用物理信息的神经网络识别大质量混凝土的热升温特征
Jae Min Lee1, Chang Joon Lee1, WoonSeong Jeong1
1Department of Architectural Engineering, Chungbuk National University, 1 Chungdae-ro, Heungdeok-gu, Cheongju 28644, Republic of Korea.
Materials (Basel, Switzerland)
|October 29, 2025
概括
基于物理学的神经网络 (PINN) 准确地识别了混凝土质中的亚亚波动温度升高 (ATR) 特性. 这种方法即使在杂的短期数据中也被证明是可靠的,展示了其对参数识别的稳定性.
科学领域:
- 计算力学 计算力学 计算力学
- 材料科学 材料科学 材料科学
- 在工程领域的人工智能.
背景情况:
- 准确预测热温度升高 (ATR) 对大质量混凝土结构至关重要,以防止热裂变.
- 识别ATR特征的传统方法可能是复杂和数据密集的.
- 物理信息神经网络 (PINNs) 通过将物理定律集成到神经网络训练中,提供了一种新的方法.
研究的目的:
- 调查PINNs在混凝土中识别ATR关键特征的能力.
- 为了确定数据观察期和噪声对参数识别PINN性能的影响.
- 评估基于PINN的方法的可靠性和一致性.
主要方法:
- 利用了从三种不同的ATR模型的数值模拟中生成的虚拟实验数据.
- 使用PINN来识别定义最大ATR和温度上升速度的参数.
- 引入了不同级别的噪音和观察期,以评估强度和性能.
主要成果:
- 即使使用短期观察数据,PINNs也成功地以高准确度识别了未知的ATR参数.
- 参数识别性能受到数据观察期的影响,但在引入的噪声下仍然稳健.
- 对10个独立培训课程的统计分析证实了PINN方法的可靠性和一致性.
结论:
- PINNs提供了一种强大而可靠的工具,用于识别混凝土质量中的附加性温度上升特征.
- 该方法即使在具有挑战性的数据条件下也表现出有效性,例如有限的观察时间和数据噪声.
- 这项研究验证了PINNs在土木工程应用中的反向问题的潜力.
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