通过使用物理信息的神经网络预测预压混凝土梁的早期时间依赖行为
1Department of ICT Integrated Safe Ocean Smart Cities, Dong-A University, 37 Nakdong-Daero 550beon-gil, Saha-gu, Busan 49315, Republic of Korea.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
一个新的物理信息神经网络 (PINN) 通过学习复杂的时间依赖因素,准确地预测预压混凝土梁中的早期行为. 这种高效的方法避免了离散,为传统的数值技术提供了更好的替代方案.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算力学 计算力学 计算力学
- 材料科学 材料科学 材料科学
背景情况:
- 时间依赖的行为,如爬行,收缩和放松,显著影响预应力混凝土 (PSC) 梁的性能.
- 准确预测这些行为对于结构完整性和长期耐用性至关重要.
- 传统的数值方法可能是计算密集型,需要离散,可能会限制效率.
研究的目的:
- 提出和验证一个基于物理学的神经网络 (PINN),用于预测PSC光束中早期的时间依赖行为.
- 利用深度神经网络来建模影响PSC光束行为因素的复杂相互作用.
- 为传统的数值方法提供一个高效和准确的替代方案.
主要方法:
- 一个基于物理学的神经网络 (PINN) 已经开发出来,可以直接解决管理的整微分方程.
- PINN学习了有效的前应力与混凝土爬行,收缩,肌松和模量变化等因素之间的时间依赖的合.
- 通过平衡解决方案准确性和计算成本来确定PINN的最佳超参数.
主要成果:
- 拟议的PINN展示了一种有效和准确的方法来预测PSC光束中的时间依赖行为.
- 该模型成功地学习了影响光束性能的复杂相互作用.
- 对两个代表性横截面的有限差异方法的验证证实了PINN的有效性.
结论:
- 基于物理学的神经网络提供了一个强大而有效的工具,用于分析预应力混凝土梁中早期的时间依赖行为.
- PINNs提供了一种无离散的方法,克服了传统数值方法的局限性.
- 该研究验证了PINN在实际工程应用中的准确性和效率.
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