バイオインスピレーションによる適応確率IVYPSOアルゴリズムと,高性能コンクリートの強さを予測する適応戦略のニューラルネットワーク最適化
Kaifan Zhang1, Xiangyu Li2, Songsong Zhang1
1School of Computer Science, Hubei University of Technology, Wuhan 430068, China.
Biomimetics (Basel, Switzerland)
|August 27, 2025
まとめ
この研究は,高性能コンクリート (HPC) の強度予測のための新しいハイブリッドモデルであるAP-IVYPSO-BPを導入します. このモデルは,複雑なコンクリートミックス設計の予測精度と信頼性を大幅に改善します.
科学分野:
- 土木工学
- 材料科学
- コンピューター・インテリジェンス
背景:
- 高性能コンクリート (HPC) の強さの正確な予測は,構造的整合性と持続可能な建設に不可欠です.
- HPC構成要素内の複雑な相互作用は,従来の予測モデルに挑戦し,不正確さにつながります.
- 既存の方法は高次元性と騒々しいデータで,オーバーフィットとローカル・オプティマスのリスクがあります.
研究 の 目的:
- バイオインスパイアされた新しいハイブリッド最適化モデル,AP-IVYPSO-BPを開発し,HPCの強さを予測する.
- HPCにおける非線形および多要素関係を捉えるための従来のモデルの限界に対処する.
- コンクリートの強度予測モデルの精度,強度,一般化を向上させる.
主な方法:
- 粒子群最適化 (PSO) と適応確率戦略とのイビーアルゴリズム (IVYA) の統合.
- AP-IVYPSOアルゴリズムを使用して,バックプロパガンダニューラルネットワーク (BPNN) の重みとバイアスの最適化.
- HPCミックスサンプル1030のデータセットでのトレーニングと検証
主要な成果:
- AP-IVYPSO-BPは従来のBPNN,PSO-BP,GA-BP,IVY-BPモデルと比較して優れた性能を示した.
- R2 0.9542,MAE 3.0404,RMSE 3.7991 のテストセットで高い精度を達成しました.
- このモデルは,グローバル探査とローカル利用を効果的にバランスさせ,コンバージェンスの問題を緩和しました.
結論:
- 提案されたAP-IVYPSO-BPモデルは,HPCの圧縮強さを予測するための非常に正確で信頼性の高いソリューションを提供します.
- このバイオインスパイアされたアプローチは,土木工学アプリケーションと材料設計に実用的な価値を提供します.
- このモデルの予測能力は より堅牢で持続可能な建設手法に寄与します
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