A regularized concrete model for high strain rates with a FAIR parameter estimation framework

爆炸荷载下混凝土性能的研究对于处理烈性炸药或寻求保护以防止故意爆炸事件的工业和政府机构至关重要。虽然实地实验提供了见解，但它们是资源密集型的。因此，数值模拟提供了一种具有可调参数的成本效益的替代方案。对于高应变率下混凝土的复杂行为的模拟，存在几个考虑不同现象的局部模型（具有孔隙破碎、应变率依赖性、硬化、软化、压力依赖性屈服面、压缩状态下的剩余强度等历史变量的非线性状态方程）。这种复杂性还导致了较大的参数空间，使得模型校准成为一项重要的任务。近年来，梯度增强显式模拟已被引入。然而，这些模型仅部分涵盖了现实中观察到的失效机制的复杂性。因此，提出了RHT模型的正则化扩展，该模型模拟复杂的材料行为，同时与网格无关。这包括一个梯度增强与惯性项，以模拟在高应变率的混凝土的强度增加。此外，粘度项被引入到低应变率。与现有模型中使用唯象方法描述应变率相关强度不同，新模型基于物理假设。梯度增强进一步与减小的长度参数相结合，从而减轻损伤区的虚假扩张。集成梯度增强向已经复杂的模型引入额外的参数，使得参数确定更加复杂。为了利用先前的研究，开发了一个具有相应元数据模式的实验数据数据库，并根据FAIR原则与研究社区共享。由于正则化，本构参数是使用贝叶斯推断确定的与网格无关的材料参数。在实际应用中，需要从有限的数据中获取完整的参数集而无需进行实验。因此，参数空间探索使用机器学习和降维技术，以确定参数和混凝土配合比组成之间可能的关系。这种方法可以确定可靠的近似任意混凝土混合物。为了确保整个方法的可重复性，包括数据、模拟模型和材料参数的确定，所有软件组件都是开源的，并使用自动化工具进行链接。其目的是开发一种方法，使其能够使用复杂的数值模型与工业应用的质量保证。

The investigation of concrete behavior under blast loads is of utmost importance for industries and government agencies handling high explosives or seeking protection against intentional blast events. While field experiments offer insights, they are resource-intensive. Therefore, numerical simulations provide a cost-effective alternative with adjustable parameters. For the simulation of the complex behavior of concrete under high strain rates, several local models exist that consider different phenomena (nonlinear equation of state with history variable for pore-crushing, strain-rate-dependency, hardening, softening, pressure-dependent yield surfaces, residual strength in compressive states, etc.). This complexity also results in a large parameter space, making model calibration a non-trivial task. In recent years, gradient-enhancement for explicit simulations has been introduced. However, these models only partly cover the complexity of the failure mechanisms observed in reality. Therefore, a regularized extension of the RHT model is proposed that simulates the complex material behavior while being mesh-independent. This includes a gradient-enhancement with an inertia term to model the strength increase of the concrete in high strain rates. Additionally, a viscosity term is introduced to account for low strain rates. In contrast to a description of the strain-rate dependent strength using phenomenological approaches in state-of-the art models, the new model is based on physical assumptions. The gradient enhancement is further combined with a decreasing length parameter, mitigating a spurious expansion of the damage zone. Integrating gradient enhancement introduces additional parameters to an already complex model complicating the parameter determination even further. To leverage prior research, a database of experimental data with corresponding metadata schema is developed and shared with the research community according to the FAIR principles. Due to the regularization, constitutive parameters are mesh-independent material parameters which are determined using Bayesian inference. In practical applications, acquiring the complete parameter set from limited data without conducting experiments is desirable. Hence, the parameter space is explored using machine learning and dimensionality reduction techniques to identify possible relationships among the parameters and the concrete mix composition. This approach enables the determination of reliable approximations for arbitrary concrete mixtures. In order to ensure the reproducibility of the entire methodology, including the data, the simulation models and the determination of the material parameters, all software components are developed as open source and linked using automation tools. The aim is to develop a methodology that makes it possible to use complex numerical models with quality assurance for industrial applications.