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基于机器学习的堆基础的多点振动采集的智能测试方法

Ke Wang^1,2, Weikai Zhao¹, Juntao Wu¹

¹College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.

Sensors (Basel, Switzerland)

|May 14, 2025

概括

本研究介绍了一种智能机器学习模型,用于评估堆基础完整性,改进了传统方法. 拟议的模型使用振动数据准确地识别了使用中的堆基础中的缺陷.

科学领域:

土木工程土木工程是指土木工程.
地质技术工程地质技术工程
人工智能的人工智能

背景情况:

传统的低应变反射波方法用于堆基础测试具有局限性.
评估使用中的完整性,高密度堆基础需要先进的技术.

研究的目的:

提出一个智能多点振动采集测试模型,用于评估堆基础完整性.
克服现有的堆检测方法的局限性.

主要方法:

开发一种基于机器学习的模型,利用多点振动采集.
对不同的模型框架进行比较评估,包括卷积神经网络 (CNN) 和长短期记忆 (LSTM) 神经网络.
使用时间序列堆叠方法预处理多传感器融合信号.

主要成果:

两种CNN和LSTM模型都在确定堆轴中的第一个反射点时表现出高精度.
获得的R平方值>0.98,平均绝对误差<0.41m,差异占比>98%.
这些模型表现出强大的预测能力,测试稳定性和实际实用性.

结论:

智能多点振动采集模型对于评估在使用中的堆基础完整性是有效的.
建议使用CNN来分析堆基础完整性,使用预处理的多点振动和多传感器融合信号.
这些发现支持运营商在地质工程应用中的决策.

关键词:

高市值的公司.低应变低应变的压力机器学习是机器学习.多点振动采集器多点振动采集器堆基础的基础堆基础

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