Journal of Science and Transport Technology最新文献_第2页

Effect of Various Factors on Heat Transfer Characteristics of Steady Magnetohydrodynamic Casson Nanofluid (Cu+Water) Between Two Infinite Parallel Plates by Akbari Ganji’s Method Considering Cattaneo–Christov Heat Flux Model 考虑Cattaneo-Christov热流密度模型的Akbari Ganji法研究各因素对稳定磁流体卡森纳米流体(Cu+水)在两个无限平行板间换热特性的影响

Journal of Science and Transport Technology Pub Date : 2023-03-30 DOI: 10.58845/jstt.utt.2023.en.3.1.54-66

A. E. Harfouf, D. Trong, H. Mes-adi, S. Mounir, U. Saraç, Quoc Tuan Tran, V. C. Long, Ș. Ţălu

{"title":"Effect of Various Factors on Heat Transfer Characteristics of Steady Magnetohydrodynamic Casson Nanofluid (Cu+Water) Between Two Infinite Parallel Plates by Akbari Ganji’s Method Considering Cattaneo–Christov Heat Flux Model","authors":"A. E. Harfouf, D. Trong, H. Mes-adi, S. Mounir, U. Saraç, Quoc Tuan Tran, V. C. Long, Ș. Ţălu","doi":"10.58845/jstt.utt.2023.en.3.1.54-66","DOIUrl":"https://doi.org/10.58845/jstt.utt.2023.en.3.1.54-66","url":null,"abstract":"In this study, the influence of various factors on the heat transfer characteristics of the steady magnetohydrodynamic Casson nanofluid (Cu+Water) between two infinite parallel plates considering the Cattaneo–Christov heat flux model is explored by means of the Akbari Ganji’s Method. The values of Nusselt number are also determined for different values of viscosity, magnetic, and volume fraction parameters and various metallic and nonmetallic nanoparticles (NPs). The findings reveal that the temperature profile (TP) decreases with rising casson fluid and thermal relaxation parameters. However, an increment in the TP is detected for large values of the volume fraction parameter, radiation parameter, Prandtl number, and Eckert number. It is found that the varies proportionally with the viscosity and volume fraction parameters, but it is inversely proportional to the magnetic parameter. The results also show that different metallic and nonmetallic NPs have different values of.","PeriodicalId":117856,"journal":{"name":"Journal of Science and Transport Technology","volume":"431 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132298277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temperature effect on the characteristic quantities of microstructure and phase transition of the alloy Ag0.25Au0.75 温度对Ag0.25Au0.75合金显微组织和相变特征量的影响

Journal of Science and Transport Technology Pub Date : 2023-03-30 DOI: 10.58845/jstt.utt.2023.en.3.1.45-53

Ș. Ţălu, T. Quoc, Hoang Van Ong, Ha Thi-Thanh Vu, T. Duyen, Thu-Cuc Thi Nguyen

{"title":"Temperature effect on the characteristic quantities of microstructure and phase transition of the alloy Ag0.25Au0.75","authors":"Ș. Ţălu, T. Quoc, Hoang Van Ong, Ha Thi-Thanh Vu, T. Duyen, Thu-Cuc Thi Nguyen","doi":"10.58845/jstt.utt.2023.en.3.1.45-53","DOIUrl":"https://doi.org/10.58845/jstt.utt.2023.en.3.1.45-53","url":null,"abstract":"In this research, Molecular Dynamics (MD) simulations were conducted to explore the temperature effect on the microstructure and phase transition of the Ag0.25Au0.75 alloy. The findings reveal that as the temperature rises, the material's phase transition switches from crystalline to liquid and vice versa. Notably, during the phase transition, significant changes occur in the link length (r), the total energy of the system (Etot), and the number of structural units FCC, HCP, BCC, and Amor. The microstructural features of the models were analyzed using the radial distribution function (RDF), a number of structural units, shape, size (l), and total energy of the system (Etot). In addition, the length of the link Ag-Ag, Ag-Au, Au-Au, the size of the material has a very small change value and is considered almost constant, and the height of the radial distribution function (RDF) decreases. The number of structural units FCC, HCP decreased, BCC, Amor increased, and the total energy of the system increased, thereby confirming that the influence of temperature on the microstructure and phase transition of the Ag0.25Au0.75 alloy is very large. Besides, the micro-structural characteristics of the Ag0.25Au0.75 alloy can be applied as a basis for future experimental studies.","PeriodicalId":117856,"journal":{"name":"Journal of Science and Transport Technology","volume":"32 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113941466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Estimating the Compressive Strength of Self-compacting Concrete with fiber using an Extreme Gradient Boosting model 用极限梯度增强模型估计纤维自密实混凝土的抗压强度

Journal of Science and Transport Technology Pub Date : 2023-03-30 DOI: 10.58845/jstt.utt.2023.en.3.1.12-26

Indra Prakash, T. Phan, Hai-Van Thi Mai

{"title":"Estimating the Compressive Strength of Self-compacting Concrete with fiber using an Extreme Gradient Boosting model","authors":"Indra Prakash, T. Phan, Hai-Van Thi Mai","doi":"10.58845/jstt.utt.2023.en.3.1.12-26","DOIUrl":"https://doi.org/10.58845/jstt.utt.2023.en.3.1.12-26","url":null,"abstract":"Self-compacting concrete reinforced with fiber (SCCRF) is extensively utilized in the construction and transportation industries due to its numerous advantages, such as ease of building in challenging sites, noise reduction, enhanced tensile strength, bending strength, and decreased structural cracking. Traditional methods for assessing the compressive strength of SCCRF are generally time-consuming and expensive, necessitating the development of a model to forecast compressive strength. This research aimed to predict the CS of SCCRF using the Extreme Gradient Boosting (XGB) machine learning technique. The research uses the grid search method to optimize the XGB model's hyperparameters. A database of 387 samples is collected in this work, which is also the most enormous dataset compared to those utilized in previous studies. An excellent result (R2 max = 0.97798 for the testing dataset) proves that the proposed XGB model has very good predictive power. Finally, a sensitivity analysis using Shapley Additive exPlanations (SHAP values) is conducted to understand the effect of each input variable on the predicted CS of SCCRF. The results show that the age of samples and cement content is the most critical factor affecting the CS. As a result, the proposed XGB model is a valuable tool for helping materials engineers have the right orientation in the design of SCCRF components to achieve the required compressive strength.\u0000 ","PeriodicalId":117856,"journal":{"name":"Journal of Science and Transport Technology","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115737633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Predicting Rubberized Concrete Compressive Strength Using Machine Learning: A Feature Importance and Partial Dependence Analysis 用机器学习预测橡胶混凝土抗压强度:特征重要性和部分依赖分析

Journal of Science and Transport Technology Pub Date : 2023-03-30 DOI: 10.58845/jstt.utt.2023.en.3.1.27-44

Mahdi Hasanipanah, R. Abdullah, Mudassir Iqbal, Hải Bằng Lý

{"title":"Predicting Rubberized Concrete Compressive Strength Using Machine Learning: A Feature Importance and Partial Dependence Analysis","authors":"Mahdi Hasanipanah, R. Abdullah, Mudassir Iqbal, Hải Bằng Lý","doi":"10.58845/jstt.utt.2023.en.3.1.27-44","DOIUrl":"https://doi.org/10.58845/jstt.utt.2023.en.3.1.27-44","url":null,"abstract":"Rubberized concrete is a material that is both ecologically friendly and sustainable, and it has been finding more and more usage in building applications recently. In this study, a machine learning model, namely LightGBM, is developed to predict the compressive strength (CS) of rubberized concrete using 11 input parameters. The performance of the model is measured using a number of different statistical criteria after it has been trained on a dataset containing 275 samples. In order to evaluate the impact that each input parameter has on the CS, feature importance and partial dependency plots (PDP) are used as analytical tools. According to the findings, the superplasticizer, chipped rubber, crumb rubber, coarse aggregate, fine aggregate, and water content all have a significant impact on the CS of rubberized concrete. On the other hand, the results indicate that the cement content, slag/fly ash content, and type of CS have a relatively minor effect. In addition to this, the PDP offers insights into the manner in which the input parameters have an effect on the CS of rubberized concrete. Overall, the developed model and analytic techniques may be used as a helpful tool in forecasting the CS of rubberized concrete and improving its mix design for a variety of construction applications.","PeriodicalId":117856,"journal":{"name":"Journal of Science and Transport Technology","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125519606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing construction progress by evolutionary algorithm considering multi-objective criteria 基于多目标准则的进化算法优化施工进度

Journal of Science and Transport Technology Pub Date : 2023-03-28 DOI: 10.58845/jstt.utt.2023.en.3.1.1-11

Tuan Anh Pham, Dinh Hue Duong

引用次数: 0

Predicting tensile strength of cemented paste backfill with aid of second order polynomial regression 利用二阶多项式回归预测胶结膏体充填体抗拉强度

Journal of Science and Transport Technology Pub Date : 2022-12-30 DOI: 10.58845/jstt-utt.2022.121

Q. T. Ngo, L. Nguyen, Quan Van Tran

{"title":"Predicting tensile strength of cemented paste backfill with aid of second order polynomial regression","authors":"Q. T. Ngo, L. Nguyen, Quan Van Tran","doi":"10.58845/jstt-utt.2022.121","DOIUrl":"https://doi.org/10.58845/jstt-utt.2022.121","url":null,"abstract":"The materials left behind after the process of separating an ore's valuable fraction from the unprofitable fraction are known as tailings in the mining industry. Mixing tailing, cement and water can create a new material called Cemented paste backfill (CPB). Research and solve the problem of predicting the tensile strength of cement paste backfill based on a polynomial model combined with the Monte Carlo Simulation method. Three models were built to evaluate performance. The optimal performance model is then used to predict the tensile strength of cement paste backfill. The results indicate that using the polynomial regression model has a satisfactory result for predicting the tensile strength of cement paste backfill. The best performance of second order polynomial regression model is evaluated by three metrics such as R2=0.958 RMSE=33.211 kPa MAE=29.097 kPa for testing part in predicting the tensile strength of cemented paste backfill. Finally, the influence of Cement/Tailings ratio and Solid content on the tensile strength on tensile strength and importance is also evaluated with aid of the best performance of second order polynomial regression model.","PeriodicalId":117856,"journal":{"name":"Journal of Science and Transport Technology","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126651918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synthesis and CO gas adsorption properties of GO/ZnFe2O4 nanocomposites GO/ZnFe2O4纳米复合材料的合成及其CO气体吸附性能

Journal of Science and Transport Technology Pub Date : 2022-12-27 DOI: 10.58845/jstt.utt.2022.en123

Vinh Thanh Nguyen, Tuan Quoc Tran, Cuong Van Nguyen, Hung Van Nguyen, Hai Thanh Nguyen, Hang Thi Bui, Dang Van Tran, Quy Van Nguyen

引用次数: 0

Forecasting Construction Price Index using Artificial Intelligence Models: Support Vector Machines and Radial Basis Function Neural Network 用人工智能模型预测建筑价格指数:支持向量机和径向基函数神经网络

Journal of Science and Transport Technology Pub Date : 2022-12-27 DOI: 10.58845/jstt.utt.2022.en125

Tuan Thanh Nguyen, Dam Duc Nguyen, Son Duc Nguyen, Indra Prakash, Phong Van Tran, Binh Thai Pham

{"title":"Forecasting Construction Price Index using Artificial Intelligence Models: Support Vector Machines and Radial Basis Function Neural Network","authors":"Tuan Thanh Nguyen, Dam Duc Nguyen, Son Duc Nguyen, Indra Prakash, Phong Van Tran, Binh Thai Pham","doi":"10.58845/jstt.utt.2022.en125","DOIUrl":"https://doi.org/10.58845/jstt.utt.2022.en125","url":null,"abstract":"Estimation of Construction Price Index (CPI) is important for a market economy and it is a measure to manage construction investment costs. This is a tool to help organizations and individuals to reduce the effort and management of expenses for construction projects by reducing time of procedures for calculating and adjusting the total investment for the estimation and evaluation of contract price. The CPI is an indicator that reflects the level of construction price fluctuations of the type of work over time. In this study, the CPI data of Son La province, Vietnam from January 2016 to March 2022 (75 dataset) has been used for the modelling. Two Artificial Intelligence (AI) models namely Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFN) were proposed to predict the CPI based on limited input data. Performance of the models in correctly predicting CPI was evaluated using standard statistical indicators such as Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) based on the historical CPI data. The results show that performance of both the models is good in predicting CPI, but performance of the SVM model (R2 train = 0.915, R2 test = 0.811) is the best in comparison to RBFN model (R2 train = 0.985, R2 test = 0.733). The proposed AI models can be used to quickly and accurately predict the CPI of an area to help management agencies, investors, construction contractors for assessing cost of construction for the purchase and development of properties/ infrastructures.","PeriodicalId":117856,"journal":{"name":"Journal of Science and Transport Technology","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134560147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Development of effective XGB model to predict the Axial Load Capacity of circular CFST columns CFST圆形柱轴向承载力有效的XGB模型的建立

Journal of Science and Transport Technology Pub Date : 2022-12-26 DOI: 10.58845/jstt.utt.2022.en128

Indra Prakash, Raghvendra Kumar, Thuy-Anh Nguyen, Phuong Thao Vu

{"title":"Development of effective XGB model to predict the Axial Load Capacity of circular CFST columns","authors":"Indra Prakash, Raghvendra Kumar, Thuy-Anh Nguyen, Phuong Thao Vu","doi":"10.58845/jstt.utt.2022.en128","DOIUrl":"https://doi.org/10.58845/jstt.utt.2022.en128","url":null,"abstract":"The Axial Load Capacity (ALC) of Concrete-Filled Steel Tubular (CFST) structural members is regarded as one of the most crucial technical factors for the design of these composite structures. This work proposes the development and application of the Extreme Gradient Boosting (XGB) model to forecast the ALC of circular CFST structural components using the affecting input parameters, namely column diameter, steel tube thickness, column length, steel yield strength, and concrete compressive strength. A dataset of 2073 experimental results from the literature was used for the model development. The performance of the XGB model was evaluated using statistical criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Mean Absolute Percentage Error (MAPE). The five-fold cross-validation technique and Monte Carlo simulation method were used to evaluate the model's performance. The results show good performance of the XGB model (R2 = 0.999, RMSE = 242.757 kN, MAE = 157.045 kN, and MAPE = 0.057) in predicting the circular CFST’s ALC.","PeriodicalId":117856,"journal":{"name":"Journal of Science and Transport Technology","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116492162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model design of automatic defective product sorting system 残品自动分拣系统的模型设计

Journal of Science and Transport Technology Pub Date : 2022-12-26 DOI: 10.58845/jstt.utt.2022.en127

Lubos Chovanec, Khanh Quang Duong, Huong Thi Vuong, Lanh Thi Ngo, Nam Thi Nguyen, Dung Duy Tran, Son Thai Nguyen, Long Hai Pham

引用次数: 0