###### 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 (\(\lambda\)), 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 configuration, host transformation, and point transfiguration operations on supervised test datasets, which were divided into three categories, namely, narrow penetration (\(X/d<0.5\)), intermediate penetration (\(0.5\leq X/d<5.0\)), and deep penetration (\(X/d\geq 5.0\)). The proposed model shows a significant relationship with all data in the category for medium penetration, where R\({}^{2}\) = 0.88, and R\({}^{2}\) = 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. caption <.> 2022  
Footnote †: journal: Applied Sciences  
The historical penetration depth prediction model, that was developed based on experimental data, showed that the USA most frequently used the Petry model/the modified Petry model to predict penetration depth into a concrete block [1,3]. The Petry model was initially developed in 1910 and is considered the oldest available empirical model in literature [1,3]. Later, the Petry model was modified by [PERSON] in the S.I unit [1,3]. In 1941, the Ballistic Research Laboratory (BRL) proposed the penetration prediction model [1,3]. Commencing in 1943, the Army Corp of Engineers also established the ACE model [1,3]. In 1946, the National Defense Research Committee (NDRC) proposed a model founded on the ACE model [1,3,4] The limitation of the NDRC hypothesis was that the NDRC model was used for rear penetration depths [1,3]. The NDRC also suggested a nose shape factor N* for projectiles, as mentioned in Table A1. Later, Ammann and Whitney's model was anticipated to foretell the penetration of concrete traceable to the effect of strenuously precipitated particles at comparatively sophisticated velocities [1,3]. According to [PERSON] [5], the Ammann and Whitney model can be used for velocities over 1000 ft/s [1,3]. [PERSON] further continued research in the British Road Research Laboratory in the United Kingdom, using experimental data acquired from World War II, and developed a prediction model for penetration depth [1,3]. [PERSON] altered the NDRC model, employing reversion regarding the modulus of elasticity (E), where E is the modulus of elasticity of the projectile, and Es is the modulus of elasticity of steel [1,3]. In the United Kingdom, [PERSON] recommended a UKAEA model by reforming the NDRC model derived from wide-ranging research on nuclear power plant structures [1,3]. The Haldar-Hamieh model [1,3,6] of penetration depth relies on the dimensionless impact factor (Ia), where and N* is the nose shape factor [1,3,6]. [PERSON] and [PERSON] improved the impact factor (Ia), familiarized by [PERSON] and [PERSON], using regression on [PERSON]'s experimental data [1,3].