Page 61 - 《爆炸与冲击》2026年第5期

第 46 卷 第 5 期 爆 炸 与 冲 击 Vol. 46, No. 5
2026 年 5 月 EXPLOSION AND SHOCK WAVES May, 2026

DOI：10.11883/bzycj-2025-0254

基于 PAWN 全局敏感性分析与智能优化算法的

岩石 RHT 本构参数反演 *

田浩帆 ，邵泽楷 ，于 季 ，游 帅 ，王峥峥 1
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（1. 大连理工大学建设工程学院，辽宁 大连 116024；
2. 北京科技大学土木与资源工程学院，北京 100083）
摘要： 针对 Riedel-Hiermaier-Thoma (RHT) 本构模型中 16 个难以标定的参数，基于 Pianosi-Wagener (PAWN) 全局敏
感性分析方法与智能优化算法，联合 MATLAB 与 ANSYS/LS-DYNA 仿真计算平台，引入应力-应变曲线面积差作为核
心评价指标，开发了计算结果的批量提取与自动化三波对齐技术，构建了一套高效、可靠的 RHT 本构参数反演体系，
首次实现了 RHT 模型关键参数的全局敏感性分析与自动化反演。结果表明，在 16 个难以标定的参数中，仅有 8 个参
数对模型响应具有显著的影响。基于智能优化算法的参数反演相对误差控制在 0.23%～9.28% 之间，并通过半圆盘三
点弯试验和缩尺爆破试验验证了其可靠性。该方法显著提升了 RHT 本构参数的标定效率和准确性，其不依赖于构建
庞大的样本数据集，适用于多种荷载工况下的参数标定。相较于传统方法，该方法仅需不到 15 次迭代即可满足反演
精度，能满足计算效率和精度的双重需求，具有良好的工程适用性。
关键词： RHT 本构参数标定；PAWN 方法；智能优化算法；全局敏感性；参数反演
中图分类号： O347.1 国标学科代码： 13015 文献标志码： A

Parameter inversion of rock RHT constitutive model using PAWN global
sensitivity analysis and intelligent optimization algorithm

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TIAN Haofan , SHAO Zekai , YU Ji , YOU Shuai , WANG Zhengzheng 1
（1. School of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, Liaoning, China;
2. School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China）
Abstract: The Riedel-Hiermaier-Thoma (RHT) constitutive model has been widely applied in tunnel blasting, impact-resistant
structural design, and underground protective engineering due to its strong capability to describe the mechanical behavior of
brittle materials such as rock and concrete under high-strain-rate and high-pressure conditions. However, the model involves a
large number of nonlinear parameters, some of which are difficult to determine experimentally because of the high cost of
testing. These key parameters are often adjusted through trial-and-error methods, which limit both modeling efficiency and
simulation accuracy. To overcome these limitations, a comprehensive parameter inversion framework was developed for 16
difficult-to-calibrate parameters of the RHT model. The framework integrated the PAWN (Pianosi-Wagener) global sensitivity
analysis method with intelligent optimization algorithms and coupled MATLAB with the ANSYS/LS-DYNA simulation
platform. The area difference of the stress-strain curve was introduced as the core evaluation metric, and a batch result-
extraction and automated three-wave alignment technique was developed. Based on these developments, an efficient and
reliable RHT parameter inversion system was established, achieving, for the first time, a global sensitivity analysis (GSA) and
automated inversion of key parameters in the RHT model. The results show that, among the 16 parameters analyzed, only eight
exert a significant influence on the model response. The intelligent optimization–based inversion achieved relative errors

* 收稿日期： 2025-08-05；修回日期： 2025-11-09
第一作者： 田浩帆（1999－ ），男，博士研究生，hftian@dlut.edu.cn
通信作者： 王峥峥（1982－ ），男，博士，教授，博士生导师，wangzhengzheng@dlut.edu.cn

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