理の効率化、合理化を図るものである。
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Report
(6 results)
Research Products
(8 results)
