循环神经网络的长期短期记忆模型,以检测堆的脚,使用堆完整性测试的原始数据
Reham M Samaan1, Mohamed S A Saafan2, Abdelsalam A Mokhtar2
1Faculty of Engineering, Ain Shams University, Cairo, Egypt. 2201036@eng.asu.edu.eg.
Scientific reports
|February 12, 2026
概括
本研究介绍了一种使用长期短期记忆的循环神经网络 (RNN-LSTM) 来自动生成堆积完整性测试反射图的AI系统. 人工智能模型准确地识别了脚的位置,提高了低应变完整性测试的可靠性和效率.
科学领域:
- 地质技术工程 地质技术工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 传统的低应变完整性测试 (LSIT) 依赖于专家的解释,导致主观性和效率问题.
- 自动化反射图生成可以提高准确性,减少堆完整性评估中的人为错误.
研究的目的:
- 在LSIT中开发人工智能系统,用于自动生成速度反射图.
- 减少对人类专业知识的依赖,以解释反射波信号.
- 为了提高堆完整性测试的可靠性和效率.
主要方法:
- 收集了来自埃及驱动堆项目的LSIT数据.
- 预处理的原始加速度信号成数字化速度时间序列.
- 训练并优化了各种具有长期短期记忆 (RNN-LSTM) 的重复神经网络模型.
主要成果:
- 一个六层,32个神经元的LSTM模型实现了高精度 (R2的0.9126训练,0.8778验证).
- 该模型表现出强大的预测概括,高达89.5%的"好"脚位置预测.
- RNN-LSTM有效地模仿了人类生成的反射图,误用风险较低.
结论:
- 深度学习,特别是RNN-LSTM,为传统的反射图生成提供了可靠的替代方案.
- 拟议的AI方法显著减少了在LSIT中对人类经验的依赖.
- 人工智能系统提高了堆完整性测试的准确性和效率.
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