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  • Open Access

    ARTICLE

    Advanced Machine Learning Methods for Prediction of Blast-Induced Flyrock Using Hybrid SVR Methods

    Ji Zhou1,2, Yijun Lu3, Qiong Tian1,2, Haichuan Liu3, Mahdi Hasanipanah4,5,*, Jiandong Huang3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1595-1617, 2024, DOI:10.32604/cmes.2024.048398

    Abstract Blasting in surface mines aims to fragment rock masses to a proper size. However, flyrock is an undesirable effect of blasting that can result in human injuries. In this study, support vector regression (SVR) is combined with four algorithms: gravitational search algorithm (GSA), biogeography-based optimization (BBO), ant colony optimization (ACO), and whale optimization algorithm (WOA) for predicting flyrock in two surface mines in Iran. Additionally, three other methods, including artificial neural network (ANN), kernel extreme learning machine (KELM), and general regression neural network (GRNN), are employed, and their performances are compared to those of four More >

  • 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

    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

    A Secure and Cost-Effective Training Framework Atop Serverless Computing for Object Detection in Blasting Sites

    Tianming Zhang1, Zebin Chen1, Haonan Guo2, Bojun Ren1, Quanmin Xie3,*, Mengke Tian4,*, Yong Wang4

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2139-2154, 2024, DOI:10.32604/cmes.2023.043822

    Abstract The data analysis of blasting sites has always been the research goal of relevant researchers. The rise of mobile blasting robots has aroused many researchers’ interest in machine learning methods for target detection in the field of blasting. Serverless Computing can provide a variety of computing services for people without hardware foundations and rich software development experience, which has aroused people’s interest in how to use it in the field of machine learning. In this paper, we design a distributed machine learning training application based on the AWS Lambda platform. Based on data parallelism, the More >

  • Open Access

    ARTICLE

    Effect of Blasting Stress Wave on Dynamic Crack Propagation

    Huizhen Liu1,2, Duanying Wan3, Meng Wang3, Zheming Zhu3, Liyun Yang2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 349-368, 2024, DOI:10.32604/cmes.2023.028197

    Abstract Stress waves affect the stress field at the crack tip and dominate the dynamic crack propagation. Therefore, evaluating the influence of blasting stress waves on the crack propagation behavior and the mechanical characteristics of crack propagation is of great significance for engineering blasting. In this study, ANSYS/LS-DYNA was used for blasting numerical simulation, in which the propagation characteristics of blasting stress waves and stress field distribution at the crack tip were closely observed. Moreover, ABAQUS was applied for simulating the crack propagation path and calculating dynamic stress intensity factors (DSIFs). The universal function was calculated… 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

    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

    Optimization of the Air Deck Blasting Parameters on the Basis of the Holmquist-Johnson-Cook Constitutive Model

    Zuoming Yin1,*, Xuguang Wang2, Desheng Wang1, Zhiheng Dang1, Jianfeng Shao3

    FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.2, pp. 257-269, 2022, DOI:10.32604/fdmp.2022.017915

    Abstract The present study considers the so-called air deck blasting, one of the most commonly used techniques for the improvement of blasting efficiency in mining applications. In particular, it aims to improve the operating conditions of large-scale equipment, increase the efficiency of the slope enlarging process, and reduce the mining cost. These objectives are implemented through a two-fold approach where, first, a program for slope enlarging based on the middle air-deck charge blasting-loosening technology is proposed, and second, the physical mechanism responsible for the stress wave attenuation in the rock is analyzed in the framework of More >

  • Open Access

    ARTICLE

    Super Absorption Behavior of Chitosan by Freeze-Blasting in Different Alkaline Solvents

    Min Fan1,2,3*, Qiaoling Hu4

    Journal of Renewable Materials, Vol.6, No.5, pp. 457-463, 2018, DOI:10.7569/JRM.2017.634178

    Abstract The absorption behavior of chitosan in alkaline solution by freeze-blasting was studied. The influence of alkaline type, concentration, and small molecules was investigated, as well as the different roles of LiOH and NaOH in the absorption. Chitosan reached its maximum absorption rate when LiOH concentration was 4.8 wt% and NaOH 4.0 wt%, respectively. Chitosan showed better absorption capacity in LiOH solution. Urea could improve the absorption when its concentration was more than or equal to 4.0 wt%, and the improvement was greater in NaOH solution. Thiourea showed no obvious effect in LiOH solution, but showed More >

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