Article
Computer Science, Interdisciplinary Applications
Mahdi Hasanipanah, Behrooz Keshtegar, Duc-Kien Thai, Nguyen-Thoi Troung
Summary: A novel hybrid artificial neural network (ANN) model based on adaptive musical inspired optimization method is proposed for accurate prediction of blast-induced flyrock. The model showed better predictive performance compared to other models.
ENGINEERING WITH COMPUTERS
(2022)
Article
Mathematics
Xianan Wang, Shahab Hosseini, Danial Jahed Armaghani, Edy Tonnizam Mohamad
Summary: Flyrock induced by blasting is a highly undesirable consequence in open-pit mines and civil activities. This study developed an optimized artificial neural network (ANN) model using particle swarm optimization (PSO) and jellyfish search algorithm (JSA) to predict flyrock distance resulting from boulder blasting. Results showed that the JSA-ANN model outperformed the ANN and PSO-ANN models in terms of accuracy.
Article
Engineering, Geological
Shahab Hosseini, Rashed Poormirzaee, Mohsen Hajihassani, Roohollah Kalatehjari
Summary: This paper proposes a novel method for predicting flyrock distance in open-pit mine blasting using the integration of artificial neural network and fuzzy cognitive map (FCM) with Z-number reliability information. The developed model, called artificial causality-weighted neural networks based on reliability (ACWNNsR), is proven to result in more accurate prediction compared to other neural network models.
ROCK MECHANICS AND ROCK ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Hadi Fattahi, Mahdi Hasanipanah
Summary: The study developed a new integrated intelligent model for predicting flyrock in open-pit mines, using ANFIS in combination with GOA and CA to achieve high accuracy in flyrock approximation.
ENGINEERING WITH COMPUTERS
(2022)
Article
Environmental Sciences
Jiandong Huang, Junhua Xue
Summary: This study introduces a new model for determining critical flyrock events in mines. By optimizing the support vector regression function using the human learning optimization algorithm, flyrock was predicted and optimized using a field database. The results indicate that the model has high accuracy and can find optimal options under different conditions.
ENVIRONMENTAL EARTH SCIENCES
(2022)
Article
Geosciences, Multidisciplinary
Diyuan Li, Mohammadreza Koopialipoor, Danial Jahed Armaghani
Summary: The study used hybrid intelligence techniques to examine the parameters affecting flyrock, optimized models with various algorithms, and ultimately selected the FA-ANN combination model as the best choice for accurate prediction of flyrock distance.
NATURAL RESOURCES RESEARCH
(2021)
Article
Engineering, Geological
D. P. Blair
Summary: This paper presents a probabilistic analysis of flyrock range produced by blasting based on measured data from 6 sites. A two-dimensional ballistics model is used to predict flyrock ranges, and the results are consistent with measured ranges. The model considers random launch angle and size of flyrock fragments, as well as the influence of fragment shapes and wind forces. The study also constructs pseudo-3D and fully 3D models to analyze the impact of pit walls, prevailing wind, and surface topography on flyrock range. The research is significant for the protection of Heritage Sites from flyrock impact.
INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES
(2022)
Article
Green & Sustainable Science & Technology
Lalit Singh Chouhan, Avtar K. Raina, V. M. S. R. Murthy, Mohanad Muayad Sabri Sabri, Edy Tonnizam Mohamad, Ramesh Murlidhar Bhatawdekar
Summary: This study investigated the impact of different blasthole firing patterns on rock fragmentation, revealing that the V-type firing pattern can significantly reduce fragment size, primarily due to collision of rock fragments during flight.
Article
Green & Sustainable Science & Technology
Xiaohua Ding, Mehdi Jamei, Mahdi Hasanipanah, Rini Asnida Abdullah, Binh Nguyen Le
Summary: Using explosive material to fragment rock masses is a common method in surface mines, but it can lead to environmental problems such as flyrock. This study develops hybrid models for predicting flyrock using neural networks and optimization algorithms. The models were tested using data from granite quarry sites in Malaysia, and the results showed that the LSSVM-WOA model was the most accurate in predicting flyrock values.
Article
Chemistry, Multidisciplinary
Mojtaba Yari, Danial Jahed Armaghani, Chrysanthos Maraveas, Alireza Nouri Ejlali, Edy Tonnizam Mohamad, Panagiotis G. Asteris
Summary: Blasting operations could cause damage to equipment and surrounding areas, with flyrock being the most important environmental issue. Accurate prediction of flyrock is crucial for identifying the safety zone of a blasting area. This study introduces tree-based techniques, including decision tree, random forest, extreme gradient boosting, and adaptive boosting, for accurate flyrock prediction. Among these techniques, AdaBoost shows the highest accuracy in estimating flyrock.
APPLIED SCIENCES-BASEL
(2023)
Article
Mechanics
Noureddine Fahem, Idir Belaidi, Abdelmoumin Oulad Brahim, Mohammad Noori, Samir Khatir, Magd Abdel Wahab
Summary: This work presents experimental and numerical studies on the effect of porosity on the mechanical properties of Glass Fiber Reinforced Polymer (GFRP). The study found that increasing the size of air bubbles significantly reduces the load in both tensile and bending cases. Additionally, an Artificial Neural Network-Enhanced Jaya Algorithm (ANN-E JAYA) is used to predict the reduction of tensile load, and compared with other methods, showing slightly higher accuracy for ANN-E JAYA.
COMPOSITE STRUCTURES
(2023)
Article
Computer Science, Artificial Intelligence
K. Aditya Shastry, H. A. Sanjay
Summary: Crop yield prediction is crucial in agriculture, but many developing countries still rely on manual methods which are inefficient and error-prone. To address this issue, a hybrid prediction strategy is proposed, incorporating weighted principal component analysis and artificial neural network to enhance the accuracy of crop yield prediction.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Mohammad Rezaei, Masoud Monjezi, Fariborz Matinpoor, Shadman Mohammadi Bolbanabad, Hazhar Habibi
Summary: This study introduces a simulation model that integrates CART analysis and PCA to predict flyrock occurrences during mine blasting operations. Using the Sangan iron ore mine as a case study, the researchers propose 21 key guidelines for designing blasting patterns and highlight the varying importance of input variables through sensitivity analysis.
SIMULATION MODELLING PRACTICE AND THEORY
(2023)
Article
Geosciences, Multidisciplinary
Jinbi Ye, Mohammadreza Koopialipoor, Jian Zhou, Danial Jahed Armaghani, Xiaoli He
Summary: Novel techniques were developed in this study to predict and simulate the flyrock phenomenon in mines due to blasting, with GP and RF models showing good performance in predicting flyrock distance in different quarry sites in Malaysia. Monte Carlo simulation was used to analyze flyrock risk, showing that only 10% of flyrock events will exceed 290 m, which can help in identifying blast safety areas for blasting operations.
NATURAL RESOURCES RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Wusi Chen, Mahdi Hasanipanah, Hima Nikafshan Rad, Danial Jahed Armaghani, M. M. Tahir
Summary: This study examines the suitability of hybridizing different optimization algorithms with data-driven models to predict ground vibration, and compares the performance of various models through a case study, with the MFA-SVR model being the most accurate.
ENGINEERING WITH COMPUTERS
(2021)
Article
Engineering, Geological
Ling Fan, Zhuyan Zheng, Shuquan Peng, Jian Zhou, Tianli Shen, Hua Wan, Hongling Ma
Summary: Both wall movement mode and soil arching are important factors in determining the active earth pressure on rigid retaining wall. This study proposes a method that considers the arching effects under different wall movement modes. The improved method shows better agreement with experimental results and can be used in engineering practice. The application point of the active earth pressure on the retaining wall changes with the consideration of soil arching effect, and the soil arching effect becomes more notable with a larger equivalent friction coefficient.
INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS
(2023)
Article
Mechanics
Shiming Wang, Jiaqi Wang, Xianrui Xiong, Zhongjun Ren, Lei Weng, Manoj Khandelwal, Jian Zhou
Summary: Thin Spray-On Liner (TSL) is widely used in underground engineering support like mining due to its ease of operation and effective support. A series of uniaxial compression experiments were conducted on TSL-coated specimens under static and dynamic loads to study the support effect. The results showed that the TSL improves the strength and peak strain of sandstone, with a more significant improvement observed with increased coating thickness. TSL provides good bonding ability and tensile properties to support the rock. Simulation results confirmed the experimental findings, demonstrating that TSL delays failure time and reduces cracks.
MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES
(2023)
Article
Chemistry, Multidisciplinary
Lili Zhang, Mengdi Guo, Jian Zhou, Cong Fang, Xiaoyan Sun
Summary: This study successfully synthesized three isostructural Cu-6 nanoclusters ligated with different mono-thiol ligands, providing an ideal platform to investigate the role of ligands. The structural evolution process of Cu-6 nanoclusters was mapped out using mass spectrometry for the first time. It was found that even with atomic differences, the ligands can profoundly affect the assembly processes, chemical properties, atomic structures, and catalytic activities of Cu nanoclusters. Ion-molecule reactions combined with density functional theory calculations demonstrated that defective sites on the ligand can significantly contribute to the activation of molecular oxygen. This study provides fundamental insights into the ligand effect for the design of efficient Cu nanocluster-based catalysts.
Article
Chemistry, Physical
Peixi Yang, Chuanqi Li, Yingui Qiu, Shuai Huang, Jian Zhou
Summary: This study utilized three meta-heuristic optimization algorithms to select the optimal hyperparameters of the random forest model for predicting the punching shear strength of FRP-RC beams. The ALO-RF model with a population size of 100 showed the best prediction performance, and adjusting the slab's effective depth effectively controlled the punching shear strength. Furthermore, the hybrid machine learning model optimized by metaheuristic algorithms outperformed traditional models in terms of prediction accuracy and error control.
Article
Chemistry, Physical
Chuanqi Li, Xiancheng Mei, Daniel Dias, Zhen Cui, Jian Zhou
Summary: This paper proposes a novel hybrid artificial neural network model optimized using a reptile search algorithm with circle mapping to predict the compressive strength of rice husk ash concrete. The proposed model achieved the most satisfactory prediction accuracy regarding R-2 (0.9709), VAF (97.0911%), RMSE (3.4489), and MAE (2.6451), outperforming previously developed models.
Article
Computer Science, Interdisciplinary Applications
Jian Zhou, Zhenyu Wang, Chuanqi Li, Wei Wei, Shiming Wang, Danial Jahed Armaghani, Kang Peng
Summary: Rock mass fractures have a significant impact on the shear behavior of natural rocks, and understanding the shear parameters of these fractures is important for maintaining the stability of underground structures. A novel approach combining the random forest (RF) model and two optimization algorithms (the Sine Cosine Algorithm (SCA) and the whale optimization algorithm (WOA)) was proposed to predict the shear strength, peak shear displacement, and dilation angles of rock fractures. The prediction performance indicated that the SCA-RF and WOA-RF models achieved high accuracy in predicting the shear strength and dilation angles of rock fractures.
JOURNAL OF COMPUTATIONAL SCIENCE
(2023)
Article
Chemistry, Multidisciplinary
Chuanqi Li, Daniel Dias
Summary: This paper utilizes four metaheuristic optimization algorithms to optimize the random forest model for predicting the rock elasticity modulus (EM), and evaluates the predictive performance of different models. The results show that the PRO-RF model achieves the best prediction accuracy, and porosity (Pn) is the most important variable for predicting the rock EM. This study provides a good example for the subsequent application of soft techniques in EM and other important rock parameter estimations.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Geological
Jian Zhou, Rui Zhang, Yingui Qiu, Manoj Khandelwal
Summary: Rock strength is crucial for underground projects, and this study utilizes a GEP algorithm-based model to predict true triaxial strength, considering the influence of rock genesis. The proposed criterion shows superior prediction accuracy and stability compared to existing models.
JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Hai Yu, Lanlan Qin, Jian Zhou
Summary: This study used molecular dynamics simulations to investigate the effect of oil polarity on the orientation and conformation of proteins at oil-water interfaces. It was found that the oil polarity can influence the protein adsorption orientation and conformation by modulating intermolecular interactions. Therefore, the stability and activity of proteins can be regulated by changing the oil polarity.
Article
Computer Science, Interdisciplinary Applications
Jian Zhou, Yulin Zhang, Chuanqi Li, Weixun Yong, Yingui Qiu, Kun Du, Shiming Wang
Summary: This research introduces a groundbreaking intelligent model called the GWO-RF model for predicting water inflow during tunnel construction. By combining the capabilities of Grey Wolf Optimization (GWO) with the Random Forest (RF) algorithm, this model aims to enhance the accuracy and effectiveness of water inflow prediction, ultimately improving safety measures in tunnel construction projects. The GWO-RF model outperforms other ensemble models in terms of predictive accuracy, making it invaluable for tunneling projects.
EARTH SCIENCE INFORMATICS
(2023)
Article
Energy & Fuels
Minghui Liu, Xinyao Luo, Ruiyang Bi, Jian Zhou, Kun Du
Summary: A series of uniaxial compression tests were conducted on bedded limestone, phyllite, and shale specimens to investigate their mechanical behavior and crack evolution properties. The experimental results showed that the mechanical properties of bedded rocks were greatly influenced by the cementation types of bedding planes. A novel strength criterion for bedded rocks and a crack classification criterion were proposed.
GEOMECHANICS FOR ENERGY AND THE ENVIRONMENT
(2023)
Article
Energy & Fuels
Kun Du, Xinyao Luo, Songge Yang, Jahed Armaghani Danial, Jian Zhou
Summary: This paper studies the rockburst tendency indices through laboratory tests and finds that the WPET and AEF can reflect the energy and rockburst proneness of hard rock. Loading rates of the two energy indices are determined and the determination and calculation process are clarified. The results show that the energy index of rock cannot fully reflect the rockburst proneness and a displacement control mode of 0.3-1 mm/min and a load control mode of 50-140 kN/min are recommended for hard rocks.
GEOMECHANICS FOR ENERGY AND THE ENVIRONMENT
(2023)
Article
Mathematics
Zhi Yu, Chuanqi Li, Jian Zhou
Summary: This study improves the prediction accuracy of tunnel boring machine (TBM) performance by employing supervised learning methods and swarm intelligence algorithms, with experimental results showing that this approach can enhance the accuracy of the prediction models.
Article
Mining & Mineral Processing
Chuanqi Li, Jian Zhou, Kun Du, Daniel Dias
Summary: This paper aims to develop hybrid support vector machine (SVM) models improved by three metaheuristic algorithms known as grey wolf optimizer (GWO), whale optimization algorithm (WOA) and sparrow search algorithm (SSA) for predicting the hard rock pillar stability. The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices. However, the performance of the SSA-SVM model for predicting the unstable pillar is not as good as those for stable and failed pillars.
INTERNATIONAL JOURNAL OF MINING SCIENCE AND TECHNOLOGY
(2023)
