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Thermal post-buckling measurement of the advanced nanocomposites reinforced concrete systems via both mathematical modeling and machine learning algorithm

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ADVANCES IN NANO RESEARCH 2024年第6期16卷 623-638页

作者： Zhou, Minggui Yan, Gongxing Hu, Danping Mahmoud, Haitham A. Luzhou Vocat & Tech Coll Sch Intelligent Construct Luzhou 646000 Sichuan Peoples R China Luzhou Key Lab Intelligent Construct & Low Carbon Luzhou 646000 Peoples R China King Saud Univ Coll Engn Ind Engn Dept Riyadh 11421 Saudi Arabia

This study investigates the thermal post-buckling behavior of concrete eccentric annular sector plates reinforced with graphene oxide powders (GOPs). Employing the minimum total potential energy principle, the plates' stability and response under thermal loads are analyzed. The Haber-Schaim foundation model is utilized to account for the support conditions, while the transform differential quadrature method (TDQM) is applied to solve the governing differential equations efficiently. The integration of GOPs significantly enhances the mechanical properties and stability of the plates, making them suitable for advanced engineering applications. Numerical results demonstrate the critical thermal loads and post-buckling paths, providing valuable insights into the design and optimization of such reinforced structures. This study presents a machine learning algorithm designed to predict complex engineering phenomena using datasets derived from presented mathematical modeling. By leveraging advanced data analytics and machine learning techniques, the algorithm effectively captures and learns intricate patterns from the mathematical models, providing accurate and efficient predictions. The methodology involves generating comprehensive datasets from mathematical simulations, which are then used to train the machine learning model. The trained model is capable of predicting various engineering outcomes, such as stress, strain, and thermal responses, with high precision. This approach significantly reduces the computational time and resources required for traditional simulations, enabling rapid and reliable analysis. This comprehensive approach offers a robust framework for predicting the thermal post-buckling behavior of reinforced concrete plates, contributing to the development of resilient and efficient structural components in civil engineering.

关键词： advanced nanocomposites concrete eccentric systems machine learning algorithm TDQM thermal post buckling

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Building a machine learning algorithm-Based Model to Suggest Tourist Attractions in Response to Travelers' "Slow Life" Requirements

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JOURNAL OF CASES ON INFORMATION TECHNOLOGY 2025年第1期27卷

作者： Li, Xiuxia Xinxiang Univ Urumqi Peoples R China

In order to promote the development of personalized recommendation system in the tourism industry, this study adopts a machine learning collaborative filtering algorithm model, and uses data provided by users and data mining technology to reduce the overload of Internet messages and provide customized content recommendations. The construction of tourist attractions recommendation model based on machine learning is 18% better than the traditional algorithm under the demand of tourists' "slow life". This research can effectively solve the problems of sparse data and cold start. It provides users with more personalized and people-centered travel route recommendations, which significantly enhancing the travel experience.

关键词： Slow Living machine learning algorithm Tourist Attraction Recommendation Model

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Application of functionally graded graphene nanoplatelets to improve transient vibrations of the car's roof under mechanical loading: Introducing machine learning algorithm for transient problems

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MATERIALS TODAY COMMUNICATIONS 2024年 39卷

作者： Chen, Yeye Bi, Shaoping Zhang, Enming Fouly, Ahmed Awwad, Emad Mahrous Quzhou Coll Technol Quzhou 324000 Peoples R China King Saud Univ Coll Engn Dept Mech Engn POB 800 Riyadh 11421 Saudi Arabia King Saud Univ Coll Engn Dept Elect Engn POB 800 Riyadh 11421 Saudi Arabia

Doubly curved structures under external force due to their applicable structure can be used in the roofs of various structures. Good knowledge about the transient dynamics of this kind of structure under external shock loading is very important in the engineering field. So, in the current work, steady and transient vibrations of composite doubly curved structures under time-dependent external force for the first time are presented. The current structure is reinforced by graphene nanoplatelets with high efficiency to improve the mechanical properties of the system in the thickness direction due to external shock loading in this direction. The peak pulse pressure, the Laplace transform, and some mathematical work to get time-dependent vibration responses of the composite doubly curved structures under external force. With the aid of different pre-processing of machine learning algorithms (Z-Score, Rrobust, Min-Max, and Quantile transformation), the results are predicted. The optimization algorithm, neural network structure, learning rate, mean of square error, and other training data are obtained for the current machine learning algorithm. The outputs of the machine learning method show that instead of simulating the complex structures in various situations with the aid of mathematical simulation, the researchers can use machine learning algorithms to correctly simulate this kind of system in different situations and material properties to obtain optimum conditions for doubly curved structure structures. The findings in the results section suggest that the time-dependent displacement and stress fields of the system are significantly influenced by the geometrical and physical characteristics of the current composite curved structure.

关键词： Improving transient response Elasticity theory Multi-layer graphene sheets machine learning algorithm Mechanical loading

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A framework for prediction of extrusion responses using machine learning algorithm

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2024年 1-14页

作者： Manohar, Grandhi Francy, K. Anupama Rao, Ch. Srinivasa Andhra Univ Coll Engn Visakhapatnam 530003 Andhra Pradesh India Vishnu Inst Technol Bhimavaram 534202 Andhra Pradesh India

The objective of the present study is to develop a Customized Automated machine learning (CAML) framework to support machine learning models in manufacturing processes. The proposed CAML framework features a user-friendly interface that enables users to perform key tasks and predict outcomes based on user-defined process parameters. In the present study, machine learning-based prediction of output responses is employed to estimate the process parameters during the cold extrusion process. The input responses are Die angle, Ram speed and Coefficient of Friction while the output responses are Extrusion Force, Damage Factor, Displacement of work piece and Extrusion Time. The framework utilized various machine learning algorithms, including Linear Regression, Ridge, Lasso, Elastic Net, Polynomial Regression, Gaussian Process Regressor, XGB Regressor, LGBM Regressor, Random Forest, Gradient Boosting Regressor, AdaBoost Regressor, Bagging Regressor, Extra-Trees Regressor, KNN Regressor, Nu SVR, Support Vector Regression (SVR), Kernel Ridge, RANSAC Regressor, Huber Regressor, LarsCV, Orthogonal Matching Pursuit, and Bayesian Ridge. The evaluation was performed by analyzing the aggregate R-2 score and aggregate Root Mean Absolute Error (RMSE). Additionally, for optimization, a machine learning-based algorithm, Multimodal Optimization NSGA-2 (Non-dominated Sorting Genetic algorithm 2), is employed to predict extrusion process parameters and enhance the efficiency of the actual extrusion operation. This approach bridges the gap between simulation results and real-world production systems. For Extrusion Force, Displacement of work piece and Extrusion Time. accuracies are 99.38%, 99.80%, and 99.25% respectively, with error percentages below 1% for GBR. However, the Damage Factor, with smaller values 0.017, 0.014), showed higher error (8.24%), where XGBR proved more consistent. The training of all machine learning models (MLMs) took 17.89645 s based on R2 and 18.9131 s based on R

关键词： CAML framework machine learning algorithm Non-dominated Sorting Genetic algorithm 2 Cold extrusion process

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Performance analysis and prediction of hybrid battery thermal management system integrating PCM with air cooling based on machine learning algorithm

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APPLIED THERMAL ENGINEERING 2024年 257卷

作者： Zhang, Ning Zhang, Zhiyuan Li, Jintao Cao, Xing Qingdao Univ Sci & Technol Coll Electromech Engn Qingdao 266061 Peoples R China

Targeted at addressing the battery temperature concerns of rapid discharging and extreme ambient temperature, a hybrid battery thermal management system (BTMS) integrating phase change material (PCM) with air cooling is introduced, in which the Cantor fractal fin and segmented PCM block are employed to enhance thermal management capacity. Through the numerical simulation, the effects of fin parameters, filling strategy of segmented PCM block and airflow direction are analyzed. Moreover, the machine learning algorithm is adopted to predict the thermal control of hybrid BTMS. The results demonstrate that compared to the rectangular fin, the Cantor fractal fin reduces the maximum temperature (T-max) of batteries by 0.53 K, and continually increasing the fractal number of fin improves the cooling performance, but the improvement extent is restricted. The strategy that the middle section is filled with RT35 and the bilateral section is filled with RT35HC provides better temperature management capacity, and so does the strategy that the PCM filling proportion of middle section maintains at 0.5. Applying the segmented PCM block, the T-max and maximum temperature difference (Delta T-max) of batteries are kept at 308.43 K and 1.64 K at 4C discharge rate respectively. Varying airflow direction hardly affects the T-max, however the Delta T-max is obviously decreased when the reverse flow is utilized. The Delta T-max with reverse flow is reduced by 17 % and 21.2 % separately in contrast to those with concurrent flow and staggered flow. The proposed hybrid BTMS owns superior cooling and insulating capacities under rapid discharge rate, high ambient temperature and low ambient temperature. Based on the machine learning algorithm, a back propagation neural network is established and can accurately predict T-max and Delta T-max of batteries.

关键词： Battery thermal management Phase change material Air cooling Cantor fractal fin machine learning algorithm

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Identification of Mild Cognitive Impairment by machine learning algorithm

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NEURAL COMPUTING & APPLICATIONS 2023年第36期35卷 25121-25130页

作者： Li, Haozhen Univ Southern Calif Leonard Davis Sch Gerontol Los Angeles CA 90089 USA

With the aging of the population, China has a large population base. The number of people suffering from mild cognitive impairment has gradually increased and gradually turned to senile dementia. The probability of mild cognitive impairment in China is about 5-7%, and about 15 million people are ill. To better distinguish mild cognitive impairment from Alzheimer's, this paper adopted the machine learning (ML) method to model. The evaluation table was established by considering the reaction time, education, background, memory, and other aspects of the patient. machine learning has been applied in the fields of the Internet, finance, medicine, automation, biological science, etc. Through the machine learning support vector machine (SVM) and linear regression, the classification accuracy was compared. The results showed that the classification accuracy of SVM was 87.92% under the ML algorithm. It also showed that ML was more conducive to classification and recognition, which has played an important role in the identification of mild cognitive impairment in the current aging population.

关键词： Mild cognitive improvement machine learning algorithm Population aging Support vector machines

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Research on Prediction model of Carotid-Femoral Pulse Wave Velocity: Based on machine learning algorithm

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JOURNAL OF CLINICAL HYPERTENSION 2025年第3期27卷 e70017页

作者： Chen, Minghui Xiong, Jing Li, Moran Hu, Tao Zhang, Yi Univ Shanghai Sci & Technol Sch Hlth Sci & Engn Shanghai Peoples R China Tongji Univ Shanghai Peoples Hosp 10 Sch Med Dept Cardiol Shanghai Peoples R China Fourth Mil Med Univ Xijing Hosp Dept Cardiol Xian Shanxi Peoples R China

Carotid-femoral pulse wave velocity (cf-PWV) is an important but difficult to obtain measure of arterial stiffness and an independent predictor of cardiovascular events and all-cause mortality. The objective of this study was to develop a predictive model for cf-PWV based on brachial-ankle pulse wave velocity (baPWV) and other the accessible clinical *** model aims to allow patients to estimate their cf-PWV in advance without the need for direct measurement. We selected participants of the Northern Shanghai community from 2013 to 2022 as the study object. The Pearson correlation coefficient was employed for correlation analysis in feature selection. The linear regression models demonstrated low root mean square error (RMSE), error term (epsilon), and R2 values, indicating good predictive performance. A Cox proportional hazards model revealed a significant association between machine learning-predicted cf-PWV and mortality risk, supporting the validity of prediction model. Using a threshold of cf-PWV greater than 10 m/s as the criterion, a classification prediction model was developed. Shapley Additive Explanations (SHAP) analysis was then applied to the Gradient Boosting model to elucidate the predictive mechanism of the optimal model. Without precise instruments, doctors often cannot determine a patient's cf-PWV. When the cf-PWV value predicted by the machine learning algorithm is high, patients can be recommended for more precise measurements to confirm the prediction and emphasize the importance of follow-up health management and psychological support. It is feasible to use a machine learning algorithm based on baPWV and other readily available clinical parameters to predict cf-PWV.

关键词： cardiovascular patients cervical and femoral pulse velocity machine learning algorithm

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Establishment and validation of a risk stratification model for stroke risk within three years in patients with cerebral small vessel disease using a combined MRI and machine learning algorithm

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SLAS TECHNOLOGY 2024年第5期29卷 100177页

作者： Yang, Xiaolong Chang, Hui Yanan Univ Affiliated Hosp Cardiocerebrovasc Dis Hosp Dept Radiol Yanan 716000 Shaanxi Peoples R China Yanan Univ Cardiocerebrovasc Dis Hosp Affiliated Hosp Dept Anesthesiol Operat Room Yanan 716000 Shaanxi Peoples R China

Background: Cerebral small vessel disease (CSVD) is a major cause of stroke, particularly in the elderly population, leading to significant morbidity and mortality. Accurate identification of high-risk patients and timing of stroke occurrence plays a crucial role in patient prevention and treatment. The study aimed to establish and validate a risk stratification model for stroke within three years in patients with CSVD using a combined MRI and machine learning algorithm approach. Methods: The assessment encompassed demographic, clinical, biochemical, and MRI-derived parameters. Correlation analysis, logistic regression, receiver operating characteristic (ROC) curve analysis, and nnet neural network algorithm were employed to evaluate the predictive value of machine learning algorithms and MRI parameters for stroke occurrence within 3 years in patients with CSVD. Results: MRI-derived parameters, including average WMH volume, perfusion deficit volume, ischemic core volume, microbleed count, and perivascular spaces, exhibited strong correlations with stroke occurrence (P < 0.001). MRI-derived parameters demonstrated high sensitivities (0.719 to 0.906), specificities (0.704 to 0.877), and AUC values (0.815 to 0.871). The combined model of machine learning algorithms and MRI parameters yielded an AUC value of 0.925, indicating significantly high predictive accuracy for identifying the risk of stroke within three years in CSVD patients. Conclusion: The integrated risk stratification model, incorporating machine learning algorithms and MRI parameters, demonstrated strong predictive potential for stroke within three years in patients with CSVD. This model offered valuable insights for personalized interventions and clinical decision-making in the management of CSVD.

关键词： Risk stratification model Stroke Cerebral small vessel disease MRI machine learning algorithm

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Application of machine learning algorithm and data evaluation in computer network security situation awareness technology

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第4期18卷 2827-2839页

作者： Zhang, Xuxia Chen, Weijie Wang, Jian Fang, Rang Shandong Elect Power Coll State Grid Tech Coll Jinan 250001 Shandong Peoples R China

With the rapid development of information technology and the rapid popularization of the Internet, while people enjoy the convenience and efficiency brought about by new technologies, they are also suffering from the harm caused by cyber attacks. In addition to efficiently thwarting network assaults, a high volume of complicated security event data might unintentionally increase the strain of policy makers. At present, NS threats mainly include network viruses, trojans, DOS (Denial-Of-Service), etc. For the increasingly complex Network Security (NS) problems, the traditional rule-based network monitoring technology is difficult to predict the unknown attack behavior. Environment-based, dynamic and integrated data fusion can integrate data from a macro perspective. In recent years, machine learning (ML) technology has developed rapidly, which could easily train, test and predict existing third-party models. It uses ML algorithms to find out the association between data rather than manually sets rules. Support vector machine is a common ML method, which can predict the security of the network well after training and testing. In order to monitor the overall security status of the entire network, NS situation awareness refers to the real-time and accurate reproduction of network attacks using the reconstruction approach. Situation awareness technology is a powerful network monitoring and security technology, but there are many problems in the existing NS technology. For example, the state of the network cannot be accurately detected, and its change rule cannot be understood. In order to effectively predict network attacks, this paper adopted a technology based on ML and data analysis, and constructed a NS situational awareness model. The results showed that the detection efficiency of the model based on ML and data analysis was 7.18% higher than that of the traditional NS state awareness model.

关键词： Network security machine learning algorithm situation awareness data analysis

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Preparation process and performance of polyurethane modified bitumen investigated using machine learning algorithm

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第7期56卷 6775-6800页

作者： Lu, Pengzhen Huang, Simin Zhou, Chenhao Xu, Zijie Wu, Ying Zhejiang Univ Technol Hangzhou 310014 Zhejiang Peoples R China Jiaxing Nanhu Univ Jiaxing 314001 Zhejiang Peoples R China

In the preparation of high-performance polyurethane (PU) modified bitumen, due to the different kinds of PU modifiers, the design parameters of the preparation process are numerous, and indexes of the performance response need to be selected. As a result, the preparation process of PU-modified bitumen is not universally applicable. Therefore, according to different application environments, how determining the process parameters of the PU-modified bitumen accurately and efficiently is a key problem to be solved urgently. Based on fthe Kriging-PSO hybrid optimization algorithm, this paper proposed a novel design method for the preparation process for the PU-modified bitumen. The response indicators with high relative sensitivity (softening point, rutting factor, Brookfield viscosity, and dispersion coefficient) were screened by using range and variance analysis to improve the fitting accuracy of the Kriging-PSO model after training. Among them, the dispersion coefficient was evaluated by fluorescence microscopy test using the Christiansen coefficient method to evaluate the uniformity of the dispersed phase of the PU modifier. Through the Kriging-PSO algorithm, the main process parameters for preparing PU-modified bitumen in the laboratory were determined as follows: shearing time 86 min, shearing speed 2450 rpm, shearing temperature 148, and PU content 18.6%. The prepared PU-modified bitumen was placed in an oven at 100 for 2 h. The performance indicators of PU modified bitumen were: softening point 90, rutting factor 30 kPa, Brookfield viscosity 80,000 Pa center dot s, and dispersion coefficient 0.92. The PU-modified bitumen prepared by this optimal process met the expected performance indicators. The results of this paper showed that the Kriging-PSO algorithm provided a new idea for the design of a modified bitumen preparation process and achieve the purpose of designing the optimum process parameters of PU modified bitumen efficiently using fewer samples. Meanwhil

关键词： Polyurethane modified bitumen Preparation process machine learning algorithm Kriging-PSO model

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