Article
Chemistry, Physical
Takashi Tsuchimochi, Yoohee Ryo, Seiichiro L. Ten-no, Kazuki Sasasako
Summary: In this study, several improvements are made to the Quantum Imaginary Time Evolution (QITE) algorithm, with a focus on molecular applications. By analyzing the derivation of the QITE equation and suggesting a theoretically grounded modification, our results demonstrate the soundness of the derived equation and its ability to better approximate imaginary time propagation. We also discuss accurately estimating the norm of an imaginary-time-evolved state and its application to excited state calculations. Additionally, the folded-spectrum QITE scheme is proposed as a straightforward extension for general excited-state simulations.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2023)
Article
Chemistry, Physical
Takashi Tsuchimochi, Yoohee Ryo, Seiichiro L. Ten-no, Kazuki Sasasako
Summary: Quantum imaginary time evolution (QITE) is a hybrid algorithm that can guarantee reaching the lowest state of a system. This study improves upon QITE, specifically for molecular applications. The derivation of the QITE equation is analyzed step-by-step, and a theoretically well-founded modification is proposed. The results demonstrate the effectiveness of the derived equation, providing a better approximation for imaginary time propagation. Additionally, accurate estimation of the norm of an imaginary-time-evolved state is discussed and applied in excited state calculations using the quantum Lanczos algorithm. The folded-spectrum QITE scheme is also introduced as an extension for general excited-state simulations.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2023)
Article
Engineering, Multidisciplinary
Anurag Agrawal, B. S. Choudhary, V. M. S. R. Murthy, Sunny Murmu
Summary: Traditional blasting techniques have been restructured to minimize ground vibrations and protect structures, while maintaining production efficiency.
Article
Engineering, Geological
Yan-Gang Zhao, Rui Zhang, Haizhong Zhang
Summary: Probabilistic prediction of ground-motion intensity in regions lacking strong ground-motion records is a vital issue for seismic structural design. A novel method is proposed in this study, which uses a Fourier amplitude spectral (FAS) model to express the seismic transmission process and applies random vibration theory to obtain the ground-motion intensities of PGA or SA. The exceedance probability is calculated based on Monte Carlo simulations. This method is convenient for regions lacking strong ground-motion records due to the linear system theory and the requirement of fewer ground-motion records for FAS model determination.
SOIL DYNAMICS AND EARTHQUAKE ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Helong Yu, Shimeng Qiao, Ali Asghar Heidari, Ayman A. El-Saleh, Chunguang Bi, Majdi Mafarja, Zhennao Cai, Huiling Chen
Summary: An enhanced Harris hawks optimization algorithm based on Laplace crossover and random replacement strategy is proposed in this paper to improve the local exploitation ability and slow convergence speed problem of the original algorithm. Experimental results show that the algorithm has strong optimization ability, high convergence accuracy, and fast convergence speed.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2022)
Article
Engineering, Geological
Bhatawdekar Ramesh Murlidhar, Hoang Nguyen, Jamal Rostami, XuanNam Bui, Danial Jahed Armaghani, Prashanth Ragam, Edy Tonnizam Mohamad
Summary: This study examined and estimated the flyrock distance induced by blasting using five artificial intelligent algorithms, with the Harris Hawks optimization-based MLP (HHO-MLP) showing the best performance among all models. Data was collected from 152 blasting events in three open-pit granite mines in Johor, Malaysia.
JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING
(2021)
Article
Biodiversity Conservation
Zizhao Li, Shoudong Bi, Shuang Hao, Yuhuan Cui
Summary: This study investigates the impact of different variables on aboveground biomass (AGB) inversion in forests and evaluates the resulting model uncertainties. The results indicate that the model based on multiple variables performs the best, highlighting its importance in improving the accuracy of AGB estimates.
ECOLOGICAL INDICATORS
(2022)
Article
Engineering, Electrical & Electronic
Lingzhi Yi, Ganlin Jiang, Guoyong Zhang, Wenxin Yu, You Guo, Tao Sun
Summary: In this paper, a fault diagnosis method for oil-immersed transformers based on improved Harris Hawks optimized random forest is proposed. The method adjusts algorithm parameters, energy factors, and introduces mutation method to establish a fault diagnosis model with high accuracy for oil-immersed transformers.
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Ozan Akdag, Abdullah Ates, Celaleddin Yeroglu
Summary: The paper proposes modifications to the HHO algorithm and applies it to the OPF problem, using seven types of random distribution functions to demonstrate their impact on performance. The results show that the modified HHO algorithm achieved satisfactory outcomes in minimizing the total fuel cost of the power system, active/reactive power losses, and emissions.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Engineering, Civil
Ziqi Wang
Summary: This study addresses the fundamental limitation of equivalent linearization methods in nonlinear random vibration analysis, proposing a method to construct an estimator that converges on the nonlinear system solution using a limited number of nonlinear system simulations and optimizing the equivalent linear system to approach the nonlinear system solution quickly, especially for rare event probabilities.
Article
Geosciences, Multidisciplinary
Canxin Yu, Mohammadreza Koopialipoor, Bhatawdekar Ramesh Murlidhar, Ahmed Salih Mohammed, Danial Jahed Armaghani, Edy Tonnizam Mohamad, Zengli Wang
Summary: The study uses machine learning techniques to control and predict ground vibrations resulting from mine blasting, with the GOA-ELM and HHO-ELM models providing higher performance capacity and showing more accurate results in testing compared to the HHO-ELM model.
NATURAL RESOURCES RESEARCH
(2021)
Article
Engineering, Multidisciplinary
Lin Huang, Qiang Fu, Nan Tong
Summary: This paper proposes an Improved Harris Hawks Optimization (IHHO) algorithm to address the issues of the original Harris Hawks Optimization (HHO) algorithm. The improvements include the addition of a circle map, reduction of population boundary number, introduction of a random-oriented strategy, improved sine-trend search strategy, and a nonlinear jump strength. The simulation results demonstrate the competitiveness and effectiveness of the Improved Harris Hawks Optimization algorithm in accuracy, convergence speed, and non-origin symmetric interval search efficiency.
Article
Mathematics, Applied
Fernando A. Morales, Jorge M. Ramirez, Edgar A. Ramos
Summary: This study presents a mathematical analysis of the Isolation Random Forest Method (IRF Method) for anomaly detection, proving its convergence and proposing an improved version. It also provides a criterion for choosing the number of sampled trees to ensure confidence intervals of the numerical results.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2023)
Article
Computer Science, Information Systems
Zhiyu Zhou, Dexin Liu, Yaming Wang, Zefei Zhu
Summary: This study proposes a method for illumination correction using an improved RVFL algorithm based on MVO-HHO. The algorithm determines the optimal parameters and restores the image through diagonal transformation. Experimental results show that this method has high accuracy and significant differences compared to other algorithms.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
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
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
Public, Environmental & Occupational Health
Jian Zhou, Yuxin Chen, Hui Chen, Manoj Khandelwal, Masoud Monjezi, Kang Peng
Summary: Pillar stability is crucial for safe work in mines. Accurate estimation of induced stresses in pillars is important for design and guaranteeing stability. Machine learning algorithms, such as back-propagation neural network (BPNN), have been successfully applied to pillar stability assessment with high accuracy.
FRONTIERS IN PUBLIC HEALTH
(2023)
Article
Metallurgy & Metallurgical Engineering
Jian Zhou, Peixi Yang, Pingan Peng, Manoj Khandelwal, Yingui Qiu
Summary: Three hybrid support vector machine (SVM) models optimized by particle swarm optimization (PSO), Harris hawk optimization (HHO), and moth flame optimization (MFO) are proposed to predict rockburst hazard level (RHL), achieving better accuracy and performance than the unoptimized SVM model.
MINING METALLURGY & EXPLORATION
(2023)
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
Kun Du, Songge Yang, Jian Zhou, Lichang Wang
Summary: It is important to study the evaluation indexes and classification criteria of the bursting liability of hard rocks for the prediction and prevention of rockbursts. In this study, the rockburst tendency was evaluated using the brittleness indicator (B2) and the strength decrease rate (SDR). The measuring methods and classification criteria were analyzed, and four grades of rockburst tendency were defined based on the test results.
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
Metallurgy & Metallurgical Engineering
Chao Chen, Jian Zhou
Summary: An empirical classification model based on elastic energy index (W-et) and impact energy index (K-c) was established to analyze the risk level of coal burst. The Kriging method was used to display the classification boundaries and distribution characteristics of coal burst liabilities (CBLs) on a 2D chart. The reliability of the spatial interpolation model was further validated using 43 test samples. Results showed that the Kriging model had a classification accuracy of 91% and outperformed other uncertainty-based methods. This model can be a valuable tool for geological hazard prevention and initial design.
JOURNAL OF CENTRAL SOUTH UNIVERSITY
(2023)
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
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)
Article
Geosciences, Multidisciplinary
Chuanqi Li, Jian Zhou, Daniel Dias, Kun Du, Manoj Khandelwal
Summary: In this study, 386 rock samples were used to predict the uniaxial compressive strength (UCS) using various empirical equations and artificial intelligence methods. The evaluation results showed that the artificial intelligence models outperformed the empirical approaches, especially the LSO-RF model. The porosity (Pn) was identified as the most important input variable for predicting UCS.
Article
Engineering, Geological
Yingui Qiu, Jian Zhou
Summary: Rockburst poses significant risks to mine workers and infrastructure. This study developed a novel hybrid model, SCSO-XGBoost, for predicting the scale of short-term rockburst damage. The model achieved high accuracy and outperformed other models, demonstrating its effectiveness in rockburst damage prediction.
ROCK MECHANICS AND ROCK ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Mingchun Lin, Guanqi Wang, Jian Zhou, Wei Zhou, Ni An, Gang Ma
Summary: This study analyzes the deformation characteristics of crushable particle materials through a series of cyclic loading tests conducted by numerical simulation. The investigation of hysteretic behavior from a particle scale reveals that an increase in particles with contacts less than two may cause residual strain, and particle breakage facilitates particle rearrangement and volume contraction. The accumulation of plastic strain and the resilient modulus are found to be influenced by confining pressures, stress levels, cyclic loading amplitudes, and the number of cycles. A function for plastic strain accumulation and an evolution function for resilient modulus are proposed.
JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A
(2023)
Article
Engineering, Civil
Jian Zhou, Shuai Huang, Ming Tao, Manoj Khandelwal, Yong Dai, Mingsheng Zhao
Summary: This study proposes a new prediction method based on machine learning to scientifically adjust the critical span graph. The prediction performance of the proposed PSO-GBDT model is the most reliable among the other eight models, with a classification accuracy of 0.93. It has great potential to provide a more scientific and accurate choice for the stability prediction of underground excavations.
Article
Engineering, Civil
Enming Li, Ning Zhang, Bin Xi, Jian Zhou, Xiaofeng Gao
Summary: This study successfully predicts the compressive strength of green concrete by combining novel algorithms with the XGB model, with the SSA-XGB model performing the best.
FRONTIERS OF STRUCTURAL AND CIVIL ENGINEERING
(2023)