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

爆炸荷载下混凝土行为的调查对于处理烈性炸药或寻求针对故意爆炸事件的保护措施的行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参数可调的经济有效的替代方案。为了模拟混凝土在高应变率下的复杂行为，存在几种局部模型，它们考虑了不同的现象(具有历史变量的孔破裂的非线性状态方程、应变率相关的状态方程、硬化、软化、压力相关的屈服面、压缩状态下的剩余强度等)。这种复杂性也导致了很大的参数空间，使得模型校准成为一项不平凡的任务。近年来，用于显式模拟的梯度增强方法被引入。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，提出了一种正则化扩展的RHT模型，它模拟了复杂材料的行为，同时与网格无关。这包括一个带有惯性项的梯度增强，以模拟混凝土在高应变率下的强度增长。此外，还引入了一个粘性项来解释低应变率。与最先进的模型中使用现象学方法描述应变率依赖强度的情况不同，新模型基于物理假设。梯度增强还与减小的长度参数相结合，从而减轻了损伤区的虚假扩展。集成梯度增强将额外的参数引入到本已复杂的模型中，使参数确定进一步复杂化。为了利用先前的研究，按照公平的原则，开发了一个具有相应元数据模式的实验数据数据库，并与研究界共享。由于正则化，本构参数是与网格无关的材料参数，通过贝叶斯推理确定。在实际应用中，不需要进行实验就能从有限的数据中获取完整的参数集。因此，使用机器学习和降维技术来探索参数空间，以识别参数和混凝土配合比之间的可能关系。这种方法能够确定任意混凝土混合料的可靠近似值。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，所有软件组件都是作为开放源代码开发的，并使用自动化工具进行链接。其目的是开发一种方法，使在工业应用中使用具有质量保证的复杂数值模型成为可能。

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.