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統計的手法および機械学習手法を用いたリスクのある交通インフラ資産のマッピング

Rakesh Salunke1, Sadik Khan2

  • 1Department of Civil and Environmental Engineering, Jackson State University, Jackson, MS, 39217, USA. rakesh.salunke@jsums.edu.

Scientific reports
|January 29, 2026
PubMed
まとめ

脆弱な高速道路の土手や斜面（HWS）のマッピングは、交通インフラにとって非常に重要です。この研究では、リスクのあるHWSを特定するための機械学習手法を開発し、資産管理と地滑り防止を改善しました。

キーワード:
地理情報システム地盤工学的資産管理高速道路の斜面インフラ機械学習ランダムフォレスト感受性

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科学分野:

  • 地盤工学;交通インフラ管理;地理情報システム（GIS）

背景:

  • 高速道路の土手や斜面（HWS）は、重要でありながら見過ごされがちな交通資産です。;HWSは地滑りが発生しやすく、極端な降雨イベントによって悪化します。;脆弱なHWSの正確なマッピングは、効果的なインフラ管理に不可欠です。

研究 の 目的:

  • リスクのある高速道路の土手や斜面をマッピングするための機械学習モデルを開発・評価すること。;積極的な資産管理のために、脆弱なHWSの信頼できる目録を作成すること。;HWSの故障に影響を与える主要因を特定すること。

主な方法:

  • リモートセンシングデータから得られた数値標高モデル（DEM）を利用して、原因となる要因を導き出しました。;ランダムフォレストを含む教師あり機械学習モデルを開発しました。;既知のHWS故障位置を正解データとして、地盤工学的、地形学的、水文学的データを使用してモデルをトレーニングしました。;AUC、F1スコア、精度メトリクスを使用してモデルのパフォーマンスを評価しました。

主要な成果:

  • ランダムフォレストモデルは、完璧なスコア（AUC、F1、精度=1.0）を達成しました。;予測精度をバランスさせるために、0.75の最適な確率しきい値が決定されました。;HWSの故障に影響を与える主要因として、標高、河川からの距離、NDVI、降水量が特定されました。

結論:

  • 開発されたGISベースの機械学習手法は、広範囲にわたる脆弱なHWSを効果的にマッピングします。;このアプローチにより、ターゲットを絞った介入とインフラメンテナンスのための資金利用の最適化が可能になります。;交通当局は、戦略的な地盤工学的資産管理のためにこの方法論を採用できます。