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  • Open Access

    ARTICLE

    Prediction of Ground Vibration Induced by Rock Blasting Based on Optimized Support Vector Regression Models

    Yifan Huang1, Zikang Zhou1,2, Mingyu Li1, Xuedong Luo1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3147-3165, 2024, DOI:10.32604/cmes.2024.045947 - 11 March 2024

    Abstract Accurately estimating blasting vibration during rock blasting is the foundation of blasting vibration management. In this study, Tuna Swarm Optimization (TSO), Whale Optimization Algorithm (WOA), and Cuckoo Search (CS) were used to optimize two hyperparameters in support vector regression (SVR). Based on these methods, three hybrid models to predict peak particle velocity (PPV) for bench blasting were developed. Eighty-eight samples were collected to establish the PPV database, eight initial blasting parameters were chosen as input parameters for the prediction model, and the PPV was the output parameter. As predictive performance evaluation indicators, the coefficient of More >

  • Open Access

    ARTICLE

    Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models

    Quang-Hieu Tran1,2,*, Hoang Nguyen1,2, Xuan-Nam Bui1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 2227-2246, 2023, DOI:10.32604/cmes.2022.021893 - 20 September 2022

    Abstract This study considered and predicted blast-induced ground vibration (PPV) in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms. Accordingly, four machine learning algorithms, including support vector regression (SVR), extra trees (ExTree), K-nearest neighbors (KNN), and decision tree regression (DTR), were used as the base models for the purposes of combination and PPV initial prediction. The bagging regressor (BA) was then applied to combine these base models with the efforts of variance reduction, overfitting elimination, and generating more robust predictive models, abbreviated as BA-ExTree, BAKNN, BA-SVR, and BA-DTR. It… More >

  • Open Access

    ARTICLE

    A Numerical Study on the Extinguishing Performances of High-Pressure Water Mist on Power-Transformer Fires for Different Flow Rates and Particle Velocities

    Yongheng Ku1, Jinguang Zhang2,3, Zhenyu Wang3,4, Youxin Li3,5, Haowei Yao3,5,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.17, No.6, pp. 1077-1090, 2021, DOI:10.32604/fdmp.2021.015779 - 08 September 2021

    Abstract In order to study the extinguishing performance of high-pressure-water-mist-based systems on the fires originating from power transformers the PyroSim software is used. Different particle velocities and flow rates are considered. The evolution laws of temperature around transformer, flue gas concentration and upper layer temperature of flue gas are analyzed under different boundary conditions. It is shown that the higher the particle velocity is, the lower the smoke concentration is, the better the cooling effect on the upper layer temperature of flue gas layer is, the larger the flow rate is and the better the cooling More >

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