Edy Tonnizam Mohamad

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.

One of the most undesirable consequences induced by blasting in open-pit mines and civil activities is flyrock. Furthermore, the production of oversize boulders creates many problems for the continuation of the work and usually imposes additional costs on the project. In this way, the breakage of oversize boulders is associated with throwing small fragments particles at high speed, which can lead to serious risks to human resources and infrastructures. Hence, the accurate prediction of flyrock induced by boulder blasting is crucial to avoid possible consequences and its’ environmental side effects. This study attempts to develop an optimized artificial neural network (ANN) by particle swarm optimization (PSO) and jellyfish search algorithm (JSA) to construct the hybrid models for anticipating flyrock distance resulting in boulder blasting in a quarry mine. The PSO and JSA algorithms were used to determine the optimum values of neurons’ weight and biases connected to neurons. In this regard, a database involving 65 monitored boulders blasting for recording flyrock distance was collected that comprises six influential parameters on flyrock distance, i.e., hole depth, burden, hole angle, charge weight, stemming, and powder factor and one target parameter, i.e., flyrock distance. The ten various models of ANN, PSO–ANN, and JSA–ANN were established for estimating flyrock distance, and their results were investigated by applying three evaluation indices of coefficient of determination (R²), root mean square error (RMSE) and value accounted for (VAF). The results of the calculation of evaluation indicators revealed that R², values of (0.957, 0.972 and 0.995) and (0.945, 0.954 and 0.989) were determined to train and test of proposed predictive models, respectively. The yielded results denoted that although ANN model is capable of anticipating flyrock distance, the hybrid PSO–ANN and JSA–ANN models can anticipate flyrock distance with more accuracy. Furthermore, the performance and accuracy level of the JSA–ANN predictive model can estimate better compared to ANN and PSO–ANN models. Therefore, the JSA–ANN model is identified as the superior predictive model in estimating flyrock distance induced from boulder blasting. In the final, a sensitivity analysis was conducted to determine the most influential parameters in flyrock distance, and the results showed that charge weight, powder factor, and hole angle have a high impact on flyrock changes.