发表机构
New York University Abu Dhabi; Mansoura University（阿布扎比纽约大学; 曼苏拉大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对岩土设计中力学性质样本稀缺而基本性质丰富的表征差距，提出模块化框架BRIDGE，通过分箱、少量标定与力学模型代理，从常规性质预测力学性质，并以ECC模型在东京黏土数据库验证了仅用10-20%样本即可预测全单元。
AI 中文摘要
岩土设计所需的力学性质必须通过室内试验获得，因此其可用样本数量远少于几乎每个样本都可获得的常规测量的基本（指标）性质。我们将这一差异称为表征差距。纯数据驱动模型未明确施加物理定律，在其训练域之外可能不可靠；而直接的本构标定保持了物理一致性，但需要高级室内试验数据和专家判断。为此，我们提出BRIDGE（连接岩土工程常规输入与数字建模的桥梁），一个从常规可得的基本性质预测力学性质的模块化框架。BRIDGE对数据集进行异常值和非物理响应筛查，将保留样本分箱为单元（Cell），并从每个单元中选择一个小型标定数据集，其余样本用于验证。随后对每个标定样本标定一个力学模型，收敛的标定结果共同定义该单元的力学代理模型及其参数范围，以及力学性质空间中的相应预测包络。当大多数验证样本落在该包络内时，该单元即通过验证，此时代理模型可预测基本性质位于该单元内的新样本的力学性质范围。在本工作中，我们采用扩展剑桥黏土（ECC）本构模型的有限元实现作为力学模型，其四个参数通过多阶段过程进行标定。应用于Tokyo-CLAY/14/67760数据库时，BRIDGE仅需标定一个单元中10%至20%的样本即可预测整个单元的力学性质。最后，由于力学模型、分箱准则和采样方法均为BRIDGE的组成部分，该框架易于扩展。
英文摘要
The mechanical properties needed for geotechnical design require laboratory tests and are thus available for far fewer field samples than routinely measured basic (index) properties which are available for nearly every sample. We term this difference as the characterization gap. Purely data-driven models do not explicitly enforce physical laws and may be unreliable beyond their training domain, while direct constitutive calibration preserves physical consistency but requires advanced laboratory data and expert judgment. We therefore propose BRIDGE (Bridging Routine Inputs and Digital modelling for Geotechnical Engineering) a modular framework that predicts mechanical properties from routinely available basic properties. BRIDGE screens the dataset for outliers and non-physical responses, bins the retained samples into Cells, and selects a small calibration dataset from each Cell, while the remaining samples are used for validation. A mechanical model is then calibrated for each calibration sample, and the converged calibrations collectively define the mechanical surrogate of the Cell together with its parameter ranges and the corresponding predictive envelope in the mechanical property space. A Cell is validated when most validation samples fall within this envelope, whereupon the surrogate can predict the mechanical property ranges of new samples whose basic properties lie within the Cell. In this work, we adopt a finite element implementation of the Extended Cam-Clay (ECC) constitutive model as the mechanical model, whose four parameters are calibrated in a multi-stage process. Applied to the Tokyo-CLAY/14/67760 database, BRIDGE predicts the mechanical properties of an entire Cell from calibrations of just 10 to 20% of its samples. Finally, since the mechanical model, the binning criteria, and the sampling method are components of BRIDGE, the framework is easily extensible.
