Abstract
Despite the availability of large number of empirical and semi-empirical models, the problem of penetration depth prediction for concrete targets has remained inconclusive partly due to the complexity of the phenomenon involved and partly because of the limitations of the statistical regression employed. Conventional statistical analysis is now being replaced in many fields by the alternative approach of neural networks. Neural networks have advantages over statistical models like their data-driven nature, model-free form of predictions, and tolerance to data errors. The objective of this study is to reanalyze the data for the prediction of penetration depth by employing the technique of neural networks with a view towards seeing if better predictions are possible. The data used in the analysis pertains to the ogive-nose steel projectiles on concrete targets and the neural network models result in very low errors and high correlation coefficients as compared to the regression based models.
Highlights
Concrete has been widely used over many years by military and civil engineers in the design and construction of protective structures to resist impact and explosive loads
Empirical formulae on penetration depth, perforation limit and scabbing limit in a thick concrete target had been reviewed by Kennedy (1976), which covered most of the test data in the US and European till 1970s
A regression based empirical model has been developed based upon the data available in literature for the prediction of penetration depth in concrete target by ogive-nose steel projectiles
Summary
Concrete has been widely used over many years by military and civil engineers in the design and construction of protective structures to resist impact and explosive loads. Potential missiles include kinetic munitions, vehicle and aircraft crashes, fragments generated by military and terrorist bombing, fragments generated by accidental explosions and other events (e.g. failure of a pressurized vessel, failure of a turbine blade or other high-speed rotating machines), flying objects due to natural forces (tornados, volcanos, meteoroids), etc. These missiles vary broadly in their sizes and shapes, impact velocities, hardness, rigidities, impact attitude (i.e. obliquity, yaw, tumbling, etc.) and produce a wide spectrum of damage in the target (Li et al, 2005). Among the most commonly used formulae are the Pettry formula, the Army Corps of Engineers formula (ACE), Ballistic Research Laboratory formula (BRL) and the National Defence Research Committee (NDRC) formula
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