Mojtaba Yari^1,*, Manoj Khandelwal², Payam Abbasi³, Evangelos I. Koutras⁴, Danial Jahed Armaghani^5,*, Panagiotis G. Asteris⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2207-2238, 2024, DOI:10.32604/cmes.2024.048071

Abstract Geo-engineering problems are known for their complexity and high uncertainty levels, requiring precise definitions, past experiences, logical reasoning, mathematical analysis, and practical insight to address them effectively. Soft Computing (SC) methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements. Unlike traditional hard computing approaches, SC models use soft values and fuzzy sets to navigate uncertain environments. This study focuses on the application of SC methods to predict backbreak, a common issue in blasting operations within mining and civil projects. Backbreak, which refers to More >

Ji Zhou^1,2, Yijun Lu³, Qiong Tian^1,2, Haichuan Liu³, Mahdi Hasanipanah^4,5,*, Jiandong Huang^3,*

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 >

Yifan Huang¹, Zikang Zhou^1,2, Mingyu Li¹, Xuedong Luo^1,*

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 >

Tianming Zhang¹, Zebin Chen¹, Haonan Guo², Bojun Ren¹, Quanmin Xie^3,*, Mengke Tian^4,*, 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 >

Huizhen Liu^1,2, Duanying Wan³, Meng Wang³, Zheming Zhu³, Liyun Yang^2,*

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 >

Quang-Hieu Tran^1,2,*, Hoang Nguyen^1,2, Xuan-Nam Bui^1,2

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 >

Zuoming Yin^1,*, 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 >

Min Fan^1,2,3*, 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 >