Evaluation of automatic versus material test-based calibrations of concrete models for ballistic impact simulations

IF 2.1 Q2 ENGINEERING, CIVIL
A. Antoniou, T. Børvik, M. Kristoffersen
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引用次数: 3

Abstract

Concrete is used for protective structures all over the world. Accurate response estimates to a given threat is vital for designing such structures. Concrete models often require numerous input parameters for which sufficient experimental data can be challenging to obtain. Some models are accompanied by parameter generators which use the unconfined compression strength to extrapolate the remainder of the parameters based on experimental databases. This study investigates simulation of ballistic impact on high-strength concrete with 75 MPa nominal unconfined cylindrical compressive strength. The first objective is to investigate the accuracy parameter generators to produce input data for commonly used concrete material models. The second objective is to establish and evaluate a simplified parameter calibration procedure based on standard material experiments and data from the literature. The results employing parameter generators varied notably between the models while still giving decent ballpark estimates. The parameters obtained from inverse modelling of standardized material tests improved the results significantly. The findings of this study recommend caution when using automatic parameter generators. Although a detailed calibration of these concrete models is complicated, a simplified calibration gives reasonable predictions, making this the advisable approach for designing concrete protective structures.
基于自动与材料测试的弹道冲击模拟混凝土模型校准评估
混凝土被用于世界各地的保护结构。对给定威胁的准确反应估计对于设计此类结构至关重要。具体模型通常需要大量的输入参数,要获得足够的实验数据可能很困难。一些模型配有参数生成器,该生成器使用无侧限抗压强度来推断基于实验数据库的其余参数。本研究研究模拟了具有75MPa标称无侧限圆柱抗压强度的高强度混凝土的弹道冲击。第一个目标是研究精度参数生成器,以生成常用混凝土材料模型的输入数据。第二个目标是基于标准材料实验和文献数据,建立和评估简化的参数校准程序。使用参数生成器的结果在模型之间变化显著,同时仍然给出了不错的近似估计。通过标准化材料试验的逆向建模获得的参数显著改善了结果。这项研究的结果建议在使用自动参数生成器时要小心。尽管这些混凝土模型的详细校准很复杂,但简化的校准可以给出合理的预测,使其成为设计混凝土保护结构的可取方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.30
自引率
25.00%
发文量
48
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