在极端环境条件下,基于飞灰的地质聚合物高性能混凝土的耐用性和使用寿命预测
1Department of Civil Engineering, Priyadarshini College of Engineering, Nagpur, Maharashtra, 440019, India. vikrant.vairagade@pcenagpur.edu.in.
Scientific reports
|December 4, 2025
概括
本研究介绍了一种使用先进人工智能模型的综合框架,用于在极端条件下预测地质聚合物高性能混凝土 (HPC) 的耐用性和使用寿命. 该框架提高了准确性,并减少了对更好的结构材料工程的数据需求.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 人工智能的人工智能
背景情况:
- 在极端环境下,基于飞灰的高性能地聚合物混凝土 (HPC) 的耐用性对结构完整性至关重要.
- 现有的评估方法在复杂的降解机制和预测剩余使用寿命方面扎.
研究的目的:
- 开发一个集成的人工智能驱动的框架,用于预测地质聚合物HPC的耐用性和使用寿命.
- 模拟多重降解机制 (冷解,硫酸盐,热) 并优化混合设计.
- 为基于风险的决策提供准确,可解释和可扩展的耐用性预测.
主要方法:
- 基于变压器的物理信息神经网络 (TPINN) 用于降解建模.
- 空间时间图神经网络 (ST-GNN) 用于裂传播分析.
- 深度强化学习 (DRL) 用于混合优化和高图自主监督学习 (HSSL) 用于热变形.
- 不确定性量化扩散概率模型 (DPM-UQ) 用于服务寿命预测.
主要成果:
- 综合框架显著提高了地质聚合物HPC耐久性的预测准确性和可解释性.
- 它通过像HSSL这样的技术减少了对广泛标记数据的需求.
- 该DPM-UQ提供随机服务寿命预测与量化的故障风险.
结论:
- 开发的框架为预测地质聚合物HPC在极端环境中的耐用性提供了可信和可扩展的解决方案.
- 它有助于延长关键基础设施的使用寿命和维护计划.
- 这种方法通过将物理知情人工智能与深度学习相结合,推进结构材料工程.
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