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◆ Geosciences2025-11-19· Rock mass classification

Engineering Performance of Data-Driven Powder Factor Optimization in Tunnel Blasting Under Complex Geological Conditions

Azita Yusoof, Edy Tonnizam Mohamad, Mohd Saidin Misnan, Mohammad Afrazi, Danial Jahed Armaghani

原始摘要(英文原文)· Original abstract
This study introduces a cohesive and flexible methodology for optimizing tunnel blasting in the Gambang Tunnel 1 project, located in Malaysia’s geologically intricate tropical setting. The study examines the intrinsic difficulties of attaining safe and effective rock fragmentation in weathered, fractured, and large rock conditions commonly found along the tunnel alignment. The study utilized established rock mass classification methodologies, specifically the Q-system and Rock Mass Rating (RMR), to classify the tunnel face into specific geological zones. The optimization of the blast design aimed to determine appropriate powder factor ranges for each rock class by connecting rock mass quality with actual blast performance and fragmentation results. The results indicated that weathered zones (Q-value < 1.0) efficiently responded to powder factors of 0.65 to 0.85 kg/m3, whereas fractured zones (Q-value 1.0–4.0) attained optimal fragmentation with powder factors ranging from 0.85 to 1.10 kg/m3. The study emphasizes that incorporating rock mass categorization into blast design increases technical accuracy, minimizes over break and vibration, optimizes mucking efficiency, and fosters safer working environments. Furthermore, the methodology complies with regulatory standards established by authorities, ensuring that blasting activities are secure and subject to audit. This study offers pragmatic recommendations for forthcoming tunneling endeavors in analogous geological contexts, illustrating the significance of data-informed, site-specific blast design in fulfilling engineering, safety, and environmental goals.
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Engineering Performance of Data-Driven Powder Factor Optimization in Tunnel Blasting Under Complex Geological Conditions — 科研速览 Science Skim