Efficacy of concrete constitutive models for bullet impact tests
Structures such as defenses/ammunition bunkers in the military and elsewhere are generally constructed at remote locations where concrete structures are more convenient to construct. Such protective structures in most cases are constructed from plain or reinforced concrete due to inherent ease of planning, transportation and construction. The design of these structures is based on field tests which ideally is required to be done for each caliber of weapon for which the protective structure is designed against. Such field tests are time consuming and uneconomical. A cost-effective alternative to field testing is the use of high fidelity physics based numerical modeling techniques. However, constitutive modeling of concrete when subjected to high velocity projectiles is very complex due to factors like material erosion and strain-rate effects. These factors lead to a highly non-linear response, hence, high accuracy of the concrete constitutive model is required to accurately simulate field test results. Commercial finite element software offer various concrete constitutive models. This paper reviews the concrete constitutive models available for modeling bullet impact. Experimental observations from bullet impact on plain concrete with a muzzle velocity (MV) of 900 m/s are presented and used to assess the concrete constitutive models in LS-DYNA. The importance of modeling parameters like strain-rate effects and erosion criteria have been reviewed. It was concluded that *MAT_CSCM (Mat_159) constitutive law was able to accurately simulate the field observations. The numerical results also suggest that an additional increase in the material strength to account for strain-rate effects is inappropriate.
|Conference||6th International Conference on Engineering Mechanics and Materials 2017|
Dua, A. (Alok), & Braimah, A. (2017). Efficacy of concrete constitutive models for bullet impact tests. In 6th International Conference on Engineering Mechanics and Materials 2017 (pp. 402–411).