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Assessment of peak particle velocity of blast vibration using hybrid soft computing approaches

JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2025年第2期12卷 154-176页

作者： Yuan, Haiping Zou, Yangyao Li, Hengzhe Ji, Shuaijie Gu, Ziang He, Liu Hu, Ruichao Hefei Univ Technol Coll Civil Engn Hefei 230009 Peoples R China State Key Lab Min Induced Response & Disaster Prev Huainan 232001 Peoples R China

Blasting vibration is a major adverse effect in rock blasting excavation, and accurately predicting its peak particle velocity (PPV) is vital for ensuring engineering safety and risk management. This study proposes an innovative IHO-VMD-catboost model that integrates variational mode decomposition (VMD) and the catboost algorithm, with hyperparameters globally optimized using the improved hippopotamus optimization (IHO) algorithm. Compared to existing models, the proposed method improves feature extraction from vibration signals and significantly enhances prediction accuracy, especially in complex geological conditions. Using measured data from open-pit mine blasting, the model extracts key features such as maximum section charge, total charge, and horizontal distance, achieving superior performance compared to 13 traditional models. It reports a root mean square error of 0.28 cm/s, a mean absolute error of 0.17 cm/s, an index of agreement of 0.993, and a variance accounted for value of 97.28%, demonstrating superior prediction accuracy, a high degree of fit with observed data, and overall robustness in PPV prediction. Additionally, analyses based on the SHapley Additive Explanations framework provide insights into the complex nonlinear relationships between factors like horizontal distance and maximum section charge, improving the model's interpretability. The model demonstrates robustness, stability, and applicability in various tests, confirming its reliability in complex engineering scenarios, and offering a valuable solution for safe mining and optimized blasting design.

关键词： blast vibration peak particle velocity catboost algorithm variational mode decomposition improved hippopotamus optimization SHapley Additive exPlanations

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Improved estimation of dark fermentation biohydrogen production utilizing a robust categorical boosting machine-learning algorithm

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年第PartA期52卷 190-199页

作者： Bandpey, Atieh Fahimi Abdi, Jafar Firozjaee, Tahere Taghizade Lappeenranta Lahti Univ Technol LUT Sch Engn Sci Ind Engn & Management IEM POB 20 FI-53851 Lappeenranta Finland Shahrood Univ Technol Fac Chem & Mat Engn Shahrood *** Iran Shahrood Univ Technol Fac Civil Engn Shahrood *** Iran

Dark fermentation is attracting great attention due to some capabilities such as wastewater treatment and biofuel production. In this study, the performance of different machine learning algorithms, including Bagging-Extreme Gradient Boosting (BagXGBoost), Categorical Boosting (catboost), K Nearest Neighborhood (KNN), Light Gradient Boosting Machine (LightGBM), and Decision Trees (DT) in the prediction of biohydrogen production from wastewater during dark fermentation is investigated. Effect of parameters Ni, Fe, biomass, pH, chemical oxygen demand (COD), HRT, acetate, ethanol, butyrate, and acetate/butyrate ratio as inputs were considered for the model development. A databank with 208 data points was culled from the literature. For the validation of the proposed models, various statistical criteria were determined. catboost and BagXGBoost exhibited superior performance in predicting fermentative hydrogen (H2) production. Shapley additive explanations were used for relative analysis, revealing the highest absolute SHAP values of 1.1, 1, and 0.07 for COD, nickel, and butyrate, respectively, highlighting their significance in H2 production. The insights gained from this study can contribute to further optimization of the dark biohydrogen fermentation process.

关键词： Dark fermentation Machine learning catboost algorithm SHAP values Relevancy analysis

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Novel models based on machine learning to predict the prognosis of metaplastic breast cancer

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BREAST 2025年 79卷 103858-103858页

作者： Zhang, Yinghui An, Wenxin Wang, Cong Liu, Xiaolei Zhang, Qihong Zhang, Yue Cheng, Shaoqiang Harbin Med Univ Dept Breast Surg Canc Hosp Harbin 150081 Heilongjiang Peoples R China Harbin Med Univ Canc Hosp Dept Urol Surg Harbin Peoples R China Harbin Med Univ Canc Hosp Dept Gastrointestinal Surg Harbin Peoples R China Harbin Med Univ Canc Hosp Dept Breast Med Harbin 150081 Heilongjiang Peoples R China

Background: Metaplastic breast cancer (MBC) is a rare and highly aggressive histological subtype of breast cancer. There remains a significant lack of precise predictive models available for use in clinical practice. Methods: This study utilized patient data from the SEER database (2010-2018) for data analysis. We utilized prognostic factors to develop a novel machine learning model (catboost) for predicting patient survival rates. Simultaneously, our hospital's cohort of MBC patients was utilized to validate our model. We compared the benefits of radiotherapy among the three groups of patients. Results: The catboost model we developed exhibits high accuracy and correctness, making it the best-performing model for predicting survival outcomes in patients with MBC (1-year AUC = 0.833, 3-year AUC = 0.806;5-year AUC = 0.810). Furthermore, the catboost model maintains strong performance in an external independent dataset, with AUC values of 0.937 for 1-year survival, 0.907 for 3-year survival, and 0.890 for 5-year survival, respectively. Radiotherapy is more suitable for patients undergoing breast-conserving surgery with M0 stage [group1: (OS:HR = 0.499, 95%CI 0.320-0.777 p < 0.001;BCSS: HR = 0.519, 95%CI 0.290-0.929 p = 0.008)] and those with T3-4/N2-3M0 stage undergoing mastectomy [group2: (OS:HR = 0.595, 95%CI 0.437-0.810 p < 0.001;BCSS: HR = 0.607, 95%CI 0.427-0.862 p = 0.003)], compared to patients with stage T1-2/N0-1M0 undergoing mastectomy [group3: (OS:HR = 1.090, 95%CI 0.673-1.750 p = 0.730;BCSS: HR = 1.909, 95%CI 1.036-3.515 p = 0.038)]. Conclusion: We developed three machine learning prognostic models to predict survival rates in patients with MBC. Radiotherapy is considered more appropriate for patients who have undergone breast-conserving surgery with M0 stage as well as those in stage T3-4/N2-3M0 undergoing mastectomy.

关键词： Metaplastic breast cancer catboost algorithm SEER SHAP Radiotherapy

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Machine Learning for Predicting Required Cross-Sectional Dimensions of Circular Concrete-Filled Steel Tubular Columns

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BUILDINGS 2025年第9期15卷 1438-1438页

作者： Chepurnenko, Anton Al-Zgul, Samir Tyurina, Vasilina Don State Tech Univ Struct Mech & Theory Struct Dept Rostov Na Donu 344003 Russia

Machine learning methods are widely used to predict the bearing capacity of concrete-filled steel tubular (CFST) columns. However, in addition to this task, the engineer often faces the inverse problem: to determine what cross-section dimensions of the CFST column are required for given loads. This paper is devoted to the development of machine learning models for predicting the geometric parameters of a circular cross-section for concrete-filled steel tubular (CFST) columns under the combined action of bending moments and compressive axial forces. This problem has not been solved by machine learning methods before. The main focus is on automating the design process of CFST columns using the catboost algorithm and artificial neural networks. Three machine learning models were developed to solve the problem. The first and second models are based on the catboost algorithm. They predict the column diameter at minimum and maximum wall thicknesses, respectively. The third model is an artificial neural network, which is designed to determine the wall thickness of a CFST column. The models were trained on synthetic data generated in accordance with Russian design codes. The first and second models demonstrated high accuracy in predicting the column diameter (RMSE = 3.86 mm and 4.12 mm, respectively). The third model showed high efficiency over the entire range of wall thicknesses (correlation coefficient R = 0.99974). Feature importance analysis using SHAP values confirmed the key role of bending moment and axial force in predicting geometric parameters.

关键词： concrete-filled steel tubular columns machine learning catboost algorithm artificial neural networks bearing capacity

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Interactive 3D Vase Design Based on Gradient Boosting Decision Trees

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algorithmS 2024年第9期17卷 407页

作者： Wang, Dongming Xu, Xing Xia, Xuewen Jia, Heming Minnan Normal Univ Coll Phys & Informat Engn Zhangzhou 363000 Peoples R China Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China

Traditionally, ceramic design began with sketches on rough paper and later evolved into using CAD software for more complex designs and simulations. With technological advancements, optimization algorithms have gradually been introduced into ceramic design to enhance design efficiency and creative diversity. The use of Interactive Genetic algorithms (IGAs) for ceramic design is a new approach, but an IGA requires a significant amount of user evaluation, which can result in user fatigue. To overcome this problem, this paper introduces the LightGBM algorithm and the catboost algorithm to improve the IGA because they have excellent predictive capabilities that can assist users in evaluations. The algorithms are also applied to a vase design platform for validation. First, bicubic B & eacute;zier surfaces are used for modeling, and the genetic encoding of the vase is designed with appropriate evolutionary operators selected. Second, user data from the online platform are collected to train and optimize the LightGBM and catboost algorithms. Finally, LightGBM and catboost are combined with an IGA and applied to the vase design platform to verify their effectiveness. Comparing the improved algorithm to traditional IGAs, KD trees, Random Forest, and XGBoost, it is found that IGAs improve with LightGBM, and catboost performs better overall, requiring fewer evaluations and less time. Its R2 is higher than other proxy models, achieving 0.816 and 0.839, respectively. The improved method proposed in this paper can effectively alleviate user fatigue and enhance the user experience in product design participation.

关键词： Interactive Genetic algorithm bicubic B & eacute zier surface LightGBM algorithm catboost algorithm 3D vase

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Lipoproteins and metabolites in diagnosing and predicting Alzheimer's disease using machine learning

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LIPIDS IN HEALTH AND DISEASE 2024年第1期23卷 1-12页

作者： Wang, Fenglin Wang, Aimin Huang, Yiming Gao, Wenfeng Xu, Yaqi Zhang, Wenjing Guo, Guiya Song, Wangchen Kong, Yujia Wang, Qinghua Wang, Suzhen Shi, Fuyan Shandong Second Med Univ Sch Publ Hlth Dept Hlth Stat Weifang 261053 Shandong Peoples R China Shandong Second Med Univ Affiliated Hosp Dept Rheumatol & Immunol Weifang 261031 Shandong Peoples R China

Background Alzheimer's disease (AD) is a chronic neurodegenerative disorder that poses a substantial economic burden. The Random forest algorithm is effective in predicting AD;however, the key factors influencing AD onset remain unclear. This study aimed to analyze the key lipoprotein and metabolite factors influencing AD onset using machine-learning methods. It provides new insights for researchers and medical personnel to understand AD and provides a reference for the early diagnosis, treatment, and early prevention of *** A total of 603 participants, including controls and patients with AD with complete lipoprotein and metabolite data from the Alzheimer's disease Neuroimaging Initiative (ADNI) database between 2005 and 2016, were enrolled. Random forest, Lasso regression, and catboost algorithms were employed to rank and filter 213 lipoprotein and metabolite variables. Variables with consistently high importance rankings from any two methods were incorporated into the models. Finally, the variables selected from the three methods, with the participants' age, sex, and marital status, were used to construct a random forest predictive *** Fourteen lipoprotein and metabolite variables were screened using the three methods, and 17 variables were included in the AD prediction model based on age, sex, and marital status of the participants. The optimal random forest modeling was constructed with "mtry" set to 3 and "ntree" set to 300. The model exhibited an accuracy of 71.01%, a sensitivity of 79.59%, a specificity of 65.28%, and an AUC (95%CI) of 0.724 (0.645-0.804). When Mean Decrease Accuracy and Gini were used to rank the proteins, age, phospholipids to total lipids ratio in intermediate-density lipoproteins (IDL_PL_PCT), and creatinine were among the top five *** Age, IDL_PL_PCT, and creatinine levels play crucial roles in AD onset. Regular monitoring of lipoproteins and their metabolites in older individuals is significant for ea

关键词： Alzheimer's disease Random forest Lasso regression catboost algorithm

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Machine learning prediction of electric flux in concrete and mix proportion optimization design

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MATERIALS TODAY COMMUNICATIONS 2024年 38卷

作者： Dai, Jinpeng Yang, Xiaoyuan He, Jie Wang, Qicai Zhang, Zhijie Lanzhou Jiaotong Univ Natl & Prov Joint Engn Lab Rd & Bridge Disaster Pr Lanzhou 730070 Peoples R China Jiangsu Res Inst Bldg Sci CO LTD State Key Lab High Preformance Civil Engn Mat Nanjing 210008 Peoples R China Southeast Univ Sch Mat Sci & Engn Nanjing 211189 Peoples R China Lanzhou Jiaotong Univ Civil Engn Dept Lanzhou 730070 Peoples R China

This study explored nine machine learning (ML) methods, including linear, nonlinear and integrated learning models, to predict concrete electrical flux metrics reflecting the chloride ion permeability of concrete. Seven concrete mix parameters, including water (W), cement (C), fly ash (FA), ground granulated blast furnace slag (GGBS), fine aggregate (S), coarse aggregate (G) and water reducing admixture (WRA), were used as input parameters. A dataset consisting of 4029 sets of data was used to train and test the prediction model. K-fold crossvalidation confirms the model's accuracy and is performed by R2, MAE, MSE, and RMSE. The results showed that the nonlinear model outperformed the linear model;in particular, the model integrated with learning catboost exhibited higher prediction accuracy (R2 = 0.72, RMSE = 0.01, MSE = 0.01, MAE = 0.04). Based on this, a concrete chloride impermeability mix proportion optimization method targeting cost per square is proposed. The proposed model aims to achieve an ideal concrete mix proportion optimization based on ML algorithms and national specifications, which is of great significance to practical engineering.

关键词： Machine learning catboost algorithm Electrical flux Impermeability Mix proportion optimization

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Analyzing the Determinants of US Residential Energy Usage and Spending: A Machine Learning Approach

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ENERGY JOURNAL 2025年第1期46卷 147 - 177页

作者： Chidmi, Benaissa Ettoumi, Youssef Aljohani, Emad S. Texas Tech Univ Dept Agr & Appl Econ MS 42132 Lubbock TX 79409 USA Natl Sch Architecture Tetouan Tetouan Morocco King Saud Univ Dept Agr Econ Riyadh Saudi Arabia

This study explores the factors that impact residential energy usage and spending in the United States. Using data from the 2020 Residential Energy Consumption Survey (RECS), we investigate the significance of different energy consumption determinants at various analysis levels. Our analysis covers residential energy usage, electricity, natural gas, propane, and fuel oil consumption. We also examine energy usage for space heating, cooling, and water heating. To leverage the extensive RECS data, which includes over 180 variables, we utilized machine learning (ML) techniques for feature selection and determined their Shapley contribution for different target outcomes. Our results indicate that the catboost algorithm outperforms other ML techniques on the 2020 Residential Energy Consumption Survey sample. Our findings demonstrate that it is not appropriate to aggregate consumption and expenditure, as each level has distinct important *** Classification: D12, Q41, R21

关键词： residential energy consumption and expenditure space heating space cooling water heating catboost algorithm residential energy consumption survey

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Segmentation and classification of white blood cancer cells from bone marrow microscopic images using duplet-convolutional neural network design

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第23期82卷 35277-35299页

作者： Devi, Tulasi Gayatri Patil, Nagamma Rai, Sharada Sarah, Cheryl Philipose Natl Inst Technol Karnataka Dept Informat Technol Mangalore Karnataka India Kasturba Med Coll & Hosp Dept Pathol Mangalore Karnataka India

Cancer is a disease linked to the untamed and rapid division of cells in the body. Cancer detection through conventional methods like complete blood count is a tedious and time-consuming task prone to human errors. The introduction of image processing techniques and computer-aided diagnostics is beneficial to this field as the results obtained by utilizing these methods are quick and accurate. The proposed method in this paper uses a design Convolutional Leaky RELU with catboost and XGBoost (CLR-CXG) to segment the images and extract the important features that help in classification. The binary classification algorithm and gradient boosting algorithm catboost (Categorical Boost) and XGBoost (Extreme Gradient Boost) are implemented individually. Moreover, Convolutional Leaky RELU with catboost (CLRC) is designed to decrease bias and provide high accuracy, while Convolutional Leaky RELU with XGBoost (CLRXG) is designed for classification or regression prediction problems which will increase the speed of executing the algorithm and improve its performance. Thus the CLR-CXG classifies the test images into Acute Lymphoblastic Leukemia (ALL) or Multiple Myeloma (MM). Finally, the CLRC algorithm achieved 100% accuracy in classifying cancer cells, and the recorded run time is 10s. Moreover, the CLRXG algorithm has gained an accuracy of 97.12% for classifying cancer cells and 12 s for executing the process.

关键词： Acute Lymphoblastic Leukemia Adamoptimizer Binary Classification Blood Cancer catboost algorithm Convolutional Neural Network Extreme Gradient Boost algorithm Multiple Myeloma

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Sensitivity analysis of low salinity waterflood alternating immiscible CO2 injection (Immiscible CO2-LSWAG) performance using machine learning application in sandstone reservoir

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2024年第11期14卷 3055-3077页

作者： Efras, Muhammad Ridho Dzulkarnain, Iskandar Ridha, Syahrir Syahputra, Loris Alif Rasool, Muhammad Hammad Merdeka, Mohammad Galang Pramana, Agus Astra Univ Teknol PETRONAS Dept Petr Engn Bandar Seri Iskandar 32610 Perak Malaysia Univ Teknol PETRONAS Inst Sustainable Energy & Resources Bandar Seri Iskandar 32610 Perak Malaysia Univ Teknol PETRONAS Dept Petr Engn Bandar Seri Iskandar 32610 Perak Malaysia Inst Teknol Bandung Dept Petr Engn Bandung 40132 Indonesia Univ Pertamina Dept Petr Engn Jakarta 12220 Indonesia

Low salinity water alternating immiscible gas CO2 (Immiscible CO2-LSWAG) injection is a popular technique for enhanced oil recovery (EOR) that combines the benefits of low salinity and immiscible CO2 flooding to increase and accelerate oil production. This approach modifies the displacement properties of the reservoir, resulting in higher sweep efficiency and greater oil production. The current study employs a combination of numerical and machine learning techniques to comprehensively investigate the performance of immiscible CO2-LSWAG injection in a sandstone reservoir. Furthermore, a detailed sensitivity analysis of various injection and reservoir parameters is conducted to gain deeper insights into their impact on the process. In order to predict the oil recovery factor (RF), the study employs 1000 experimental designs on initial oil-wet. The numerical simulation results indicate that immiscible CO2-LSWAG injection outperforms conventional immiscible CO2 and low salinity waterflood injection, resulting in a higher oil RF. The machine learning models of catboost and LightGBM used in this study produced R-2 scores higher than 0.95 with lower errors between the predicted and actual results. This indicates that machine learning models can provide a faster and more accurate alternative to numerical simulation. The sensitivity analysis results from the machine learning model reveal that the major contributing factors to oil RF are the chemical composition of the injected water and the injection rate. In summary, this study leverages machine learning for sensitivity analysis in immiscible CO2-LSWAG performance in oil-wet sandstone reservoirs. Key findings include the identification of top influencing parameters and high predictive accuracy of catboost and LightGBM algorithms. The results facilitate quick decision-making for field trials by focusing on major contributing factors, with future research suggested for broader applications.

关键词： Enhanced oil recovery Sensitivity analysis Low salinity waterflood alternate immiscible CO2 injection catboost algorithm LightGBM algorithm Machine Learning

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