检索结果-南通市图书馆

Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling with Relief algorithm

在线全文

学校读者我要写书评

暂无评论

Frontiers of Structural and Civil Engineering 2022年第2期16卷 224-238页

作者： Hai-Bang LY Huong-Lan Thi VU Lanh Si HO binh thai pham Department of Civil Engineering University of Transport TechnologyHanoi100000Vietnam Civil and Environmental Engineering Program Hiroshima UniversityHiroshima739-8527Japan Department of Science Technology and International CooperationUniversity of Transport TechnologyHanoi100000Vietnam

The consolidation coefficient of soil(C_(v))is a crucial parameter used for the design of structures leaned on soft *** general,the C_(v) is determined experimentally in the ***,the experimental tests are time-consuming as well as ***,researchers tried several ways to determine C_(v) via other simple soil *** this study,we developed a hybrid model of Random Forest coupling with a Relief algorithm(RF-RL)to predict the C_(v) of *** conduct this study,a database of soil parameters collected from a case study region in Vietnam was used for *** performance of the proposed models was assessed via statistical indicators,namely Coefficient of determination(R^(2)),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE).The proposal models were constructed with four sets of soil variables,including 6,7,8,and 13 *** results revealed that all models performed well with a high performance(R^(2)>0.980).Although the RF-RL model with 13 variables has the highest prediction accuracy(R^(2)=0.9869),the difference compared with other models was negligible(i.e.,R^(2)=0.9824,0.9850,0.9825 for the cases with 6,7,8 inputs,respectively).Thus,it can be concluded that the hybrid model of RF-RL can be employed to predict C_(v) based on the basic soil parameters.

关键词： soil consolidation coefficient machine learning random forest Relief

Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization:a case study in Central Vietnam

在线全文

学校读者我要写书评

暂无评论

International Journal of Digital Earth 2016年第11期9卷 1077-1097页

作者： Dieu Tien Bui binh thai pham Quoc Phi Nguyen Nhat-Duc Hoang Geographic Information System Group Department of Business Administration and Computer ScienceUniversity College of Southeast NorwayBøi TelemarkNorway Faculty of Geomatics and Land Administration Hanoi University of Mining and GeologyHanoiVietnam Department of Civil Engineering Gujarat Technological UniversityAhmedabadGujaratIndia Department of Geotechnical Engineering University of Transport TechnologyHa NoiVietNam Department of Environmental Sciences Hanoi University of Mining and GeologyHanoiVietnam Faculty of Civil Engineering Institute of Research and DevelopmentDuy Tan UniversityDanangVietnam

This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction,named as *** LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM *** this research,a GIS database with 129 historical landslide records in the Quy Hop area(Central Vietnam)has been collected to establish the hybrid *** receiver operating characteristic(ROC)curve and area under the curve(AUC)were used to assess the performance of the newly constructed *** results show that the proposed model has high performances with approximately 82%of AUCs on both training and validating *** model’s results were compared with those obtained from other methods,Support Vector Machines,Multilayer Perceptron Neural Networks,and J48 Decision *** result comparison demonstrates that the DE-LSSVMSLP deems best suited for the dataset at hand;therefore,the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.

关键词： Shallow landslide Least-Squares Support Vector Machines differential evolution GIS Vietnam

Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive strength of concrete using rebound hammer field test

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

Frontiers of Structural and Civil Engineering 2022年第8期16卷 1003-1016页

作者： Dung Quang VU Fazal EJALAL Mudassir IQBAL Dam Duc NGUYEN Duong Kien TRONG Indra PRAKASH binh thai pham University of Transport Technology Hanoi 100000Vietnam Department of Civil Engineering Shanghai Jiao Tong UniversityShanghai 200240China Department of Civil Engineering University of Engineering and TechnologyPeshawar 25120Pakistan DDG(R)Geological Survey of India Gandhinagar 382010India

In this study,we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System(ANFIS)optimized by Shuffled Complex Evolution(SCE)on the one hand and ANFIS with Artificial Bee Colony(ABC)on the other *** were used to predict compressive strength(Cs)of concrete relating to thirteen concrete-strength affecting parameters which are easy to determine in the *** and laboratory tests data of 108 structural elements of 18 concrete bridges of the Ha Long-Van Don Expressway,Vietnam were *** dataset was randomly divided into a 70:30 ratio,for training(70%)and testing(30%)of the hybrid *** of the developed fuzzy metaheuristic models was evaluated using standard statistical metrics:Correlation Coefficient(R),Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).The results showed that both of the novel models depict close agreement between experimental and predicted ***,the ANFIS-ABC model reflected better convergence of the results and better performance compared to that of ANFIS-SCE in the prediction of the concrete ***,the ANFIS-ABC model can be used for the quick and accurate estimation of compressive strength of concrete based on easily determined parameters for the design of civil engineering structures including bridges.

关键词： shuffled complex evolution artificial bee colony ANFIS concrete compressive strength Vietnam

A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling:A case study in the Himalayan area, India

在线全文

学校读者我要写书评

暂无评论

International Journal of Sediment Research 2018年第2期33卷 157-170页

作者： binh thai pham Ataollah Shirzadi Dieu Tien Bui Indra Prakash M.B.Dholakia Department of Civil Engineering Gujarat Technological University Geographic Information System Group Department of Business and IT University College of Southeast Norway 5. Department of Science ε Technology Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG) 6. Department of Civil Engineering LDCE Gujarat Technological University Department of Geotechnical Engineering University of Transport Technology Department of Rangeland and Watershed Management College of Natural Resources University of Kurdistan

In this paper, a hybrid machine learning ensemble approach namely the Rotation Forest based Radial Basis Function(RFRBF) neural network is proposed for spatial prediction of landslides in part of the Himalayan area(India). The proposed approach is an integration of the Radial Basis Function(RBF) neural network classifier and Rotation Forest ensemble, which are state-of-the art machine learning algorithms for classification problems. For this purpose, a spatial database of the study area was established that consists of 930 landslide locations and fifteen influencing parameters(slope angle, road density, curvature, land use, distance to road, plan curvature, lineament density, distance to lineaments, rainfall,distance to river, profile curvature, elevation, slope aspect, river density, and soil type). Using the database, training and validation datasets were generated for constructing and validating the model. Performance of the model was assessed using the Receiver Operating Characteristic(ROC) curve, area under the ROC curve(AUC), statistical analysis methods, and the Chi square test. In addition, Logistic Regression(LR), Multi-layer Perceptron Neural Networks(MLP Neural Nets), Na(?)ve Bayes(NB), and the hybrid model of Rotation Forest and Decision Trees(RFDT) were selected for comparison. The results show that the proposed RFRBF model has the highest prediction capability in comparison to the other models(LR, MLP Neural Nets, NB, and RFDT); therefore, the proposed RFRBF model is promising and should be used as an alternative technique for landslide susceptibility modeling.

关键词： Landslide GIS Rotation Forest Radial Base Function Neural Network India

Hybridization of Differential Evolution and Adaptive-Network-Based Fuzzy Inference Systemin Estimation of Compression Coefficient of Plastic Clay Soil

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2022年第1期130卷 149-166页

作者： Manh Duc Nguyen Ha NguyenHai Nadhir Al-Ansari MahdisAmiri Hai-Bang Ly Indra Prakash binh thai pham University of Transport and Communications Hanoi100000Vietnam Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan***Iran University of Transport Technology Hanoi100000Vietnam DDG(R)Geological Survey of India Gandhinagar382010India

One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the *** this study,the main purpose is to develop a novel hybrid Machine Learning(ML)model(ANFIS-DE),which used Differential Evolution(DE)algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System(ANFIS),for estimating soil Compression coefficient(Cc)from other geotechnical parameters namelyWater Content,Void Ratio,SpecificGravity,Liquid Limit,Plastic Limit,Clay content and Depth of Soil *** of the predictive capability of the novel model was carried out using statistical indices:Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Correlation Coefficient(R).In addition,two popular ML models namely Reduced Error Pruning Trees(REPTree)and Decision Stump(Dstump)were used for *** showed that the performance of the novel model ANFIS-DE is the best(R=0.825,MAE=0.064 and RMSE=0.094)in comparison to other models such as REPTree(R=0.7802,MAE=0.068 and RMSE=0.0988)andDstump(R=0.7325,MAE=0.0785 and RMSE=0.1036).Therefore,the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc,which can be employed in the design and construction of civil engineering structures.

关键词： Compression coefficient differential evolution adaptive-network-based fuzzy inference system machine learning vietnam

Flash flood susceptibility mapping using a novel deep learning model based on deep belief network,back propagation and genetic algorithm

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

Geoscience Frontiers 2021年第3期12卷 146-168页

作者： Himan Shahabi Ataollah Shirzadi Somayeh Ronoud Shahrokh Asadi binh thai pham Fatemeh Mansouripour Marten Geertsema John J.Clague Dieu Tien Bui Department of Geomorphology Faculty of Natural ResourcesUniversity of KurdistanSanandaj 66177-15175Iran Board Member of Department of Zrebar Lake Environmental Research Kurdistan Studies InstituteUniversity of KurdistanSanandaj 66177-15175Iran Department of Rangeland and Watershed Management Faculty of Natural ResourcesUniversity of KurdistanSanandajIran Data Mining Laboratory Department of EngineeringCollege of FarabiUniversity of TehranTehranIran Institute of Research and Development Duy Tan UniversityDa Nang 550000Viet Nam British Columbia Ministry of ForestsLandsNatural Resource Operations and Rural DevelopmentPrince GeorgeBC V2L 1R5Canada Department of Earth Sciences Simon Fraser UniversityBurnabyBC V5A 1S6Canada Geographic Information Science Research Group Ton Duc Thang UniversityHo Chi Minh CityViet Nam Faculty of Environment and Labour Safety Ton Duc Thang UniversityHo Chi Minh CityViet Nam

Flash floods are responsible for loss of life and considerable property damage in many *** susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by landuse planners and emergency *** main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach(DBPGA)based on Deep Belief Network(DBN)with Back Propagation(BP)algorithm optimized by the Genetic Algorithm(GA).For this task,a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation(ORAE)*** well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed *** metrics include sensitivity,specificity accuracy,root mean square error(RMSE),and area under the receiver operatic characteristic curve(AUC)were used to assess the validity of the proposed *** result shows that the proposed model has the highest goodness-of-fit(AUC=0.989)and prediction accuracy(AUC=0.985),and based on the validation dataset it outperforms benchmark models including LR(0.885),LMT(0.934),BLR(0.936),ADT(0.976),NBT(0.974),REPTree(0.811),ANFIS-BAT(0.944),ANFIS-CA(0.921),ANFIS-IWO(0.939),ANFIS-ICA(0.947),and ANFIS-FA(0.917).We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.

关键词： Environmental modeling Flash flood Deep belief network Over-fitting Iran

Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy inference system

在线全文

学校读者我要写书评

暂无评论

Frontiers of Structural and Civil Engineering 2023年第5期17卷 812-826页

作者： Long Hoang NGUYEN Dung Quang VU Duc Dam NGUYEN Fazal E.JALAL Mudassir IQBAL Vinh The DANG Hiep Van LE Indra PRAKASH binh thai pham Faculty of Civil Engineering University of Transport TechnologyHanoi 10000Vietnam Department of Civil Engineering State Key Laboratory of Ocean EngineeringShanghai Jiao Tong UniversityShanghai 200025China Department of Civil Engineering University of Engineering and TechnologyPeshawar 25120Pakistan DDG(R)Geological Survey of India Gandhinagar 382010India

A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements,such as the modulus of the subgrade reaction(Y1)and the elastic modulus of the slab(Y2),which are crucial for assessing the structural strength of *** this study,we developed a novel hybrid artificial intelligence model,i.e.,a genetic algorithm(GA)-optimized adaptive neuro-fuzzy inference system(ANFIS-GA),to predict Y1 and Y2 based on easily determined 13 parameters of rigid *** performance of the novel ANFIS-GA model was compared to that of other benchmark models,namely logistic regression(LR)and radial basis function regression(RBFR)*** models were validated using standard statistical measures,namely,the coefficient of correlation(R),mean absolute error(MAE),and root mean square error(RMSE).The results indicated that the ANFIS-GA model was the best at predicting Y1(R=0.945)and Y2(R=0.887)compared to the LR and RBFR ***,the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements.

关键词： falling weight deflectometer modulus of subgrade reaction elastic modulus metaheuristic algorithms

Using Susceptible-Exposed-Infectious-Recovered Model to Forecast Coronavirus Outbreak

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第5期67卷 1595-1612页

作者： Debabrata Dansana Raghvendra Kumar Arupa Parida Rohit Sharma Janmejoy Das Adhikari Hiep Van Le binh thai pham Krishna Kant Singh Biswajeet Pradhan Department of Computer Science and Engineering GIET UniversityGunupurOdishaIndia Department of Electronics&Communication Engineering Faculty of Engineering and TechnologySRM Institute of Science and TechnologyNCR CampusDelhi-NCR CampusModinagarGhaziabadIndia Institute of Research and Development Duy Tan UniversityDa Nang550000Viet Nam University of Transport Technology Hanoi100000Viet Nam Department of ECE KIET Group of InstitutionsGhaziabadIndia Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) University of Technology SydneySydneyNSW2007Australia Department of Energy and Mineral Resources Engineering Sejong UniversitySeoul05006Korea Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600SelangorMalaysia

The Coronavirus disease 2019(COVID-19)outbreak was rst discovered in Wuhan,China,and it has since spread to more than 200 *** World Health Organization proclaimed COVID-19 a public health emergency of international concern on January 30,***,a quickly spreading infection that could jeopardize the well-being of countless individuals requires prompt action to forestall the malady in a timely ***19 is a major threat worldwide due to its ability to rapidly *** vaccines are yet available for *** objective of this paper is to examine the worldwide COVID-19 pandemic,specically studying Hubei Province,China;Taiwan;South Korea;Japan;and Italy,in terms of exposed,infected,recovered/deceased,original conrmed cases,and predict conrmed cases in specic countries by using the susceptible-exposed-infectious-recovered model to predict the future outbreak of *** applied four differential equations to calculate the number of conrmed cases in each country,plotted them on a graph,and then applied polynomial regression with the logic of multiple linear regression to predict the further spread of the *** also compared the calculated and predicted cases of conrmed population and plotted them in the graph,where we could see that the lines of calculated and predicted cases do intersect with each other to give the perfect true results for the future spread of the *** study considered the cases from 22 January 2020 to 25 April 2020.

关键词： COVID-19 SEIR forecasting global pandemic predict conrmed case

Dynamic response of honeycomb-FGS shells subjected to the dynamic loading using non-polynomial higher-order IGA

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

Defence Technology（防务技术） 2024年第7期37卷 149-161页

作者： pham binh Le Trung-Thanh Tran Faculty of Mechanical Engineering Le Quy Don Technical UniversityHanoiViet Nam

The main goal of this study is to use higher-order isogeometric analysis(IGA)to study the dynamic response of sandwich shells with an auxetic honeycomb core and two different functionally graded materials(FGM)skin layers(namely honeycomb-FGS shells)subjected to dynamic ***'s non-polynomial higher-order shear deformation theory(HSDT)is used due to its simplicity and *** governing equation is derived from Hamilton's *** verifying the present approach,the effect of input parameters on the dynamic response of honeycomb-FGS shells is carried out in detail.

关键词： IGA Elastic foundation Dynamic response FGM Auxetic honeycomb Shell