检索结果-南通市图书馆

Novel Hybrid X GBoost Model to Forecast Soil Shear Strength Based on Some Soil Index Tests

Computer Modeling in Engineering & Sciences 2023年第9期136卷 2527-2550页

作者： ehsan momeni Biao He Yasin Abdi Danial Jahed Armaghani Department of Civil Engineering Faculty of EngineeringLorestan UniversityKhorramabad***Iran Department of Civil Engineering Faculty of EngineeringUniversiti MalayaKuala Lumpur50603Malaysia Department of Geology Faculty of SciencesLorestan UniversityKhorramabad***Iran Department of Urban Planning Engineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State UniversityChelyabinsk454080Russia

When building geotechnical constructions like retaining walls and dams is of interest,one of the most important factors to consider is the soil’s shear strength *** study makes an effort to propose a novel predictive model of shear *** study implements an extreme gradient boosting(XGBoost)technique coupled with a powerful optimization algorithm,the salp swarm algorithm(SSA),to predict the shear strength of various *** do this,a database consisting of 152 sets of data is prepared where the shear strength(τ)of the soil is considered as the model output and some soil index tests(e.g.,dry unit weight,water content,and plasticity index)are set as model *** is designed and tuned using both effective parameters of XGBoost and SSA,and themost accuratemodel is introduced in this *** theSSA-XGBoostmodel is assessedbased on the coefficient of determination(R2)and variance account for(VAF).Overall,the obtained values of R^(2) and VAF(0.977 and 0.849)and(97.714%and 84.936%)for training and testing sets,respectively,confirm the workability of the developed model in forecasting the soil shear *** investigate the model generalization,the prediction performance of the model is tested for another 30 sets of data(validation data).The validation results(e.g.,R^(2) of 0.805)suggest the workability of the proposed ***,findings suggest that when the shear strength of the soil cannot be determined directly,the proposed hybrid XGBoost-SSA model can be utilized to assess this parameter.

关键词： Predictive model salp swarm algorithm soil index tests soil shear strength XGBoost

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Tribological behavior of ZK60Gd alloy reinforced by SiC particles after precipitation hardening

引用

Journal of Magnesium and Alloys 2023年第9期11卷 3362-3381页

作者： ehsan momeni Hassan Sharifi Morteza Tayebi Ahmad Keyvani Ermia Aghaie Yashar Behnamian Department of Materials Science and Engineering University of ShahrekordIran Young Researchers and Elites Club Science and Research BranchIslamic Azad UniversityTehranIran Department of Chemical and Materials Engineering University of AlbertaEdmontonAlberta T6G 1H9Canada

In this research, the effect of precipitation hardening on the tribological behavior of the ZK60Gd/SiC composite was studied. For this purpose, ZK60Gd alloy containing with 5 and 10 wt% SiC were produced with stir casting method. The microstructure characterization of the samples showed the wide distributions of Mg_(7)Zn_(3) and Gd(Mg_(0.5)Zn_(0.5)) precipitates were formed during casting. The results of hardness measurement after precipitation hardening at different temperatures showed that the hardness peck was obtained at 175 ℃. The wear tests with different loads(10, 40, 60, 90, and 120 N) and velocities(0.1, 0.3, 0.6, and 0.9 m/s) were performed on the as-cast and heat treated sample at 125, 175, and 225 for 12 h. Between the different precipitation hardening conditions, the precipitation hardened samples at 175 ℃ had the highest hardness values and least wear rate. The sample containing 10% reinforcement had the least wear rate between the unreinforced alloy and the composites. The results showed that abrasive, adhesive, delamination, MML, and fatigue wear mechanisms were the dominant wear mechanisms for the composite samples. In contrast, the dominant wear mechanism for the unreinforced samples was abrasive, adhesive,delamination, MML, and plastic deformation.

关键词： Precipitation hardening Composite Gadolinium Mg-Zn-Zr-Gd Wear map

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Bearing capacity of thin-walled shallow foundations： an experimental and artificial intelligence-based study

引用

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2016年第4期17卷 273-285页

作者： Hossein REZAEI Ramli NAZIR ehsan momeni Faculty of Engineering Lorestan University Khorram A bad 68151-44316 Iran Department of Geotechnics and Transportation Faculty of Civil Engineering Universiti Teknologi Malaysia Johor 81310 Malaysia

Thin-walled spread foundations are used in coastal projects where the soil strength is relatively low. Developing a predictive model of bearing capacity for this kind of foundation is of interest due to the fact that the famous bearing capacity equations are proposed for conventional footings. Many studies underlined the applicability of artificial neural networks （ANNs） in predicting the bearing capacity of foundations. However, the majority of these models are built using conventional ANNs, which suffer from slow rate of learning as well as getting trapped in local minima. Moreover, they are mainly developed for conventional footings. The prime objective of this study is to propose an improved ANN-based predictive model of bearing capacity for thin-walled shallow foundations. In this regard, a relatively large dataset comprising 145 recorded cases of related footing load tests was compiled from the literature. The dataset includes bearing capacity （Qu）, friction angle, unit weight of sand, footing width, and thin-wall length to footing width ratio （Lw/B）. Apart from Qu, other parameters were set as model inputs. To enhance the diversity of the data, four more related laboratory footing load tests were conducted on the Johor Bahru sand, and results were added to the dataset. Experimental findings suggest an almost 0.5 times increase in the bearing capacity in loose and dense sands when LJB is increased from 0.5 to 1.12. Overall, findings show the feasibility of the ANN-based predictive model improved with particle swarm optimization （PSO）. The correlation coefficient was 0.98 for testing data, suggesting that the model serves as a reliable tool in predicting the bearing capacity.

关键词： Thin-walled foundation Sand Bearing capacity Artificial neural network (ANN) Particle swarm optimization (PSO)

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Chitosan/polyethylene glycol impregnated activated carbons： Synthesis, characterization and adsorption performance

引用

Frontiers of Chemical Science and Engineering 2017年第4期11卷 575-585页

作者： ehsan Salehi Fereshteh Soroush Maryam momeni Aboulfazl Barati Ali Khakpour . Department of Chemical Engineering Faculty of Engineering Arak University Arak 38156-8-8349 Iran.. Department of Electrical Engineering Faculty of Engineering Arak University Arak 38156-8-8349 Iran.. Arian Fan Azma Company Tehran Iran

Novel modified activated carbons （ACs） with enhanced adsorptive properties were obtained coating by chitosan （CS）, polyethylene glycol （PEG） and blends of the two polymers （0：1, 1：0, 1：1, 1：2 and 2：1 wt/wt） on ACs by an impregnation technique. The adsorption perfor- mances of the pristine, acidified and polymer-impregnated ACs were studied using methylene blue as a model adsorbate. The adsorbents were characterized using Four- ier transform infrared spectroscopy, scanning electron microscopy and abrasion hardness tests. The average coating thicknesses were between 10 to 23 microns. The pore sizes, pore densities and pore capacities of the activated carbons increased as the wt-% PEG in the coating increased. The highest adsorption capacity （424.7 mg/g） was obtained for the chitosan-coated ACs and this adsorption was well described by the Langmuir isotherm model. The kinetic results were best described by the pseudo-second-order kinetic model. The highest rate constant was obtained with the ACs modified with the CS：PEG （2：1） coating and this result was almost 2.6 times greater than that of the unmodified ACs. The CS/PEG impregnated ACs also displayed superior hardness （-90%）, compared to unmodified ACs （-85%）. Overall the chitosan had a greater effect on improving adsorption capacity whereas the polyethylene glycol enhanced the adsorption rate.

关键词： carbon biocomposites impregnation chito-san polyethylene glycol image processing

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

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：