様々な学習モデルによる鉄筋コンクリート部材のひび割れ幅計測に関する考察

DOI Web Site 被引用文献1件 参考文献3件
  • 村上 奨太
    室蘭工業大学大学院工学研究科環境創生工学系専攻
  • 鎌田 聖也
    室蘭工業大学大学院工学研究科環境創生工学系専攻
  • 高瀬 裕也
    室蘭工業大学大学院工学研究科もの創造系領域
  • 溝口 光男
    室蘭工業大学大学院工学研究科もの創造系領域

書誌事項

タイトル別名
  • CONSIDERATION OF CRACK WIDTH MEASUREMENT OF REINFORCED CONCRETE STRUCTURES BY USING PLURAL DEEP LEARNING MODELS

抄録

<p>Recently, after a huge earthquake, reinforced concrete buildings were not available or demolished due to sever damages. Therefore, a damage assessment becomes important; hence, measuring damages from images is one of the most useful techniques. In this study, crack widths of the non-structural wall specimens were measured by using plural deep learning model. By the models which provide the extremely small values of Accuracy and Precision, cracks could not be predicted. While, the deep learning model, in which the values for Recall and F1Score were high, could properly identify the cracks; then, the crack width was reasonably measured.</p>

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