使用机器学习和电机阻抗与纳米增强传感器的组合预测早期的混凝土强度
Huang Ju1, Lin Xing2, Alaa Hussein Ali3
1School of Mechanical Engineering, Chongqing Technology and Business University, Chongqing, 400067, China.
Environmental research
|June 1, 2024
概括
实时混凝土强度监测对于结构完整性至关重要. 使用纳米增强传感器和人工智能的新混合方法准确地估计了混凝土强度的发展,提高了建筑安全.
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.
- 传感器技术 传感器技术
背景情况:
- 确保具体的结构完整性需要实时监测早期力量发展.
- 传统的方法往往不能提供连续的,准确的强度数据.
- 纳米增强传感器和先进的计算技术提供了有前途的解决方案.
研究的目的:
- 开发和验证使用非破坏性测试 (NDT) 和人工智能对混凝土的混合强度估计方法.
- 将纳米增强型压电传感器与长短期记忆 (LSTM) 和人工神经网络 (ANN) 集成,以加强监控.
- 研究各种因素对混凝土强度发展的影响,从造到28天.
主要方法:
- 使用了电机阻抗 (EMI) 方法与纳米增强型压电传感器.
- 开发了一种混合的非破坏性测试-长期短期记忆-人工神经网络 (NDT-LSTMs-ANN) 模型.
- 输入数据包括水与水泥的比率,温度,固化时间,以及基于内部温度的成熟度.
主要成果:
- 混合NDT-LSTMs-ANN模型展示了高度准确的混凝土强度估计.
- 该研究成功地监测了从早期水合到28天的混凝土强度的发展.
- 纳米技术的整合显著改善了监测能力.
结论:
- 开发的混合方法为实时混凝土强度监测提供了可行和有效的解决方案.
- 使用纳米增强传感器的基于压电的EMI技术代表了建筑结构健康监测的重大进步.
- 这种方法通过可靠的强度评估来提高建筑安全性和效率.
关键词:
人工神经网络 (ANN) 是一个人工神经网络.机电阻抗是电机械阻抗的长时间的短期记忆 (LSTM)编号法 编号法 编号法 编号法非破坏性测试 (NDT) 技术压电传感器是一种电压传感器.智能纳米智能纳米智能纳米更多相关视频
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