A Machine Learning Model for the Prediction of Concrete Penetration by the Ogive Nose Rigid Projectile

Abstract

In recent years, research interest has been revolutionized to predict the rigid projectile penetration depth in concrete. The concrete penetration predictions persist, unsettled, due to the complexity of phenomena and the continuous development of revolutionized statistical techniques, such as machine learning, neural networks, and deep learning. This research aims to develop a new model to predict the penetration depth of the ogive nose rigid projectile into concrete blocks using machine learning. Genetic coding is used in Python programming to discover the underlying mathematical relationship from the experimental data in its non-dimensional form. A populace of erratic formulations signifies the rapport amid dependent parameters, such as the impact factor (I), the geometry function of the projectile (N), the empirical constant for concrete strength (S), the slenderness of the projectile (λ), and their independent objective variable, X/d, where X is the penetration depth of the projectile and d is the diameter of the projectile. Four genetic operations were used, including the crossover, sub-tree transfiguration, hoist transfiguration, and point transfiguration operations on supervised test datasets, which were divided into three categories, namely, narrow penetration (X/d < 0.5), intermediate penetration (0.5 ≤ X/d < 5.0), and deep penetration (X/d ≥ 5.0). The proposed model shows a significant relationship with all data in the category for medium penetration, where R2 = 0.88, and R2 = 0.96 for deep penetration. Furthermore, the proposed model predictions are also compared with the most commonly used NDRC and Li and Chen models. The outcome of this research shows that the proposed model predicts the penetration depth precisely, compared to the NDRC and Li and Chen models.

Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil

“…It adeptly models the Colebrook equation governing hydraulic flow friction in hydraulics, significantly improving accuracy in turbulent scenarios [58]. Symbolic regression is used in structural engineering to estimate seismic peak drift ratio, penetration depth into concrete blocks, shear capacity of concrete beams reinforced with steel fibers, fire response of concrete structures, seismic fragility analysis, remaining fatigue life, and shear resistance in bolted connections [59][60][61][62][63][64]. Notably, in some cases [59,63,64], symbolic regression equations outperformed traditional formulas.…”

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

“…From another point of view, concrete, the main construction material for most higher-scale applications, has been also a popular subject of AI research. A number of published papers has been focused on the construction of models to estimate the seismic peak drift ratio [ 158 , 159 ], the penetration depth into concrete blocks [ 160 ], the shear capacity of steel fiber-reinforced concrete beams, tracing fire response of concrete structures [ 161 ] or the seismic response through a fragility analysis [ 162 ], while others aim on the accurate description of remaining fatigue life [ 163 , 164 ] or bearing-type bolted connections’ shear resistance. One should note that, while the investigation of previously noted instances were conducted by modelling measurements, in several occasions [ 160 , 163 , 165 ] the generated equations outperformed conventional employed formulas.…”