检索结果-内蒙古大学图书馆

A supervised machine learning approach for structural overload classification in railway bridges using weigh-in-motion data

引用

STRUCTURES 2025年 71卷

作者： Le, N. T. Keenan, M. Nguyen, A. Ghazvineh, S. Yu, Y. Li, J. Manalo, A. Univ Southern Queensland Toowoomba Australia Hanoi Univ Civil Engn Hanoi Vietnam KiwiRail Christchurch New Zealand Kharazmi Univ Tehran Iran Univ New South Wales Sydney Australia Univ Technol Sydney Sydney Australia

Weigh-in-motion (WIM) data provides valuable information on vehicle axle load, enabling efficient and economical railway structural safety management programs. However, the current method for assessing structural overload on railway bridges using WIM data is time-consuming and often requires line closure while analyses are being conducted. This paper presents the development of a novel supervised machine learning (ML) approach that can be used as an assessment tool to expedite the decision-making process and minimise economic loss. Variables for model input are carefully considered by analysing real WIM data obtained from measurement sites in New Zealand. Various supervised ML classification models are evaluated for their capability in classifying axle load combinations (ALC) into "Normal", meaning safe to go, or "Overload", meaning that line closure is required for detailed inspection of the affected bridges. It is found that the model using Neural Network (NN) outperforms other candidates in this capacity and is therefore selected for detailed model development. An initial investigation using a small dataset derived from real WIM measurements demonstrates that the NN model can achieve impressive evaluation metrics such as F1-score of 99.2 %. Subsequently, a method for artificially generating synthetic ALC data is proposed to create extensive training datasets for comprehensive structural overload model development. It is demonstrated that with sufficient overload data in the training dataset, the model can achieve an exceptional performance, reaching an F1-score of 99.84 % or higher for a single overload level and 99.5 % to 99.86 % for multiple overload thresholds. The developed model can be integrated into the WIM post-processing systems, providing a real-time bridge overload assessment tool that facilitates more efficient and cost-effective railway structural safety management.

关键词： Weigh-in-Motion Railway Bridge Structural Overload Assessment Axle Load Combination supervised machine learning

来源：评论

学校读者我要写书评

暂无评论

Advancements in supervised machine learning for outdoor thermal comfort: A comprehensive systematic review of scales, applications, and data types

引用

ENERGY AND BUILDINGS 2025年 329卷

作者： Luo, Tianze Chen, Mingze Univ Edinburgh Sch Architecture & Landscape Architecture Edinburgh EH3 9DF Scotland Univ British Columbia Fac Forestry Vancouver BC V6T 1Z4 Canada

supervised machine learning (SML) has a proven track record in addressing the complexities of urban studies. To enhance the accuracy and efficiency of current urban outdoor thermal environment research, many scholars had employed SML methods. However, the current status and limitations of SML applications in this field remained unclear. This paper offered a systematic review of SML use in urban studies based on 58 publications. It recorded the topic, use case, application, data type, and method of each paper, providing statistical insights into trends and evolution. The studies were categorized into three scales, six application types, and six data types. We found that mesoscale studies are the most popular, accuracy comparison is the most common application type, and multiple data sources are the most common input. The Random Forest (RF) algorithm was the most frequently used across categories. A detailed review showed how SML is applied in various outdoor thermal comfort (OTC) studies, suggesting that future research should cover wider study areas and longer time spans. Qualitative methods should also be incorporated into SML research as complementary tools. We discussed SML methods, highlighting algorithm synergy and portability as key areas for future OTC research. The limitations of current applications, such as high computing costs, parameter adjustment complexity, and tedious pretreatment, were identified, along with development directions to address these issues. For input data, we recommended using more dimensional datasets and advanced preprocessing models to enhance prediction accuracy and depth. In conclusion, this review demonstrated SML's potential to effectively solve outdoor thermal comfort problems. It refines and summarizes existing workflows and applications, offering three propositions for future development in this field.

关键词： Outdoor thermal comfort supervised machine learning Algorithm Urban data science

来源：评论

学校读者我要写书评

暂无评论

Optimized supervised machine learning for accurate prediction of periods in Türkiye's heritage minarets

引用

BULLETIN OF EARTHQUAKE ENGINEERING 2025年 1-24页

作者： Tran, Chon Nguyen, Nhan Thanh Vu Le, Duong Thai Nguyen, Quy Thue Livaoglu, Ramazan Nguyen Tat Thanh Univ Fac Civil Engn Ho Chi Minh City Vietnam Bursa Uludag Univ Civil Engn Dept Bursa Turkiye

Historical masonry minarets, known for their tall and slender forms, are especially susceptible to environmental and seismic impacts because of their distinct structural characteristics. Traditional methods, such as three-dimensional numerical modeling, are widely used to evaluate the stability of these structures. However, the complex, heterogeneous materials common in historical construction often lead to significant differences between simulated predictions and actual dynamic behaviors, posing challenges to accurately assessing their stability. This study addresses these issues by introducing a supervised machine learning (SML) approach specifically designed to predict the fundamental period of 27 historical minarets in T & uuml;rkiye. Unlike conventional techniques that depend on extensive field testing or highly detailed numerical models, this SML model utilizes straightforward geometric (such as equivalent height and diameters) and material parameters (Young's modulus and mass density) to achieve accurate predictions with high reliability. Additionally, the input vectors are expanded to include slenderness parameters, significantly enhancing prediction accuracy. Three optimized SML functions are systematically evaluated, with the Grid Search method identified as the most effective approach for this application. The inclusion of slenderness parameters and the use of the Grid Search method yields exceptional prediction performance, demonstrating the outstanding of the proposed methodology compared to some existing empirical equations when achieving prediction error margins below 20%. This framework offers a practical, non-invasive tool for analyzing the dynamic stability and resilience of culturally significant structures, providing a modern, efficient solution for heritage conservation.

关键词： Heritage minarets supervised machine learning Fundamental period Optimizer options Fast prediction

来源：评论

学校读者我要写书评

暂无评论

Predictive quality analytics for the viscosity of water-based architectural paint manufacturing by using improved supervised machine learning and maximum dissimilarity algorithm

引用

JOURNAL OF INTELLIGENT MANUFACTURING 2025年 1-17页

作者： Barrionuevo, Robinson Vallejo-Huanga, Diego Morillo, Paulina Mostafa, Noha A. Univ Politecn Salesiana Prod & Ind Operat Quito 170145 Ecuador Univ Politecn Salesiana IDEIAGEOCA Res Grp Quito 170145 Ecuador Zagazig Univ Ind Engn Dept Zagazig 44519 Egypt British Univ Egypt Mech Engn Dept Cairo 11837 Egypt

Viscosity is a key physical property in the production process of paint. In this research, statistical modeling was used to analyze and predict the viscosity of water-based architectural paint as part of quality control in a coating factory. In this sense, quality technicians constantly seek to improve this indicator. The Viscosity difference is modelled as a function of temperature in the ranges of 19-25 degrees C and 25-32 degrees C. Parametric polynomial regression, ANOVA analysis, residual plots, and Box-Cox transformation were used as statistical tools for data analytics and prediction. Model corrections were applied by using Cochrane-Orcutt transformation and assumptions were tested using the Kolmogorov-Smirnov statistics by Lilliefors, Breusch-Pagan, and Durbin-Watson. Improved Maximum Dissimilarity algorithm with the small group filter and representative initial set selection was used for selecting the best representative data to validate the models and three supervised machine learning methods (Random Forest, K-nearest neighbors, and Gradient-boosted trees) were employed through hyperparameter optimization, it was found that Random Forest gave the best performance. Two regression models were obtained: a second-degree polynomial model for samples with a temperature less than 25 degrees C and a simple linear non-parametric model one for samples at temperature greater than 25 degrees C. Adjusted coefficients of determination are 0.968 and 0.978, respectively. Finally, using the proposed predictive models could reduce the turnaround time by 48.5%.

关键词： Predictive analytics Viscosity measurement Maximum dissimilarity algorithm Multiple regression Quality inspection supervised machine learning

来源：评论

学校读者我要写书评

暂无评论

Estimating oil recovery efficiency of carbonated water injection with supervised machine learning paradigms and implications for uncertainty analysis

引用

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2025年 257卷

作者： Turkson, Joshua Nsiah Yusof, Muhammad Aslam Md Fjelde, Ingebret Sokama-Neuyam, Yen Adams Darkwah-Owusu, Victor Univ Teknol PETRONAS Fac Geosci & Petr Engn Petr Engn Dept Bandar Seri Iskandar 32610 Perak Darul Rid Malaysia Univ Teknol PETRONAS Inst Sustainable Energy & Resources ISER Ctr Reservoir Dynam COReD Bandar Seri Iskandar 32610 Perak Darul Rid Malaysia NORCE Norwegian Res Ctr AS Dept Energy & Technol N-4021 Stavanger Norway Kwame Nkrumah Univ Sci & Technol Fac Civil & Geoengn Dept Petr Engn Kumasi Ghana

Limited efforts have been made to develop a time-efficient and cost-effective predictive model capable of estimating the oil recovery efficiency of carbonated water injection (CWI). Therefore, in this study, we utilized supervised machine learning (ML) techniques: decision tree, support vector regression, and random forest (RF) to predict the recovery efficiency of CWI, with experimental conditions, rock properties, and fluid properties as predictors. The influence of various parameters on oil recovery efficiency was assessed using correlation technique, permutation importance, and Shapley Additive Explanations (SHAP), which sets our study apart from existing studies. Generally, the ML models yielded remarkable recovery efficiency prediction results, achieving coefficients of determination, mean absolute errors, and root mean square errors of 0.81-0.87, 4.30-4.96 %, and 4.82-5.89 %, respectively. The RF model outperformed its counterparts. Most importantly, the RF model successfully predicted the recovery efficiency on entirely new data with an error and absolute relative error of less than 15% and 19% respectively According to the SHAP analysis, high injection rate, porosity, permeability, and pressure improve oil recovery, and vice versa. Similarly, low temperature, oil density and viscosity, and salinity enhance oil recovery while injection rate and temperature were the most and least influential parameters, respectively. The RF model was successfully deployed to predict the oil recovery efficiency for 1000 randomly generated sets of independent variables in conjunction with Monte Carlo simulation, demonstrating the applicability of the model in uncertainty analysis. The current modeling study not only bridges the knowledge gaps in predictive modeling of the oil recovery efficiency of CWI but also holds significant promise for rapid estimation and optimization of oil recovery efficiency.

关键词： Carbonated water injection Monte Carlo simulation Oil recovery efficiency Random forest Shapley additive explanations supervised machine learning

来源：评论

学校读者我要写书评

暂无评论

Using supervised machine learning algorithms to predict bovine leukemia virus seropositivity in dairy cattle in Florida: A 10-year retrospective study

引用

PREVENTIVE VETERINARY MEDICINE 2025年 235卷 106387页

作者： Megahed, Ameer A. Bommineni, Reddy Short, Michael Galvao, Klibs N. Bittar, Joao H. J. Univ Florida Coll Vet Med Dept Large Anim Clin Sci Gainesville FL 32610 USA Benha Univ Fac Vet Med Dept Anim Med Internal Med Toukh 13736 Kalyobiya Egypt Florida Dept Agr & Consumer Sci Div Anim Ind Gainesville FL 32608 USA

supervised machine-learning (SML) algorithms are potentially powerful tools that may be used for screening cows for infectious diseases such as bovine leukemia virus (BLV) infection. Here, we compared six different SML algorithms to identify the most important risk factors for predicting BLV seropositivity in dairy cattle in Florida. We used a dataset of 1279 dairy blood sample records from the Bronson Animal Disease Diagnostic Laboratory that were submitted for BLV antibody testing from 2012 to 2022. The SML algorithms that we used were logistic regression (LR), decision tree (DT), gradient boosting (GB), random forest (RF), neural network (NN), and support vector machine (SVM). A total of 312 serum samples were positive for BLV with corrected seroprevalence of 26.0 %. Subject to limitations of the analyzed retrospective data, the RF model was the best model for predicting BLV seropositivity in dairy cattle indicated by the highest Kolmogorov-Smirnov (KS) statistic of 0.75, area under the receiver operating characteristic (AUROC) of 0.93, gain of 2.6;and lowest misclassification rate of 0.10. The LR model was the worst. The RF model showed that the best predictors for BLV seropositivity were age (dairy cows of age >= 5 years) and geographic location (southern Florida). We concluded that the RF and other SML algorithms hold promise for predicting BLV seropositivity in dairy cattle and that dairy cattle 5 years of age or older raised in southern Florida have a higher likelihood of testing positive for BLV. This study makes an important methodological contribution to the needed development of predictive tools for effective screening for BLV infection and emphasizes the importance of collecting and using representative data in such predictive models.

关键词： Bovine leukemia virus infection Prediction supervised machine learning Dairy cattle

来源：评论

学校读者我要写书评

暂无评论

supervised machine learning models for predicting mechanical properties of dissimilar friction stir welded AA7075-AA5083 aluminum alloys

引用

MEASUREMENT 2025年 246卷

作者： Mothilal, Meghavath Kumar, Atul Vellore Inst Technol Sch Mech Engn Vellore 632014 Tamil Nadu India

The study investigates the supervised machine learning regression-based method to find the ultimate tensile strength of friction stir welded dissimilar AA7075 (Al-Zn) and AA5083 (Al-Mg) joints. The machine learning (ML) models were trained using data from 20 experimental runs involving three input friction stir welding (FSW) process parameters like tool tilt angle 1 - 3 & ring;, welding speed 25---75 mm/min, and rotational speed 900---1300 rpm. Of these, 80 % of the dataset is used for training, and the remaining 20 % is considered for testing the models. Five ML algorithms, Decision Tree (DT), Random Forest, XGBoost, CatBoost, and AdaBoost, were evaluated using dataset 20 of experimental runs to assess their predictive capabilities. The DT model showed outstanding performance, achieving a mean absolute error (MAE) of 14.75, a mean square error (MSE) of 408.63, and an R2 score of 0.97. This means that 97 % of the variance in the ultimate tensile strength (UTS) values was accurately represented by the model, highlighting its robustness and reliability in predictions. The study demonstrates that ML models can effectively predict the UTS with accuracy. The results indicate that the predictive model marks a significant advancement in this field. It allows for more accurate and dependable predictions of essential welding process parameters, which greatly improves the efficiency and quality of welding operations.

关键词： Aluminum Ultimate tensile strength supervised machine learning FSW

来源：评论

学校读者我要写书评

暂无评论

Using supervised machine learning and ICD10 to identify non-accidental trauma in pediatric trauma patients in the Maryland Health Services Cost Review Commission dataset

引用

CHILD ABUSE & NEGLECT 2025年 160卷 107228-107228页

作者： Hou, Titus An, Daniel Hicks, Caitlin W. Haut, Elliott Nasr, Isam W. Univ Illinois Bloomberg Sch Publ Hlth Coll Med Peoria Campus Chicago IL USA Johns Hopkins Sch Med Baltimore MD USA

Background: Identifying non-accidental trauma (NAT) in pediatric trauma patients is challenging. We developed a machine learning model that uses demographic characteristics and ICD10 codes to detect the first diagnosis of NAT. Methods: We analyzed data from the Maryland Health Services Cost Review Commission (2015-2020) for patients aged 0-19 years. Relevant ICD10 codes associated with NAT and trauma were identified. Health records preceding the patients' first trauma diagnosis were analyzed. Random forest models were built using covariates selected through penalized regularization. Models were developed for confirmed and suspected NAT. Data was divided into 80/20 split for model training and testing. We conducted analysis in R. Results: We analyzed 128,351 non-NAT trauma patients, 522 confirmed NAT patients, and 2128 suspected NAT patients totaling 364,217 encounters. Variable selection identified 55 covariates for confirmed NAT and 65 for suspected NAT for model development. These covariates were primarily musculoskeletal injuries of the head and extremities. Model testing results are summarized in Table 1. Conclusion: Our study uses machine learning to identify NAT within the pediatric trauma cohort. Analyzing ICD10 categories before the first traumatic diagnosis may allow for earlier detection of NAT. Additional research in building learning models with ICD10 codes is needed to better understand how clinician and billing biases may impact predictive models. supervised machine learning can potentially augment clinical decision-making and enhance pediatric trauma care.

关键词： Non-accidental trauma supervised machine learning R ICD10

来源：评论

学校读者我要写书评

暂无评论

Indoor Localization using RSSI based supervised machine learning Approaches 5

Indoor Localization using RSSI based Supervised Machine Lear...

引用

5th National and the 1st International Conference on Applied Research in Electrical Engineering, AREE 2025

作者： Fahama, Hassoon Salman Kavian, Yousef S. Asl, Karim Ansari Soorki, Mehdi N. Najaf Technical Institute Al-Furat Al-Awsat Technical University Department of Medical Devices Techniques Najaf540037 Iraq Shahid Chamran University of Ahvaz Faculty of Engineering Department of Electrical Engineering Ahvaz61367 Iran

ISBN: (纸本)9798350377170

Wireless indoor localization has attracted significant interest from industrial and academic domains due to its crucial role in location-based applications. However, localization is not an inherent feature of the ZigBee devices;it is possible to employ wireless technology capabilities for localization. This paper uses machine learning models to map the indoor localization system between the received signal strength indicator (RSSI) and the node position. A system map is created using a fingerprint technique containing an RSSI (input)position(output) pair. To study the influence of RSSI on the localization algorithm, we proposed framework employs four different supervised machine learning models, including K-Nearest Neighbour (KNN), Feed-Forward Neural Network (FF), Cascade-Forward Neural Network (CF), and Long-Short - Term Memory-Recurrent Neural Network (LSTM-RNN). The results showed that Wi-Fi technology achieved the best performance in the CF model over all. © 2025 IEEE.

关键词： Fingerprint Localization RSSI supervised machine learning ZigBee

来源：评论

学校读者我要写书评

暂无评论

Binary or Integer Chromosome: Which Is the Best Structure for supervised machine learning Using Genetic Algorithms?

引用

APPLIED SCIENCES-BASEL 2025年第5期15卷 2608-2608页

作者： Alves, Alexandre Henrick da Silva Coelho Neto, Guilherme Antonio Gomes, Matheus de Souza Santos, Libia Diniz Bertarini, Pedro Luiz Lima do Amaral, Laurence Rodrigues Univ Fed Uberlandia Fac Computat Patos de Minas Campus BR-38701002 Patos DE Minas Brazil Univ Fed Uberlandia Biotechnol Inst Patos de Minas Campus BR-38700128 Patos De Minas Brazil Univ Fed Uberlandia Fac Chem Engn Patos de Minas Campus BR-38700002 Patos De Minas Brazil Univ Fed Uberlandia Fac Elect Engn Patos de Minas Campus BR-38700002 Patos De Minas Brazil

supervised machine learning is widely researched nowadays. Several works have already been developed using genetic algorithms (GAs) for classification tasks evolving IF-THEN classification rules. Oftentimes, these methods are built using integers and real values from one's chromosome structure. In this paper, new and important improvements are proposed to Non-linear Computation Evolutionary Environment (NLCEE), a GA-based rule-set generator proposed by Amaral and Hruschka. The proposed GA, called BIN-NLCEE, uses binary representation in its chromosome structure to simplify its mutation and also produce a higher search space. The main goal is to have a rule-set generator that produces simple and interpretable classification rules with good accuracy values and better converge rates. The BIN-NLCEE performance was compared with other GAs-based and four traditional classifiers in five medical domain datasets. The results showed a better convergence rate and higher fitness values for BIN-NLCEE when compared with the GA-based CEE and NLCEE. In 20 comparisons, BIN-NLCEE achieved better results in 9 (45%), and, according to the confidence interval, equivalent results were obtained in 11 (55%). In this way, BIN-NLCEE was better or equal to NLCEE and CEE in 100% of the comparisons. Also, BIN-NLCEE outperformed all traditional classifiers' results, i.e., achieved better results in 100% of comparisons.

关键词： supervised machine learning genetic algorithms binary chromosome integer and real chromosome IF-THEN rules nonlinear datasets

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：