基于物理学的极端学习机器 (PIELM),用于在扩展的圆柱形腔体周围进行整合
Chuan-Qin Pang1, Zhu-Hao Zhang2, Si-Han Chen3
1Shandong Provincial Key Laboratory of Intelligent Construction and Maintenance of Highway Infrastructure, Shandong Jiaotong University, Jinan, China.
PloS one
|August 14, 2025
概括
一种新的基于物理的极端学习机器 (PIELM) 准确地预测了空洞膨胀后的土壤巩固. 这种方法有效地解释了来自皮埃佐孔透测试 (CPTU) 数据的整合系数.
科学领域:
- 地质技术工程 地质技术工程
- 计算力学 计算力学 计算力学
- 土壤科学 土壤科学
背景情况:
- 腔腔扩张显著影响土壤巩固.
- 传统的物理信息神经网络 (PINNs) 可能是计算密集型的.
- 精确分析多余的孔水压力消散是至关重要的.
研究的目的:
- 引入一个基于物理的极端学习机器 (PIELM),用于分析土壤巩固后空洞扩张.
- 开发一个高效的计算工具,用于地质技术分析.
- 建立一种方法来解释来自CPTU数据的整合系数.
主要方法:
- PIELM将深度神经网络替换为一个单层隐藏的极端学习机器 (ELM).
- 精确的应力不变量定义了过多水压的初始条件.
- 结合管理条件,初始条件和边界条件的损失向量是使用最小平方方法进行优化.
主要成果:
- 该PIELM证明了空洞扩张后整合分析的准确预测.
- 过多的水压力消散被证明高度依赖于其初始分布和土壤力学.
- 该PIELM提供了一种有效的方法来解释合并系数.
结论:
- 拟议的PIELM是分析土壤巩固的有效和高效工具.
- 这种方法提供了一种新的方法,用于从CPTU测量中推导整合系数.
- 了解初始多余水压分布是预测巩固行为的关键.
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