Projectile Penetration into Calcareous Sand Subgrade Airport Runway Pavement with Genetic Algorithm Optimization

Area of Science:

• Civil Engineering
• Materials Science
• Computational Mechanics

Background:

• Airport runway pavements are critical infrastructure vulnerable to projectile impacts.
• Assessing damage from cluster munitions requires understanding projectile penetration dynamics.

Purpose of the Study:

• To develop a numerical model for assessing airport runway pavement damage under projectile impact.
• To investigate projectile penetration into concrete pavement and calcareous sand subgrade.
• To establish a method for predicting and optimizing penetration depth.

Main Methods:

• Discrete Element Method (DEM) for modeling concrete and calcareous sand behavior.
• Calibration of DEM parameters through uniaxial and triaxial compression simulations.
• Validation of DEM model with single-layer penetration tests.
• Development of a back-propagation (BP) neural network proxy model for DOP prediction.
• Coupling genetic algorithm with the BP model for intelligent optimization.

Main Results:

• Validated DEM model with relative error ≤ 5.6% for projectile penetration.
• Simulations provided depth of penetration (DOP) data for various impact conditions.
• BP neural network accurately predicted DOP, enabling rapid forecasting.
• Genetic algorithm optimized pavement penetration analysis.

Conclusions:

• The validated DEM model provides a reliable tool for deep penetration damage assessment.
• The integrated BP neural network and genetic algorithm approach enables efficient and intelligent optimization of runway pavement penetration.
• Findings contribute to understanding and mitigating runway damage from projectile impacts.