Damage Prediction of Underground Engineering by Adaptive Network Based Fuzzy Inference System

Extensive research on the penetration damage caused by warheads has revealed limitations in studies that focus solely on a single geological material or concrete medium, as well as those that only evaluate penetration depth. Such approaches fail to adequately capture the damage inflicted by explosions and shock waves on underground tunnel engineering. To address these shortcomings and fulfill the need for accurate damage prediction in scenarios where underground engineering is impacted by warheads, this paper proposes a novel prediction approach based on an adaptive fuzzy neural network system. This system integrates penetration damage and shock wave overpressure damage to predict the overall level of damage to underground structures caused by various warheads, including ground-penetrating bombs. By employing the target vulnerability and warhead power stratification method, this research evaluates the degree of damage inflicted. The adaptive fuzzy neural network is utilized to predict potential damage to crucial elements within tunnel engineering, such as protective systems.