砂岩土の土壌条件の最適化と機械学習による予測
Xiaoyan Zhang1, Hongsen Li2, Qiang Li3,4
1School of Mechanics and Civil Engineering, China University of Mining and Technology-Beijing, Beijing, 100083, China. zhangxiaoyan@cumtb.edu.cn.
Scientific reports
|September 1, 2025
まとめ
ベントニットスラージと泡を使って土壌を調節すると,砂石の土壌での泥流の可塑性が改善されます. ディープ・ラーニング・モデルは 実行可能性を正確に予測し 地圧バランスシールドのトンネリングの効率を高めます
科学分野:
- 地工学
- トンネル技術
- 材料科学
背景:
- 地圧バランス (EPB) のシールドトンネリングには適切な土壌条件付けが不可欠であり,泥のケーキやカッターの磨きなどの問題を防ぐことができます.
- 砂石の土壌は,その分級と細かい含有量のためにユニークな課題を提示し,泥の流れと作業可能性に影響を与えます.
- 既存の土壌調節方法は,様々な土壌条件に最適化する必要があります.
研究 の 目的:
- ベントニットスラージと泡が,細粒度が異なる砂状の小石の土壌に与える影響を調査する.
- コンディショニング剤の異なる濃度と注入比率の影響を分析する.
- ディープラーニングアルゴリズムを用いた土壌の耕作可能性の予測モデルを開発する.
主な方法:
- 沈殿試験は,10%から80%の微細分を含む無条件と条件化された砂岩土で実施された.
- ベントニットスラージと泡は,異なるスラージ注入比率 (SIR) と泡注入比率 (FIR) を有する調節剤として使用された.
- ディープラーニングアルゴリズムは,土壌条件とスランプ値の非線形関係をモデル化するために使用されました.
主要な成果:
- 無条件の土壌の落下値は,細部含有量の増加とともに著しく減少した.
- ベントニットスラージは,10%から20%の微細な土壌での加工性を改善し,最適なSIRは8%と6%でした.
- 泡は60~80%の細かい土壌での加工能力を大幅に向上させ,200mmまでのスランプ値を達成しました.
- ディープラーニングモデルは 95%以上の精度でスランプ値を予測しました
結論:
- 土壌調節剤,ベントニットスラージ,発泡剤は,砂岩の土壌の処理能力を効果的に高めます.
- コンディショニング剤の有効性は,精細成分と注入比に左右されます.
- ディープラーニングのアプローチは,土壌の耕作可能性を予測するための正確で効率的な方法を提供し,トンネルプロジェクトのためのインテリジェント土壌コンディショニングソリューションをサポートします.
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