Hybrid Metaheuristic Optimisation Algorithms with Least-Squares Support Vector Machine and stagnation counter for prediction vibration induced by Tunnel blasting

Ground vibrations induced by tunnel blasting can severely impact nearby infrastructure. Therefore, accurate prediction of peak particle velocity (PPV) is essential for ensuring structural safety and engineering sustainability. This study proposes a PPV prediction model based on the Least Squares Support Vector Machine (LSSVM), optimised by a novel Adaptive Stagnation Whale Optimisation Algorithm (ASWOA) . To address the limitations of the conventional WOA, a regionally dynamic threshold adjustment strategy based on stagnation counter is proposed. recording the number of consecutive iterations without improvement, and calculate the dynamic threshold by combining the decay coefficient to control the rate of change, thereby adaptively adjusts the trigger probability of spiral updates, improving global search capability. Compared with others models, the proposed method not only improves prediction accuracy but also ensure higher reliability in vibration prediction. Moreover, it provides an efficient tool for vibration control in tunnel blasting under complex geological conditions.