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Fabrication and Design of Wood-Based High-Performance Composites
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スチールスラグ強化セメント安定化リサイクルアグレガートベース:機械性能とPINNベースの硫酸塩拡散予測
Guodong Zeng1, Hao Li1, Yuyuan Deng2
1Foshan Transportation Science and Technology Co., Ltd., Foshan 528041, China.
Materials (Basel, Switzerland)
|February 13, 2026
まとめ
スチール・スラグ・アグレガート (SSA) は,舗装用ベース用のセメント安定化リサイクルアグレガート (CSR) を強化し,機械的強度と硫酸塩耐性を向上させます. 物理情報ニューラルネットワーク (PINN) モデルは,これらの改良された材料における硫酸塩拡散を正確に予測します.
科学分野:
- 材料科学 材料科学とは
- 土木工学 土木工学とは
- ジオテクニカルエンジニアリング ジオテクニカルエンジニアリング
背景:
- セメント安定化リサイクルアグレガート (CSR) は,高孔性のリサイクルアグレガート (RA) の強度が低く,舗装ベースに制限があります.
- CSRにおける硫酸塩輸送と耐久性メカニズムに関するデータは限られており,その広範な適用を妨げています.
- スチール・スラグ・アグレガート (SSA) は,高い強度と潜在的反応性を有しており,CSRの改善に適しています.
研究 の 目的:
- 舗装用アプリケーションのための鋼鉄スラグ強化セメント安定化リサイクルアグレガート (CSRS) の開発と評価.
- CSRS.の機械性能と耐久性,特に硫酸塩耐性を調査する.
- CSRS.における硫酸塩拡散を予測するための物理情報ニューラルネットワーク (PINN) モデルを開発する.
主な方法:
- 機械的性質は,無制限圧縮強度 (UCS) と間接張力強度 (ITS) 試験を使用して評価されました.
- 耐久性は,熱縮小と硫酸耐性試験によって評価されました.
- 物理情報ニューラルネットワーク (PINN) を使用して,硫酸塩予測モデルが開発されました.
主要な成果:
- CSRSは,CSRと比較して7日 (6.7%) と28日 (16.0%) のUCS,およびITS (4.3%-5.9%) の増加を示しました.
- CSRSは,硫酸性耐性を改善し,硫酸性耐性係数が18.8%増加しました.
- PINNモデルは,CSRSにおける低硫酸塩濃度を正確に予測し,平均相対誤差は14%未満でした.
結論:
- SSAを組み込むことは,CSRの機械的特性と硫酸塩耐性を大幅に高めます.
- CSRSは,歩道の基礎のための有望な材料であり,性能と耐久性を向上させています.
- 開発されたPINNモデルは,舗装材料における硫酸塩拡散のシミュレーションと予測のための信頼できるツールです.
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