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Quantification of the concrete freeze–thaw environment across the Qinghai–Tibet Plateau based on machine learning algorithms

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Journal of Mountain Science 2024年第1期21卷 322-334页

作者： QIN Yanhui MA Haoyuan ZHANG Lele YIN Jinshuai ZHENG Xionghui LI Shuo Department of Civil Engineering Qingdao University of TechnologyQingdao 266520China Department of Geography Qinghai Normal UniversityXining 810008China

The reasonable quantification of the concrete freezing environment on the Qinghai–Tibet Plateau(QTP) is the primary issue in frost resistant concrete design, which is one of the challenges that the QTP engineering managers should take into account. In this paper, we propose a more realistic method to calculate the number of concrete freeze–thaw cycles(NFTCs) on the QTP. The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7. Four machine learning methods, i.e., the random forest(RF) model, generalized boosting method(GBM), generalized linear model(GLM), and generalized additive model(GAM), are used to fit the NFTCs. The root mean square error(RMSE) values of the RF, GBM, GLM, and GAM are 32.3, 4.3, 247.9, and 161.3, respectively. The R^(2) values of the RF, GBM, GLM, and GAM are 0.93, 0.99, 0.48, and 0.66, respectively. The GBM method performs the best compared to the other three methods, which was shown by the results of RMSE and R^(2) values. The quantitative results from the GBM method indicate that the lowest, medium, and highest NFTC values are distributed in the northern, central, and southern parts of the QTP, respectively. The annual NFTCs in the QTP region are mainly concentrated at 160 and above, and the average NFTCs is 200 across the QTP. Our results can provide scientific guidance and a theoretical basis for the freezing resistance design of concrete in various projects on the QTP.

关键词： Freeze–thaw cycles Quantification machine learning algorithms Qinghai–Tibet Plateau Concrete

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Modeling potential wetland distributions in China based on geographic big data and machine learning algorithms

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International Journal of Digital Earth 2023年第1期16卷 3706-3724页

作者： Hengxing Xiang Yanbiao Xi Dehua Mao Tianyuan Xu Ming Wang Fudong Yu Kaidong Feng Zongming Wang State Key Laboratory of Black Soils Conservation and Utilization Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchunPeople’s Republic of China International Institute for Earth System Science Nanjing UniversityNanjingPeople’s Republic of China School of Foreign Languages Zhongnan University of Economics and LawWuhanPeople’s Republic of China College of Information and Technology Jilin Agricultural UniversityChangchunPeople’s Republic of China National Earth System Science Data Center BeijingPeople’s Republic of China

Climate change and human activities have reduced the area and degraded the functions and services of wetlands in China.To protect and restore wetlands,it is urgent to predict the spatial distribution of potential wetlands.In this study,the distribution of potential wetlands in China was simulated by integrating the advantages of Google Earth Engine with geographic big data and machine learning algorithms.Based on a potential wetland database with 46,000 samples and an indicator system of 30 hydrologic,soil,vegetation,and topographic factors,a simulation model was constructed by machine learning algorithms.The accuracy of the random forest model for simulating the distribution of potential wetlands in China was good,with an area under the receiver operating characteristic curve value of 0.851.The area of potential wetlands was 332,702 km^(2),with 39.0%of potential wetlands in Northeast China.Geographic features were notable,and potential wetlands were mainly concentrated in areas with 400-600 mm precipitation,semi-hydric and hydric soils,meadow and marsh vegetation,altitude less than 700 m,and slope less than 3°.The results provide an important reference for wetland remote sensing mapping and a scientific basis for wetland management in China.

关键词： Potential wetland distribution machine learning algorithms geographic big data China wetland geographic features

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Use of machine learning algorithms to assess the state of rockburst hazard in underground coal mine openings

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第3期14卷 703-713页

作者： Lukasz Wojtecki Sebastian Iwaszenko Derek B.Apel Mirosawa Bukowska Janusz Makówka Central Mining Institute Katowice40-166Poland School of Mining and Petroleum Engineering University of AlbertaEdmontonT6G 2R3Canada

The risk of rockbursts is one of the main threats in hard coal mines. Compared to other underground mines, the number of factors contributing to the rockburst at underground coal mines is much greater.Factors such as the coal seam tendency to rockbursts, the thickness of the coal seam, and the stress level in the seam have to be considered, but also the entire coal seam-surrounding rock system has to be evaluated when trying to predict the rockbursts. However, in hard coal mines, there are stroke or stress-stroke rockbursts in which the fracture of a thick layer of sandstone plays an essential role in predicting rockbursts. The occurrence of rockbursts in coal mines is complex, and their prediction is even more difficult than in other mines. In recent years, the interest in machine learning algorithms for solving complex nonlinear problems has increased, which also applies to geosciences. This study attempts to use machine learning algorithms, i.e. neural network, decision tree, random forest, gradient boosting, and extreme gradient boosting(XGB), to assess the rockburst hazard of an active hard coal mine in the Upper Silesian Coal Basin. The rock mass bursting tendency index WTGthat describes the tendency of the seam-surrounding rock system to rockbursts and the anomaly of the vertical stress component were applied for this purpose. Especially, the decision tree and neural network models were proved to be effective in correctly distinguishing rockbursts from tremors, after which the excavation was not damaged. On average, these models correctly classified about 80% of the rockbursts in the testing datasets.

关键词： Hard coal mining Rockburst hazard machine learning algorithms

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Medical Data Clustering and Classification Using TLBO and machine learning algorithms

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Computers, Materials & Continua 2022年第3期70卷 4523-4543页

作者： Ashutosh Kumar Dubey Umesh Gupta Sonal Jain Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Institute of Engineering and Technology JK Lakshmipat UniversityJaipurIndia

This study aims to empirically analyze teaching-learning-based optimization(TLBO)and machine learning algorithms using k-means and fuzzy c-means(FCM)algorithms for their individual performance evaluation in terms of clustering and classification.In the first phase,the clustering(k-means and FCM)algorithms were employed independently and the clustering accuracy was evaluated using different computationalmeasures.During the second phase,the non-clustered data obtained from the first phase were preprocessed with TLBO.TLBO was performed using k-means(TLBO-KM)and FCM(TLBO-FCM)(TLBO-KM/FCM)algorithms.The objective function was determined by considering both minimization and maximization criteria.Non-clustered data obtained from the first phase were further utilized and fed as input for threshold optimization.Five benchmark datasets were considered from theUniversity of California,Irvine(UCI)machine learning Repository for comparative study and experimentation.These are breast cancer Wisconsin(BCW),Pima Indians Diabetes,Heart-Statlog,Hepatitis,and Cleveland Heart Disease datasets.The combined average accuracy obtained collectively is approximately 99.4%in case of TLBO-KM and 98.6%in case of TLBOFCM.This approach is also capable of finding the dominating attributes.The findings indicate that TLBO-KM/FCM,considering different computational measures,perform well on the non-clustered data where k-means and FCM,if employed independently,fail to provide significant results.Evaluating different feature sets,the TLBO-KM/FCM and SVM(GS)clearly outperformed all other classifiers in terms of sensitivity,specificity and accuracy.TLBOKM/FCM attained the highest average sensitivity(98.7%),highest average specificity(98.4%)and highest average accuracy(99.4%)for 10-fold cross validation with different test data.

关键词： K-means FCM TLBO TLBO-KM TLBO-FCM TLBO-KM/FCM machine learning algorithms

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Data-driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 874-885页

作者： Wenlong Liao Dechang Yang Qi Liu Yixiong Jia Chenxi Wang Zhe Yang Wind Engineering and Renewable Energy Laboratory Ecole Polytechnique Federale de Lausanne (EPFL) the College of Information and Electrical Engineering China Agricultural University the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Electrical and Electronic Engineering (Energy Digitalization Laboratory) The University of Hong Kong the Department of Electrical Engineering The Hong Kong Polytechnic University

Reactive power optimization of distribution networks is traditionally addressed by physical model based methods, which often lead to locally optimal solutions and require heavy online inference time consumption. To improve the quality of the solution and reduce the inference time burden, this paper proposes a new graph attention networks based method to directly map the complex nonlinear relationship between graphs(topology and power loads) and reactive power scheduling schemes of distribution networks, from a data-driven perspective. The graph attention network is tailored specifically to this problem and incorporates several innovative features such as a self-loop in the adjacency matrix, a customized loss function, and the use of max-pooling layers. Additionally, a rulebased strategy is proposed to adjust infeasible solutions that violate constraints. Simulation results on multiple distribution networks demonstrate that the proposed method outperforms other machine learning based methods in terms of the solution quality and robustness to varying load conditions. Moreover, its online inference time is significantly faster than traditional physical model based methods, particularly for large-scale distribution networks.

关键词： Distribution networks Reactive power Load modeling Transformers machine learning algorithms Inference algorithms Static VAr compensators

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A Review of Hybrid Cyber Threats Modelling and Detection Using Artificial Intelligence in IIoT

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1233-1261页

作者： Yifan Liu Shancang Li Xinheng Wang Li Xu School of Computer Science and Informatics Cardiff UniversityCardiffCF244AGUK Department of Mechatronics and Robotics Xi’an Jiaotong-Liverpool UniversitySuzhou215123China Department of Information Technology&Decision Sciences Old Dominion UniversityNorfolk23529USA

The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated to cyber security threats that need to be addressed.This work investigates hybrid cyber threats(HCTs),which are now working on an entirely new level with the increasingly adopted IIoT.This work focuses on emerging methods to model,detect,and defend against hybrid cyber attacks using machine learning(ML)techniques.Specifically,a novel ML-based HCT modelling and analysis framework was proposed,in which L1 regularisation and Random Forest were used to cluster features and analyse the importance and impact of each feature in both individual threats and HCTs.A grey relation analysis-based model was employed to construct the correlation between IIoT components and different threats.

关键词： Cyber security Industrial Internet of Things artificial intelligence machine learning algorithms hybrid cyber threats

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Medical Internet of things using machine learning algorithms for lung cancer detection

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Journal of Management Analytics 2020年第4期7卷 591-623页

作者： Kanchan Pradhan Priyanka Chawla Lovely Professional University CSEPhagwaraIndia

This paper empirically evaluates the several machine learning algorithms adaptable for lung cancer detection linked with IoT devices.In this work,a review of nearly 65 papers for predicting different diseases,using machine learning algorithms,has been done.The analysis mainly focuses on various machine learning algorithms used for detecting several diseases in order to search for a gap toward the future improvement for detecting lung cancer in medical IoT.Each technique was analyzed on each step,and the overall drawbacks are pointed out.In addition,it also analyzes the type of data used for predicting the concerned disease,whether it is the benchmark or manually collected data.Finally,research directions have been identified and depicted based on the various existing methodologies.This will be helpful for the upcoming researchers to detect the cancerous patients accurately in early stages without any flaws.

关键词： disease prediction lung cancer machine learning algorithms internet of things

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Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms

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Friction 2020年第1期8卷 107-114页

作者： Osman ALTAY Turan GURGENC Mustafa ULAS Cihan OZEL Software Engineering Firat UniversityElazig 23119Turkey Automotive Engineering Firat UniversityElazig 23119Turkey Mechanical Engineering Firat UniversityElazig 23119Turkey

In this study,experimental wear losses under different loads and sliding distances of AISI 1020 steel surfaces coated with(wt.%)50FeCrC‐20FeW‐30FeB and 70FeCrC‐30FeB powder mixtures by plasma transfer arc welding were determined.The dataset comprised 99 different wear amount measurements obtained experimentally in the laboratory.The linear regression(LR),support vector machine(SVM),and Gaussian process regression(GPR)algorithms are used for predicting wear quantities.A success rate of 0.93 was obtained from the LR algorithm and 0.96 from the SVM and GPR algorithms.

关键词： surface coating plasma transfer arc(PTA)welding wear prediction machine learning algorithms

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Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin,Asir Region,Saudi Arabia

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Geoscience Frontiers 2021年第2期12卷 639-655页

作者： Ahmed Mohamed Youssef Hamid Reza Pourghasemi Geology Department Faculty of ScienceSohag UniversityEgypt Geological Hazards Department Applied Geology SectorSaudi Geological SurveyP.O.Box 54141Jeddah21514Saudi Arabia Department of Natural Resources and Environmental Engineering College of AgricultureShiraz UniversityShirazIran

The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their performances.Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue.This study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi Arabia.First,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources.All the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating purposes.Twelve landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and slope-aspect.The area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs performance.The results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for RF.Our findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other MLTs.The outcome of this study and the landslide susceptibility maps would be useful for environmental protection.

关键词： Landslide susceptibility machine learning algorithms Variables importance Saudi Arabia

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Recent innovation in benchmark rates (BMR):evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms

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Financial Innovation 2021年第1期7卷 942-961页

作者： Öer Depren Mustafa Tevfik Kartal Serpil KılıçDepren YapıKredi Bank IstanbulTurkey Borsaİstanbul Financial Reporting and Subsidiaries Directorate Reşitpaşa Mahallesi Borsaİstanbul CaddesiNo:434467 IstanbulTurkey Department of Statistics Yildiz Technical UniversityIstanbulTurkey

Some countries have announced national benchmark rates,while others have been working on the recent trend in which the London Interbank Offered Rate will be retired at the end of 2021.Considering that Turkey announced the Turkish Lira Overnight Reference Interest Rate(TLREF),this study examines the determinants of TLREF.In this context,three global determinants,five country-level macroeconomic determinants,and the COVID-19 pandemic are considered by using daily data between December 28,2018,and December 31,2020,by performing machine learning algorithms and Ordinary Least Square.The empirical results show that(1)the most significant determinant is the amount of securities bought by Central Banks;(2)country-level macroeconomic factors have a higher impact whereas global factors are less important,and the pandemic does not have a significant effect;(3)Random Forest is the most accurate prediction model.Taking action by considering the study’s findings can help support economic growth by achieving low-level benchmark rates.

关键词： Benchmark rate Determinants machine learning algorithms Turkey

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