Journal of Science and Transport Technology最新文献_第4页

Forecast of surface chloride concentration of concrete utilizing ensemble decision tree boosted 提出了利用集合决策树预测混凝土表面氯离子浓度的方法

Journal of Science and Transport Technology Pub Date : 2022-03-25 DOI: 10.58845/jstt.utt.2022.en57

A. Tran, Thanh-Hai Le, Huu May Nguyen

{"title":"Forecast of surface chloride concentration of concrete utilizing ensemble decision tree boosted","authors":"A. Tran, Thanh-Hai Le, Huu May Nguyen","doi":"10.58845/jstt.utt.2022.en57","DOIUrl":"https://doi.org/10.58845/jstt.utt.2022.en57","url":null,"abstract":"This study proposes the application of Ensemble Decision Tree Boosted (EDT Boosted) model for forecasting the surface chloride concentration of marine concrete A database of 386 experimental results was collected from 17 different sources covering twelve variables was used to build and verify the predictive power of the EDT model. The input factors considered the changes in eleven variables, including the contents of cement, fly ash, blast furnace slag, silica fume, superplasticizer, water, fine aggregate, coarse aggregate, annual mean temperature, chloride concentration in seawater, and exposure time. The results indicate that EDT Boosted is a good predictor of as verified via good performance evaluation criteria, i.e., R2, RMSE, MAE, MAPE values were 0.84, 0.16, 0.17, and 17%, respectively. Partial dependence plot (PDP) was then developed to correlate the eleven input variables with the . PDP implied that the strongest factor affecting Cs was the amount of fine aggregate content, chloride concentration, exposure time, amount of cement, and water, which is useful for material engineers in the design of the grade.","PeriodicalId":117856,"journal":{"name":"Journal of Science and Transport Technology","volume":"45 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132370488","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

Prediction and sensitivity analysis of self compacting concrete slump flow by random forest algorithm 自密实混凝土坍落度流动的随机森林算法预测及敏感性分析

Journal of Science and Transport Technology Pub Date : 2022-03-20 DOI: 10.58845/jstt.utt.2022.en58

Raghvendra Kumar, Hai-Van Thi Mai

{"title":"Prediction and sensitivity analysis of self compacting concrete slump flow by random forest algorithm","authors":"Raghvendra Kumar, Hai-Van Thi Mai","doi":"10.58845/jstt.utt.2022.en58","DOIUrl":"https://doi.org/10.58845/jstt.utt.2022.en58","url":null,"abstract":"Self-compacting concrete (SCC) is a construction material with many advantages, including high performance and the capacity to self-compact without mechanical vibration. As a result, SCC is widely used in construction, especially at locations where concrete structures are difficult to construct. Filling ability is one of the three basic requirements that must be met when designing the SCC mix. The slump flow (SF) is used to determine the SCC mixture's filling capacity. As a result, it is critical to estimate this number fast and precisely. The purpose of this study is to propose the use of a random forest (RF) model to predict the SF of SCC and to assess the effect of input parameters on output parameters. The study constructed the RF model using a dataset of 507 experimental results collected, which is the biggest data collection compared to previous studies on this subject. Additionally, a 10-fold cross-validation approach is used to improve the model's prediction performance. As a result, the performance assessment criteria for the testing dataset have values of RMSE = 59.5664 mm, MAE = 32.4483 mm, and R = 0.8614, respectively. This result shows that the RF model is an effective tool in predicting the SF of SCC.","PeriodicalId":117856,"journal":{"name":"Journal of Science and Transport Technology","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130931458","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

Application of an artificial neural network and QCM sensor coated with γ-Fe2O3 nanoparticles for estimation of SO2 gas sensing characteristics 人工神经网络与γ-Fe2O3纳米粒子包覆QCM传感器在SO2气敏特性估计中的应用

Journal of Science and Transport Technology Pub Date : 2022-03-14 DOI: 10.58845/jstt.utt.2022.en59

Vinh Nguyen Thanh, Tran Quoc Tuan, Nguyen Van Cuong, Cao Xuan Truong, Nguyen Van Quy

引用次数: 1

Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization 基于人工神经网络的贝叶斯正则化预测高温条件下混凝土抗压强度

Journal of Science and Transport Technology Pub Date : 2022-02-12 DOI: 10.58845/jstt.utt.2022.en55

M. Hadzima-Nyarko, Son Hoang Trinh

{"title":"Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization","authors":"M. Hadzima-Nyarko, Son Hoang Trinh","doi":"10.58845/jstt.utt.2022.en55","DOIUrl":"https://doi.org/10.58845/jstt.utt.2022.en55","url":null,"abstract":"Cement concrete is the most commonly used material today for constructing residential or commercial buildings, industrial parks, or particular components such as tunnel slabs where there is a high risk of fire. This structure requires concrete to be subjected to high temperatures generated by fires. However, concrete under the influence of high temperature has very complex behavior states with deformations, physical and chemical changes as the temperature rises dramatically. In this study, an artificial neural network-based Bayesian regularization (ANN) model is proposed to predict the compressive strength of concrete. The database in this study includes 208 experimental results synthesized from laboratory experiments with 9 input variables related to temperature change and design material composition. The performance of the ANN model was evaluated using K-fold cross-validation and statistical criteria, including mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results show that the proposed ANN model is a reasonable, highly accurate, and useful prediction tool for saving time and minimizing costly experiments.","PeriodicalId":117856,"journal":{"name":"Journal of Science and Transport Technology","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123323210","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

Optimizing the architecture of the artificial neural network by genetic algorithm to improve the predictability of pile bearing capacity based on CPT results 利用遗传算法优化人工神经网络结构，提高基于CPT结果的桩承载力可预测性

Journal of Science and Transport Technology Pub Date : 2022-01-27 DOI: 10.58845/jstt.utt.2022.en44

T. Pham, Huong-Lan Thi Vu

{"title":"Optimizing the architecture of the artificial neural network by genetic algorithm to improve the predictability of pile bearing capacity based on CPT results","authors":"T. Pham, Huong-Lan Thi Vu","doi":"10.58845/jstt.utt.2022.en44","DOIUrl":"https://doi.org/10.58845/jstt.utt.2022.en44","url":null,"abstract":"This paper presents the results of applying the Artificial Neural Network (ANN) model in determining pile bearing capacity. The traditional methods used to calculate the bearing capacity of piles still have many disadvantages that need to be overcome such as high cost, complicated calculation, time-consuming. Currently, Artificial Intelligence (AI) is a useful tool that is applied in many fields to save time and costs. The study develops an ANN model and optimizes the architecture, using the Genetic Algorithm (GA) to determine the pile bearing capacity. A dataset of 108 pile static compression results is used to train and test the model. The results of the study are compared with the experimental formula according to Vietnamese nation standard TCVN 10304:2014, showing that the ANN model with well optimized, allowing prediction of pile bearing capacity close to experimental results and better than the formula in nation standard. Specifically, the ANN model gives 12% and 32.4% better performance, respectively, than the empirical formula on R2 and RMSE criteria, respectively. The results of the study are a premise for the application of AI in solving pile problems in the field of construction.","PeriodicalId":117856,"journal":{"name":"Journal of Science and Transport Technology","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132799962","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

Estimation of California Bearing Ratio of Soils Using Random Forest based Machine Learning 基于随机森林的机器学习估算加州土壤承载比

Journal of Science and Transport Technology Pub Date : 2021-12-18 DOI: 10.58845/jstt.utt.2021.en14

Dung Quang Vu, D. Nguyen, Quynh-Anh Thi Bui, Duong Kien Trong, Indra Prakash, B. Pham

{"title":"Estimation of California Bearing Ratio of Soils Using Random Forest based Machine Learning","authors":"Dung Quang Vu, D. Nguyen, Quynh-Anh Thi Bui, Duong Kien Trong, Indra Prakash, B. Pham","doi":"10.58845/jstt.utt.2021.en14","DOIUrl":"https://doi.org/10.58845/jstt.utt.2021.en14","url":null,"abstract":"California Bearing Ratio (CBR) is an essential parameter utilized to evaluate the strength of the soil subgrades and base course materials of different types of pavements. In this study, the Machine Learning (ML) approach has been adopted using Random Forest (RF) model to estimate the CBR of the soil based on 10 input parameters such as Plasticity Index (PI), Liquid Limit (LL), Silt Clay content (SC), Fine Sand content (FS), Coarse sand content (CS), Optimum Water Content (OWC), Organic content (O), Plastic Limit (PL), Gravel content (G), and Maximum Dry Density (MDD), which can be easily determined in the laboratory. An experimental database was collected from 214 soil samples, which were classified according to AASHTO M 145(clayey, gravel, sand, silty and clayey soils). The data was divided into 70% training and 30% test data in the model study. Model performance was evaluated using standard statistical measures such as coefficient of determination, correlations, and errors (relative error, MAE, and RMSE). Based on the analysis results shows the RF model is capable of correct prediction of the CBR of the Soil.","PeriodicalId":117856,"journal":{"name":"Journal of Science and Transport Technology","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126009000","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: 6

Estimation of the bond strength between FRP and concrete using ANFIS and hybridized ANFIS machine learning models 使用ANFIS和混合ANFIS机器学习模型估计FRP与混凝土之间的粘结强度

Journal of Science and Transport Technology Pub Date : 2021-12-08 DOI: 10.58845/jstt.utt.2021.en9

Thuy-Anh Nguyen, H. Ly

引用次数: 1

Nonlinear buckling and postbuckling of spiral stiffened FG-GPLRC cylindrical shells subjected to torsional loads 螺旋加筋FG-GPLRC圆柱壳在扭转载荷作用下的非线性屈曲和后屈曲

Journal of Science and Transport Technology Pub Date : 2021-11-25 DOI: 10.58845/jstt.utt.2021.en8

Kha Hoa Le, T. H. Vu, Hong Quan Pham, H. Vu

引用次数: 1

Urban Railway Development in Hanoi and the Possible Impacts on Mode Shifting: Experiences from Young Transport Users 河内城市铁路发展及其对模式转换的可能影响:来自年轻交通用户的经验

Journal of Science and Transport Technology Pub Date : 2021-11-17 DOI: 10.58845/jstt.utt.2021.en5

Thi My Dung Truong, Minh Ngoc An

引用次数: 0

Prediction of California Bearing Ratio (CBR) of Stabilized Expansive Soils with Agricultural and Industrial Waste Using Light Gradient Boosting Machine 利用光梯度增压机预测工农业废弃物稳定膨胀土的加州承载比

Journal of Science and Transport Technology Pub Date : 2021-09-27 DOI: 10.58845/jstt.utt.2021.en3

Van Quan, H. Do

{"title":"Prediction of California Bearing Ratio (CBR) of Stabilized Expansive Soils with Agricultural and Industrial Waste Using Light Gradient Boosting Machine","authors":"Van Quan, H. Do","doi":"10.58845/jstt.utt.2021.en3","DOIUrl":"https://doi.org/10.58845/jstt.utt.2021.en3","url":null,"abstract":"Using agricultural and industrial waste such as bagasse ash, groundnut shell ash and coal ash in stabilizing expansive soils are used as a subgrade material to reduce harmful impaction of swelling/shrinkage of expansive soils, reduce construction costs. It is also a solution for environmental protection. California Bearing Ratio (CBR) is an important criterion to evaluate the application technique of stabilized expansive soil such as road construction, building construction, highway construction, airport construction, etc. Using the traditional method such as experimental methods or empirical approach, the estimation of CBR of stabilized expansive soils is costly, time consuming for the experiment or low accuracy for empirical method. In this investigation, open-source code of Machine Learning technique Light Gradient Boosting Machine algorithm is introduced to predict the CBR. In order to build model, data of 207 experimental samples was synthesized from the literature to create a database.\u0000The database consists of 6 input variables (ash content, ash type, liquid limit LL, plastic limit PL, optimum moisture content OMC and maximum dry density MDD) to obtain output variable CBR. The results show that the LightGBM model can successfully predict the CBR of stabilized expansive soils with high accuracy. The ash content is the most important input factor for CBR prediction using LightGBM model. In order of importanc input factor affecting CBR prediction are ash content, MDD, ash type, OMC, LL, PL.","PeriodicalId":117856,"journal":{"name":"Journal of Science and Transport Technology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130258874","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: 11