Article
Engineering, Geological
Jian Zhou, Yingui Qiu, Manoj Khandelwal, Shuangli Zhu, Xiliang Zhang
Summary: Blasting is still considered an important alternative for conventional excavations, but the ground vibration it generates can be harmful to nearby structures and should be prevented. A novel Jaya-XGBoost model was developed to predict blast-induced peak particle velocity (PPV) with high reliability using 150 sets of data and the Jaya algorithm for optimization. This model outperformed other machine learning models and traditional empirical models in predicting ground vibration.
INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES
(2021)
Article
Computer Science, Interdisciplinary Applications
Abbas Abbaszadeh Shahri, Fardin Pashamohammadi, Reza Asheghi, Hossein Abbaszadeh Shahri
Summary: The study established an optimal predictive PPV model using a generalized feedforward neural network structure and a novel automated intelligent parameter setting approach. Two new optimized hybrid models were developed, incorporating GFFN with firefly and imperialist competitive metaheuristic algorithms (FMA and ICA). Results showed that the predictability level of the hybrid GFFN-FMA model significantly outperformed the GFFN-ICA and optimum GFFN models.
ENGINEERING WITH COMPUTERS
(2022)
Article
Chemistry, Multidisciplinary
Yewuhalashet Fissha, Hajime Ikeda, Hisatoshi Toriya, Tsuyoshi Adachi, Youhei Kawamura
Summary: Rock blasting is a commonly used and cost-effective excavation technique, but it has negative environmental effects such as air overpressure, fly rock, and ground vibration. Ground vibration is the most detrimental impact, affecting both the environment and the human population. This study used Bayesian neural network and four machine learning techniques to predict blast-induced ground vibration. The evaluation of models showed that the BNN model outperformed the others with lower error: R = 0.94, RMSE = 0.17, and MSE = 0.03. SHAP analysis was also performed to explain the importance of model features and address the black box issue.
APPLIED SCIENCES-BASEL
(2023)
Article
Materials Science, Multidisciplinary
Seungro Lee, Joonhee Park, Naksoo Kim, Taeyong Lee, Luca Quagliato
Summary: This paper presents a machine learning methodology that can learn from simulation results, experimental data, or sensor signals, and is capable of predicting and optimizing specific user-defined process and design parameters. The methodology utilizes an enhanced Extreme Gradient Boosting (XGB) algorithm and a metaheuristic search algorithm based on Differential Evolution (DE) architecture for optimization.
MATERIALS & DESIGN
(2023)
Article
Computer Science, Interdisciplinary Applications
Danial Jahed Armaghani, Deepak Kumar, Pijush Samui, Mahdi Hasanipanah, Bishwajit Roy
Summary: Ground vibration induced by blasting is a significant concern in mining and tunneling projects, and developing an accurate prediction model is crucial. A new hybrid machine learning technique, AGPSO-ELM, has been proposed in this study to predict ground vibration, showing superior performance compared to other models. The AGPSO-ELM model demonstrated higher prediction accuracy for peak particle velocity, making it a valuable tool for forecasting ground vibration in various fields.
ENGINEERING WITH COMPUTERS
(2021)
Article
Computer Science, Information Systems
Sayed S. R. Moustafa, Mohamed S. Abdalzaher, Mohamed H. Yassien, Taotao Wang, Mohamed Elwekeil, Hesham E. Abdel Hafiez
Summary: This study investigated and predicted ground vibrations caused by blasting operations in a quarry using machine learning models. By optimizing the Decision Trees model, accurate predictions of PPV values with lower errors and higher accuracy were achieved.
Article
Construction & Building Technology
Hieu Nguyen, Nhat-Duc Hoang
Summary: This paper presents alternative solutions for classifying concrete spall severity based on computer vision approaches. XGBoost optimized by the Aquila metaheuristic and used with ARCS-LBP achieved an outstanding classification performance with a classification accuracy rate of roughly 99% for real-world concrete surface images.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Chemistry, Multidisciplinary
Biao He, Sai Hin Lai, Ahmed Salih Mohammed, Mohanad Muayad Sabri Sabri, Dmitrii Vladimirovich Ulrikh
Summary: This study developed a regular random forest model to accurately estimate the environmental impact of blasting. To enhance the model's performance, several techniques were proposed. The results showed that all refined weighted models outperformed the regular model, with the refined weighted RF model using the whale optimization algorithm performing the best. Sensitivity analysis revealed that the powder factor has the most significant impact on the prediction of peak particle velocity.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Multidisciplinary
Aleksandar Petrovic, Robertas Damasevicius, Luka Jovanovic, Ana Toskovic, Vladimir Simic, Nebojsa Bacanin, Miodrag Zivkovic, Petar Spalevic
Summary: This research explored the potential of using artificial intelligence techniques to classify maritime vessels and predict their trajectories based on data-driven approaches. A particle swarm optimization algorithm was introduced to optimize the hyperparameters of the models used in this study. The introduced Boosted PSO showed better performance compared to contemporary optimizers, with the XGBoost model achieving an overall accuracy of 99.72% for vessel classification and the LSTM model achieving a mean square error of 0.000098 for marine trajectory prediction. Statistical analysis and explainable AI principles were applied to validate outcomes and understand the impact of features on model decisions.
APPLIED SCIENCES-BASEL
(2023)
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
Jianbo Zhu, Rui Zhao, Yashi Li, Yankun Ma, Jianxin Wang, Qi Peng, Jun Guo
Summary: This study conducted a series of blasting tests to investigate the effect of barrier hole parameters on stress wave attenuation, vibration reduction effect, and the vibration-isolation rate. The findings showed that barrier hole diameter, spacing, and the number of barrier hole rows all significantly impact the vibration reduction effect.
ROCK MECHANICS AND ROCK ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Jessica Tito Vieira, Robson Bruno Dutra Pereira, Carlos Henrique Lauro, Lincoln Cardoso Brandao, Joao Roberto Ferreira
Summary: This study presents a statistical learning approach for modeling and optimization of the internal turning process in PEEK tubes, and an experimental multi-objective evolutionary optimization method. The results indicate that the extreme gradient boosting model has advantages in prediction and optimization.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Interdisciplinary Applications
Hieu Nguyen, Ngoc-Mai Nguyen, Minh-Tu Cao, Nhat-Duc Hoang, Xuan-Linh Tran
Summary: This study introduces a hybrid data-driven approach utilizing machine learning and swarm intelligence to predict the long-term deflection of reinforced-concrete members. Experimental results demonstrate that this hybrid framework performs well in predictive accuracy, outperforming other popular techniques in various benchmarks.
ENGINEERING WITH COMPUTERS
(2022)
Article
Geosciences, Multidisciplinary
Xiliang Zhang, Hoang Nguyen, Yosoon Choi, Xuan-Nam Bui, Jian Zhou
Summary: Peak particle velocity (PPV) is a crucial criterion in assessing ground vibration risk from mine blasting. A novel MVO-ELM model was proposed, using MVO to optimize ELM weights. The optimized model outperformed traditional ELM, MLP, and USBM models, with significant factors affecting PPV predictions identified as explosive charges, monitoring distance, and rock properties.
NATURAL RESOURCES RESEARCH
(2021)
Article
Mathematics
Abdullah Alqahtani, Shtwai Alsubai, Adel Binbusayyis, Mohemmed Sha, Abdu Gumaei, Yu-Dong Zhang
Summary: Globally, the incidence of kidney stones has increased, making early detection crucial for improving individuals' lives. Machine Learning has gained attention for its ability to continuously enhance and deal with multi-dimensional data. This study proposes a smart toilet model in an IoT-fog environment to detect kidney stones using suitable ML algorithms from real-time urinary data.
Article
Engineering, Geological
Jian Zhou, Yong Dai, Shuai Huang, Danial Jahed Armaghani, Yingui Qiu
Summary: This paper develops six machine learning algorithms optimized by the sparrow search algorithm for specific energy (SE) prediction in roadheader excavation. The results show that cutting depth, uniaxial compressive strength of the rock, and tensile strength of the rock are the most significant input variables for SE prediction.
Article
Chemistry, Physical
Shuliang Dong, Hongchao Ji, Jian Zhou, Xianzhun Li, Lan Ding, Zhenlong Wang
Summary: Micro-liquid floated gyroscopes are widely used in military applications. However, the machining of micro-ball sockets in beryllium copper alloy is challenging due to its excellent properties. In this study, we developed a method for milling micro-ball sockets in C17200 beryllium copper alloy using micro-electrical discharge machining, achieving high precision by optimizing the machining parameters. This method provides a new way to fabricate micro-ball sockets in C17200 with high efficiency for micro-liquid floated gyroscopes.
Article
Chemistry, Physical
Xiancheng Mei, Zhen Cui, Qian Sheng, Jian Zhou, Chuanqi Li
Summary: This study improves the pelican optimization algorithm (POA) by incorporating the Latin hypercube sampling (LHS) method and chaotic mapping (CM) method to optimize the random forest (RF) model. The improved model successfully predicts the seismic performance of a novel aseismic rubber-concrete material in tunnel engineering. The results show that the LHSPOA-RF model outperforms other models in predicting the strength and energy absorption properties of the material, and the rubber and cement are identified as the most important parameters for these properties.
Article
Green & Sustainable Science & Technology
Saeed Aligholi, Manoj Khandelwal
Summary: According to chaos theory, certain underlying patterns can reveal the order of disordered systems. This study discusses the intermittency of rough rock fractured surfaces as an orderable disorder at intermediate length scales, which is more complex than simple fractal or multi-scaling behaviors. The introduced parameters effectively capture the systematic behavior and quantify the intermittency of the surfaces, providing a framework for quantifying and modeling the roughness of fractured surfaces and analyzing fluid flow and shear strength in rock media. This framework can also be used in analyzing the intermittency of time series and developing new models for predicting seismic or flood events with higher accuracy in a short time.
Article
Biophysics
Lanlan Qin, Gaobo Yu, Jian Zhou
Summary: Large-scale simulations were conducted to explore the formation of protein coronas, which are formed by proteins and nanomaterials. The study investigated the effects of protein concentration, size of silica nanoparticles, and ionic strength on the formation of lysozyme-SNP coronas. The findings provide insights into the formation of protein coronas and valuable guidelines for developing novel biomolecule-NP conjugates.
Article
Mechanics
Lei Zhou, Hadi Haeri, Vahab Sarfarazi, Soheil Abharian, Manoj Khandelwal, Mohammad Fatehi Marji
Summary: The effects of porosity and its geometry on the tensile features of concrete were investigated using the Brazilian test and three-dimensional PFC model. The study found that the porosity geometry plays an important role in the fracturing pattern, with different pore shapes leading to different fracture energies and crack initiation stresses. The results highlight the significance of considering porosity geometry in understanding the mechanical behavior of concrete.
MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES
(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
Energy & Fuels
Mostafa Hosseini, Manoj Khandelwal, Rahman Lotfi, Mohsen Eslahi
Summary: Bench blasting is a typical method used in surface mines to excavate hard rock mass. However, improper blasting leads to backbreak, massive rock fragmentation, and high-intensity ground vibrations, resulting in increased production costs and decreased productivity. This study conducted a sensitivity analysis on various blast design parameters using the Taguchi method, and found that blast hole diameter is the most important factor influencing blasting outcomes.
GEOMECHANICS AND GEOPHYSICS FOR GEO-ENERGY AND GEO-RESOURCES
(2023)
Article
Engineering, Multidisciplinary
Jian Zhou, Yong Dai, Ming Tao, Manoj Khandelwal, Mingsheng Zhao, Qiyue Li
Summary: In this research, a novel intelligent model based on random forest algorithm and salp swarm algorithm has been developed to predict the mean cutting force of conical picks. The model demonstrates higher accuracy and reliability compared to other prediction tools.
RESULTS IN ENGINEERING
(2023)
Article
Engineering, Civil
Jiamin Zhang, Daniel Dias, Chuanqi Li
Summary: In this study, an intelligence approach combining the white shark optimizer and the logistic chaotic mapping was proposed to optimize the random forest model for predicting the load transfer efficiency of a single footing over rigid inclusion-reinforced soft soils. Through the investigation of seven variables, it was found that the thickness of the load transfer platform is the most important variable for predicting the load transfer efficiency.
ENGINEERING STRUCTURES
(2023)
Article
Engineering, Civil
Tingting Zhang, Lu An, Daniel Dias, Julien Baroth, Chuanqi Li
Summary: In this study, a sample-wised probabilistic approach SPAA based on the Atom Search Optimization (ASO)-Artificial Neural Network (ANN) model is proposed to analyze the stability of circular shafts considering soil parameter variabilities. The results indicate that the proposed SPAA outperforms the existing methods, requiring fewer deterministic simulations with guaranteed results accuracy, particularly for high-dimensional cases.
ENGINEERING STRUCTURES
(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
Engineering, Geological
Xiancheng Mei, Chuanqi Li, Zhen Cui, Qian Sheng, Jian Chen, Shaojun Li
Summary: This study aims to predict the energy absorption property of a novel aseismic concrete material made of rubber, sand and cement. Various hybrid prediction models, including metaheuristic optimization algorithms and a random forest model, were developed and tested. The TSA-RF model demonstrated the best performance in predicting the energy transmission rate (ETR) of the concrete material, with cement being identified as the most important parameter for ETR prediction. This study provides a feasible application of artificial intelligence in ETR prediction and offers a novel idea for the development of aseismic materials in tunnel engineering.
SOIL DYNAMICS AND EARTHQUAKE ENGINEERING
(2023)
Article
Engineering, Civil
Jian Zhou, Yuxin Chen, Chuanqi Li, Yingui Qiu, Shuai Huang, Ming Tao
Summary: Accurate prediction of tunnel wall convergence was achieved by utilizing six popular and reliable machine learning models and 142 sets of highway tunnel convergence data. The JSO-RF model demonstrated superior predictive performance and identified phi rm, Erm, and H as important input variables.
TRANSPORTATION GEOTECHNICS
(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)