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Structure, Mechanical Behavior and Puncture Resistance of Grass Carp Scales

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Journal of Bionic Engineering 2017年第2期14卷 356-368页

作者： Peng Liu Deju Zhu Junwen Wang tinh quoc bui College of Civil Engineering Hunan University Changsha 410082 China College of Civil Engineering Tongji University Shanghai 200092 China Department of Civil and Environmental Engineering Tokyo Institute of Technology Tokyo 108-233 Japan

This work is conducted to investigate the hierarchical structure, mechanical behavior and fracture resistance of grass carp scales with different water contents （hydrated and dehydrated） and load conditions （uniaxial, biaxial and punch tests）. The whole cross-section of scales is investigated, and it is found that the bony layer displays discontinuity and partly embeds in collagen layer. Four different locations are considered under both tensile and punch tests. The results of the uniaxial tensile test show a correlation between the failure mode and the distribution of surface morphology on scales. The biaxial test results show that there are minor differences in the tensile strength and the Young＇ modulus compared with those of the uniaxial tests, but the ultimate strain is about 20% - 50%. Puncture tests are also conducted with different size of needles and different hardness silicon rubbers as substrate. The results show that the puncture force and deformation are dependent on the size of needle and the hardness of substrate. The failure pattern of scales is related to the water content. Radial cracks occur in the bony layer of hydrated scale, and the collagen fibers twist around the puncture site. However, the shear failure occurs in the bony layer of dehydrated scale.

关键词： grass carp scale mechanical behavior biaxial test penetration resistance

Analysis of Unsteady Heat Transfer Problems with Complex Geometries Using Isogeometric Boundary Element Method

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Computers, Materials & Continua 2020年第2期62卷 929-962页

作者： Weihua Fang Zhilin An Tiantang Yu tinh quoc bui Nanjing Automation Institute of Water Conservancy and Hydrology Nanjing210012China Department of Engineering Mechanics Hohai UniversityNanjing211100China Institute for Research and Development Duy Tan UniversityDa Nang CityVietnam Department of Civil and Environmental Engineering Tokyo Institute of Technology2-12-1-W8-22OokayamaMeguro-kuTokyo 152-8552Japan

Numerical analysis of unsteady heat transfer problems with complex geometries by the isogeometric boundary element method(IGABEM)is *** IGABEM possesses many desirable merits and features,for instance,(a)exactly represented arbitrarily complex geometries,and higher-order continuity due to non-uniform rational B-splines(NURBS)shape functions;(b)using NURBS for both field approximation and geometric description;(c)directly utilizing geometry data from computer-aided design(CAD);and(d)only boundary *** formulation of IGABEM for unsteady heat transfer is *** domain discretization in terms of IGABEM for unsteady heat transfer is required as that in traditional *** internal values however are obtained with the analytical formula according to the values on the boundaries,and its computations are therefore mainly dependent on the discretization of the *** coordinates of internal control points are obtained with the coordinates of control points on the boundaries using Coons body interpolation *** developed approach is tested with several numerical examples from simple to complicated *** agreement is gained with reference solutions derived from either analytical or finite element methods.

关键词： Unsteady heat transfer BEM isogeometric analysis NURBS

Simulation of cohesive crack growth by a variable-node XFEM

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Frontiers of Structural and Civil Engineering 2020年第1期14卷 215-228页

作者： Weihua FANG Jiangfei WU Tiantang YU Thanh-Tung NGUYEN tinh quoc bui Nanjing Automation Institute of Water Conservancy and Hydrology Nanjing 210012China Department of Engineering Mechanics Hohai UniversityNanjing 211100China Laboratory of Solid Structures University of LuxembourgLuxembourg L-1359Luxembourg Institute for Research and Development Duy Tan UniversityDa Nang City 550000Vietnam Department of Civil and Environmental Engineering Tokyo Institute of TechnologyTokyo 152-8552Japan

A new computational approach that combines the extended finite element method associated with variable-node elements and cohesive zone model is *** using a new enriched technique based on sign function,the proposed model using 4-node quadrilateral elements can eliminate the blending element *** also allows modeling the equal stresses at both sides of the crack in the crack-tip as assumed in the cohesive model,and is able to simulate the arbitrary crack-tip *** multiscale mesh technique associated with variable-node elements and the arc-length method further improve the efficiency of the developed *** performance and accuracy of the present approach are illustrated through numerical experiments considering both mode-I and mixed-mode fracture in concrete.

关键词： extended finite element method cohesive zone model sign function crack propagation

Improved knowledge-based neural network(KBNN)model for predicting spring-back angles in metal sheet bending

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International Journal of Modeling, Simulation, and Scientific Computing 2014年第2期5卷 65-91页

作者： tinh quoc bui Anh Viet Tran Abid Ali Shah Department of Civil Engineering University of Siegen Paul-Bontz-Str.9-1157076 SiegenGermany School of Mechanical and Automotive Engineering University of Ulsan San 29Mugeo 2-dong NamguP.O.Box 18UlsanSouth Korea

We develop an efficiently improved knowledge-based neural network(KBNN)associated with optimization algorithms and finite element analysis(FEA)to accurately predict spring-back angles in metal sheet *** well-known V and U prevalent processes of bending are *** KBNN predictive results are based on the empirical model and artificial neural network(ANN)*** empirical model is constructed from the FEA results using response surface method,while the multilayer perceptron is employed to create the *** trained KBNN can accurately model the relation-ship between the spring-back angles and process *** obtained results are validated against other existing methods showing a high accuracy.

关键词： Metal sheet bending spring-back angles knowledge-based neural network genetic algorithm optimization algorithm

Damage in hybrid corrugated core sandwich structures under high velocity hail ice impact:A numerical study

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Defence Technology(防务技术) 2023年第9期27卷 217-236页

作者： Chao Zhang Xin Fang Jose L.Curiel-Sosa tinh quoc bui Chunjian Mao School of Mechanical Engineering Jiangsu UniversityZhenjiangChina Department of Mechanical Engineering The University of SheffieldSheffieldUK Duy Tan Research Institute for Computational Engineering Duy Tan UniversityHo Chi Minh CityViet Nam State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and AstronauticsNanjingChina

Potential damage in composite structures caused by hail ice impact is an essential safety threat to the aircraft in *** this study,a nonlinear finite element(FE)model is developed to investigate the dynamic response and damage behavior of hybrid corrugated sandwich structures subjected to high velocity hail ice *** impact and breaking behavior of hail are described using the FE-smoothed particle hydrodynamics(FE-SPH)method.A rate-dependent progressive damage model is employed to capture the intra-laminar damage response;cohesive element and surface-based cohesive contact are implemented to predict the inter-laminar delamination and sheet/core debonding phenomena *** transient processes of sandwich structure under different hail ice impact conditions are *** analysis is conducted to address the influences of core shape and impact position on the impact performance of sandwich structures and the corresponding energy absorption characteristics are also revealed.

关键词： Sandwich structure Hail ice impact Damage behavior Energy absorption FE-SPH modeling

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

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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

Deep learning long short-term memory combined with discrete element method for porosity prediction in gravel-bed rivers

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International Journal of Sediment Research 2023年第1期38卷 128-140页

作者： Duong Tran Anh Ahad Hasan Tanim Daniel Prakash Kushwaha quoc Bao Pham Van Hieu bui HUTECH University Center for River Harbor and Landslide Research Chittagong University of Engineering & Technology Govind Ballabh Pant University of Agriculture and Technology Institute of Applied Technology Thu Dau Mot University Faculty of Mechanical Engineering Thuyloi University

The porosity of gravel riverbed material often is an essential parameter to estimate the sediment transport rate, groundwater-river flow interaction, river ecosystem, and fluvial geomorphology. Current methods of porosity estimation are time-consuming in simulation. To evaluate the relation between porosity and grain size distribution(GSD), this study proposed a hybrid model of deep learning Long Short-Term Memory(LSTM) combined with the Discrete Element Method(DEM). The DEM is applied to model the packing pattern of gravel-bed structure and fine sediment infiltration processes in threedimensional(3D) space. The combined approaches for porosity calculation enable the porosity to be determined through real time images, fast labeling to be applied, and validation to be done. DEM outputs based on the porosity dataset were utilized to develop the deep learning LSTM model for predicting bed porosity based on the GSD. The simulation results validated with the experimental data then segregated into 800 cross sections along the vertical direction of gravel pack. Two DEM packing cases, i.e., clogging and penetration are tested to predict the porosity. The LSTM model performance measures for porosity estimation along the z-direction are the coefficient of determination(R~2), root mean squared error(RMSE), and mean absolute error(MAE) with values of 0.99, 0.01, and 0.01 respectively, which is better than the values obtained for the Clogging case which are 0.71, 0.14, and 0.03, respectively. The use of the LSTM in combination with the DEM model yields satisfactory results in a less complex gravel pack DEM setup, suggesting that it could be a viable alternative to minimize the simulation time and provide a robust tool for gravel riverbed porosity prediction. The simulated results showed that the hybrid model of the LSTM combined with the DEM is reliable and accurate in porosity prediction in gravel-bed river test samples.

关键词： Numerical modeling Discrete element method LSTM Bed porosity Gravel-bed river

Accuracy assessment of mine walls＇ surface models derived from terrestrial laser scanning

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International Journal of Coal Science & Technology 2018年第3期5卷 328-338页

作者： Nguyen quoc Long Michal M. Buczek La Phu Hien Sylwia A. Szlapinska bui Xuan Nam Nguyen Viet Nghia Cao Xuan Cuong Department of Mine Surveying Hanoi University of Mining and Geology Hanoi Vietnam Department of Mine Surveying AGH University of Science and Technology Kxakow Poland Department of Surveying and Spatial Data Analysis Hanoi University of Mining and Geology Hanoi Vietnam Department of Surface Mining Hanoi University of Mining and Geology Hanoi Vietnam

The monitoring of highwall slopes at open-pit mines is an important task to ensure safe mining. For this reason, several techniques such as total station, radar, terrestrial Light Detection and Ranging （LIDAR） can be employed for surface measurement. The objective of this study is to investigate mesh algorithms, which can be used to interpolate 3D models of pit walls. Experiments were carried out at Coc Sau open-pit mine at Quang Ninh province of Vietnam, and at experimental mine of Akademia Gtrniczo-Hutnicza University of Science and Technology in Cracow, Poland. First, 3D point cloud data for the study area was acquired by using terrestrial LIDAR, then was used to generate mesh surfaces using three algorithms--Delaunay 2.5D XY Plane, Delaunay 2.5D Best Fitting Plane, and Mesh from Points. After that, the results were rectified and optimized. Subsequently, the optimized meshes were used for generation of non-uniform rational basis spline （NURBS） surfaces. Then, the NURBS surface accuracy was assessed. The results showed that the average distance between surface and point cloud was within range of 5.6-5.8 mm with deviation of 6.2-6.8 mm, depending on the used mesh. Additionally, the quality of surfaces depends on the quality of input data set and the algorithm used to generate mesh network, and the accuracy of computed NURBS surfaces fitting into pointset was 4-5 times lower than that of optimized mesh fitting. However, the accuracy of the final product allows determining displacements on the level of centimeters.

关键词： Polygon mesh network NURBS surface LIDAR - Open-pit mine Highwall slope

Hybrid model to optimize object-based land cover classification by meta-heuristic algorithm:an example for supporting urban management in Ha Noi,Viet Nam

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International Journal of Digital Earth 2019年第10期12卷 1118-1132页

作者： Quang-Thanh bui Manh Pham Van Nguyen Thi Thuy Hang quoc-Huy Nguyen Nguyen Xuan Linh Pham Minh Hai Tran Anh Tuan Pham Van Cu Center for Applied Research in Remote sensing and GIS(CARGIS) VNU University of ScienceHa NoiViet Nam VNU University of Science Ha NoiViet Nam Vietnam Institute of Geodesy and Cartography Ha NoiViet Nam Ministry of Education and Training Ha NoiViet Nam

This study proposed a novel object-based hybrid classification model named GMNN that combines Grasshopper Optimization Algorithm(GOA)and the multiple-class Neural network(MNN)for urban pattern detection in Hanoi,*** bands of SPOT 7 image and derivable NDVI,NDWI were used to generate image segments with associated attributes by PCI Geomatics *** segments were classified into four urban surface types(namely water,impervious surface,vegetation and bare soil)by the proposed ***,three training and validation datasets of different sizes were used to verify the robustness of this *** all tests,the overall accuracies of the classification were approximately 87%,and the Area under Receiver Operating Characteristic curves for each land cover type was ***,the performance of this model was examined by comparing several statistical indicators with common benchmark *** results showed that GMNN out-performed established methods in all comparable *** results suggested that our hybrid model was successfully deployed in the study area and could be used as an alternative classification method for urban land cover *** a broader sense,classification methods will be enriched with the active and fast-growing contribution of metaheuristic algorithms.

关键词： Urban remote sensing object-based classification neural network grasshopper optimization algorithm