Assessment of peak particle velocity of blast vibration using hybrid soft computing approaches

作     者：Yuan, Haiping Zou, Yangyao Li, Hengzhe Ji, Shuaijie Gu, Ziang He, Liu Hu, Ruichao 

作者机构：Hefei Univ Technol Coll Civil Engn Hefei 230009 Peoples R China State Key Lab Min Induced Response & Disaster Prev Huainan 232001 Peoples R China 

出 版 物：《JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING》 (J. Comput. Des. Eng.)

年 卷 期：2025年第12卷第2期

页      面：154-176页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China National Natural Science Foundation of China [SKLMRDPC22KF02] State Key Laboratory Open Funding Project of Mining similar to Induced Response and Disaster Prevention and Control in Deep Coal Mines (Anhui University of Science and Technology) 

主　　题：blast vibration peak particle velocity catboost algorithm variational mode decomposition improved hippopotamus optimization SHapley Additive exPlanations 

摘      要：Blasting vibration is a major adverse effect in rock blasting excavation, and accurately predicting its peak particle velocity (PPV) is vital for ensuring engineering safety and risk management. This study proposes an innovative IHO-VMD-CatBoost model that integrates variational mode decomposition (VMD) and the CatBoost algorithm, with hyperparameters globally optimized using the improved hippopotamus optimization (IHO) algorithm. Compared to existing models, the proposed method improves feature extraction from vibration signals and significantly enhances prediction accuracy, especially in complex geological conditions. Using measured data from open-pit mine blasting, the model extracts key features such as maximum section charge, total charge, and horizontal distance, achieving superior performance compared to 13 traditional models. It reports a root mean square error of 0.28 cm/s, a mean absolute error of 0.17 cm/s, an index of agreement of 0.993, and a variance accounted for value of 97.28%, demonstrating superior prediction accuracy, a high degree of fit with observed data, and overall robustness in PPV prediction. Additionally, analyses based on the SHapley Additive Explanations framework provide insights into the complex nonlinear relationships between factors like horizontal distance and maximum section charge, improving the model s interpretability. The model demonstrates robustness, stability, and applicability in various tests, confirming its reliability in complex engineering scenarios, and offering a valuable solution for safe mining and optimized blasting design.