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Plastic and Aesthetic Research 2014年第1期1卷 85-88页

作者： Trung-Hau LêThua duc-phu bui Dang-Nhat Pham Vu-Bao Lê Albert De Mey Willy Boeckx Department of Plastic Reconstructive and Hand SurgeryHue Central HospitalHue City 47000Thừa Thiên-HuếVietnam Departments of Surgery Hue University of Medicine and PharmacyHue City 47000Thừa Thiên-HuếVietnam Department of Surgery An Sinh HospitalHo Chi Minh City 70000Vietnam Department of Plastic Surgery Brugmann University HospitalFree University of BrusselsBrussels 1020Belgium

Aim:The anatomical study and clinical application for the vascularized corticoperiosteal fl ap from the medial femoral condyle have been performed and described *** prior studies have described the composite osteomyocutaneous fl ap from the medial femoral condyle,a detailed analysis of the vascularity of this region has not yet been fully ***:This anatomical study described the variability of the arteries from the medial femoral condyle in 40 cadaveric ***:The descending genicular artery(DGA)was found in 33 of 40 cases(82.5%).The superomedial genicular artery(SGA)was present in 10 cases(25%).All 33 cases(100%)of the DGA had articular branches to the periosteum of the medial femoral *** branches and saphenous branches of the DGA were present in 25 cases(62.5%)and 26 cases(70.3%),***:The current study demonstrates that the size and length of the vessels to the medial femoral condyle are suffi cient for a vascularized bone fl ap.A careful preoperative vascular assessment is essential prior to use of the vascularized composite osteomyocutaneous fl ap from the medial femoral condyle,because of the considerable anatomical variations in different branches of the DGA.

关键词： Descending genicular artery medial femoral condyle osteomyocutaneousﬂap superomedial genicular artery

Prediction of Shear Bond Strength of Asphalt Concrete Pavement Using Machine Learning Models and Grid Search Optimization Technique

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 691-712页

作者： Quynh-Anh Thi bui Dam duc Nguyen Hiep Van Le Indra Prakash Binh Thai Pham Civil Engineering Department University of Transport Technology54 Trieu KhucThanh XuanHanoi100000Vietnam DDG(R)Geological Survey of India Gandhinagar382010India

Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement *** study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input ***,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the *** models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat *** validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the *** show that these models accurately predict SBS,with LGBM providing outstanding ***(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on ***,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.

关键词： Shear bond asphalt pavement grid search optimization machine learning

Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 467-500页

作者： Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models duc-Dam Nguyen Nguyen Viet Tiep Quynh-Anh Thi bui Hiep Van Le Indra Prakash Romulus Costache Manish Pandey Binh Thai Pham Faculty of Civil Engineering University of Transport TechnologyThanh XuanHa Noi100000Vietnam Faculty of Hydraulic Engineering Hanoi University of Civil EngineeringHa Noi100000Vietnam DDG(R)Geological Survey of India Gandhinagar382010India Research Institute of the University of Bucharest Bucharest050663Romania National Institute of Hydrology and Water Management Bucharest013686Romania Department of Civil Engineering Transilvania University of BrasovBrasov500152Romania Danube Delta National Institute for Research and Development Tulcea820112Romania Marwadi University Research Center(MURC) Marwadi UniversityRajkotGujaratBharat360003India Department of Civil Engineering Faculty of Engineering&TechnologyMarwadi UniversityRajkotGujaratBharat360003India

This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and ***,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and *** models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to *** factors were instrumental in predicting the output variable,which was the probability of landslide *** analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other *** AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of *** results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.

关键词： Landslide susceptibility map spatial analysis ensemble modelling information values(IV)

A hierarchical system to predict behavior of soil and cantilever sheet wall by data-driven models

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Frontiers of Structural and Civil Engineering 2022年第6期16卷 667-684页

作者： Nang duc bui Hieu Chi PHAN Tiep duc PHAM Ashutosh Sutra DHAR Institute of Techniques for Special Engineering Le Quy Don Technical UniversityHanoi 100000Vietnam Faculty of Engineering and Applied Science Memorial University of NewfoundlandSt.John’sNL A1B 3X5Canada

The study proposes a framework combining machine learning(ML)models into a logical hierarchical system which evaluates the stability of the sheet wall before other *** study uses the hardening soil(HS)model to develop a 200-sample finite element analysis(FEA)database,to develop the ML ***,a system containing three trained ML models is proposed to first predict the stability status(random forest classification,RFC)followed by 1)the cantilever top horizontal displacement of sheet wall(artificial neural network regression models,RANN1)and 2)vertical settlement of soil(RANN2).The uncertainty of this data-driven system is partially investigated by developing 1000 RFC models,based on the application of random sampling technique in the data splitting *** on the distribution of the evaluation metrics reveals negative skewed data toward the 1.0000 *** implies a high performance of RFC on the database with medians of accuracy,precision,and recall,on test set are 1.0000,1.0000,and 0.92857,*** regression ANN models have coefficient of determinations on test set,as high as 0.9521 for RANN1,and 0.9988 for RANN2,*** parametric study for these regressions is also provided to evaluate the relative insight influence of inputs to output.

关键词： finite element analysis cantilever sheet wall machine learning artificial neural network random forest

A comparative study of three different learning algorithms applied to ANFIS for predicting daily suspended sediment concentration

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International Journal of Sediment Research 2017年第3期32卷 340-350页

作者： Keivan Kaveh Minh duc bui Peter Rutschmann Department of hydraulic and water resource engineering/School of civil engineering Technische Universitt Mnchen (TUM)

The modeling and prediction of suspended sediment in a river are key elements in global water recourses and environment policy and management. In the present study, an Adaptive Neuro-Fuzzy Inference System model trained with the Levenberg-Marquardt learning algorithm is considered for time series modeling of suspended sediment concentration in a river. The model is trained and validated using daily river discharge and suspended sediment concentration data from the Schuylkill River in the United States. The results of the proposed method are evaluated and compared with similar networks trained with the common Hybrid and Back-Propagation algorithms, which are widely used in the literature for prediction of suspended sediment concentration. Obtained results demonstrate that models trained with the Hybrid and Levenberg-Marquardt algorithms are comparable in terms of prediction ***, the networks trained with the Levenberg-Marquardt algorithm perform better than those trained with the Hybrid approach.

关键词： Fuzzy Inference System Hybrid learning rule Levenberg-Marquardt algorithm Schuylkill river Suspended sediments

Adaptive Controller Design for Dynamic Maneuvers of High Speed Underwater Vehicles

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China Ocean Engineering 2022年第2期36卷 311-321页

作者： bui duc Hong phuC Sam-Sang YOU phuc Thinh DOAN Sang-Do LEE Department of Mechanical Engineering The University of TulsaTulsaOK 74104USA Division of Mechanical Engineering Korea Maritime and Ocean UniversityBusan 49112Republic of Korea Institute of Interdisciplinary Social Sciences Nguyen Tat Thanh UniversityHo Chi Minh CityVietnam Division of Navigation and Information System Mokpo National Maritime UniversityMokpoJeollanam-do 58628Republic of Korea

The control synthesis of the high-speed underwater vehicle faces many technical challenges due to its inherent structure and surrounding operational *** this paper,the dynamical behavior is firstly described through a bifurcation analysis to give some insights for robust control *** a novel adaptive fractional-order sliding mode controller(AFOSMC)is realized to effectively manipulate the supercavitating vehicle against payload changes,nonlinear planing force,and external *** fractional order(FO)calculus can offer more flexibility and more freedom for tuning active control synthesis than the integer-order *** addition,the adaptation law has been presented to directly handle the payload change *** stability of the controlled vehicle system is proven via Lyapunov stability ***,the dynamic performance of the proposed controller is verified through extensive simulation results,which demonstrate the control accuracy with faster responses compared with existing integer-order ***,the proposed fractional order controllers can provide higher performance than their integer order counterparts with control algorithms.

关键词： fractional order calculus supercavitating underwater vehicles planing force payload change adaptive sliding mode control

基于离心模型试验的高强度桩复合地基桩帽效应分析

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岩土工程学报 2008年第8期30卷 1158-1164页

作者： bui phu Doanh 罗强张良西南交通大学土木工程学院四川成都610031

桩帽的设置及其覆盖率的变化对高强度桩复合地基的工程特性影响巨大,对桩帽尺寸的合理设计是实现高强度桩–桩间土–加筋垫层协调承载和变形控制的技术关键。结合某铁路新建双线段(v≥200 km/h)现场软基处理工点的具体情况,进行了4组设... 详细信息

桩帽的设置及其覆盖率的变化对高强度桩复合地基的工程特性影响巨大,对桩帽尺寸的合理设计是实现高强度桩–桩间土–加筋垫层协调承载和变形控制的技术关键。结合某铁路新建双线段(v≥200 km/h)现场软基处理工点的具体情况,进行了4组设置桩帽的高强度桩复合地基离心模型试验,测试了地基变形、桩身应变、桩帽顶面及桩间土压力、垫层拉筋受力等数据。试验数据表明:①在桩顶设置桩帽能有效发挥高强度桩的承载能力,调整桩和桩间土的荷载分配,提高地基的稳定性;②在桩间距不变的条件下,地基变形、垫层拉筋受力、桩帽顶面及桩间土压力等随桩帽覆盖率的提高而减小,桩土应力比则增加;③垫层拉筋受力沿路基横截面基本呈M型对称分布,峰值出现在左右路肩附近位置,与地基的剪切破坏位置有较好的一致性。

关键词：离心模型试验软土地基路堤高强度桩复合地基桩帽效应

Occurrence of microcystins in water,sediment,and aquatic animals in Dau Tieng Reservoir,Vietnam

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Journal of Oceanology and Limnology 2024年第6期42卷 1751-1763页

作者： Thanh Luu PHAM Manh Ha bui Tan duc NGUYEN Thanh Son DAO Faculty of Environment and Labour Safety Ton Duc Thang UniversityHo Chi Minh City 700000Vietnam Faculty of Environment Saigon UniversityHo Chi Minh City 700000Vietnam Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention and Guangdong Provincial Key Laboratory of Marine BiotechnologyShantou UniversityShantou 515063China Faculty of Environment and Natural Resources Ho Chi Minh City University of Technology(HCMUT)Ho Chi Minh City 700000Vietnam Asian Center for Water Research(Centre Asiatique de Recherchesur l’Eau CARE)Ho Chi Minh City University of Technology(HCMUT)Vietnam National UniversityHo Chi Minh City 700000Vietnam

This study investigates the variations of microcystins(MCs)in water,cyanobacterial blooms,sediment,and aquatic organisms collected from the Dau Tieng Reservoir(DTR).*** liquid chromatography(HPLC)was employed to measure MC concentrations in various target *** indicate that Microcystis *** as the primary MC producer in the *** average concentrations of analyzed MCs in surface water ranged from 1.10 to 5.54μg/L,temporally and *** sediment,average concentrations varied from 0.15 to 1.13μg/g wet weight(WW)temporally and from 0.41 to 0.72μg/g WW *** were detected in different organs of fish species(Oreochromis *** Labiobarbus sp.)and in the entire soft tissues of bivalve(Corbicula sp.)and gastropod(Assiminea sp.).The highest observed MC concentration in July was 0.83±0.22μg/g WW in the intestines of fish Oreochromis *** presence of MCs in grass shrimp Palaemonetes *** observed solely in June,reaching a concentration of 0.28±0.19μg/g *** is the first report of MC accumulation in the grass shrimp Palaemonetes *** field *** the bivalve Corbicula sp.,the presence of analyzed MCs was consistent throughout the study period,except for March and September,with the highest concentrations in July at 0.77±0.1μg/g *** correlation analysis revealed significant positive correlations between MCs in water and sediment with MC concentrations in aquatic animals,indicating the potential transfer of MCs across different trophic *** estimated daily intake values for analyzed MCs indicate that fish collected from the DTR are considered safe for consumption,as long as only the edible organs,such as the muscle,are ***,bivalves or gastropods collected from the DTR are not safe for human *** study underscored the importance of monitoring MC accumulation in aquatic animals used as food to mitigate adverse effects on human health.

关键词： aquatic animal bioaccumulation cyanotoxin health risk toxic cyanobacteria

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

基于离心模型试验的高强度桩复合地基桩间距效应分析

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铁道学报 2009年第6期31卷 69-75页

作者： bui phu DOANH 罗强张良苏兴西南交通大学土木工程学院四川成都610031 中铁八局集团达成铁路新建双线段指挥部四川大英629300 越南国家建设大学道路与桥梁工程学院越南河内

针对客运专线在软土和松软土地基处理中大规模采用高强度桩复合地基技术的应用情况,进行路堤荷载作用下不同桩间距的离心模型试验,分析桩间距的变化对高强度桩复合地基的荷载传递、破坏特点、桩土应力及垫层拉筋受力、地基沉降变形等工... 详细信息

针对客运专线在软土和松软土地基处理中大规模采用高强度桩复合地基技术的应用情况,进行路堤荷载作用下不同桩间距的离心模型试验,分析桩间距的变化对高强度桩复合地基的荷载传递、破坏特点、桩土应力及垫层拉筋受力、地基沉降变形等工程特性的影响。试验数据表明:桩间距由3倍增至6倍桩径,高强度桩复合地基的沉降变形、桩土应力及比值、垫层拉筋受力等力学响应增大明显;随着桩间距的加大,高强度桩复合地基的桩顶和桩间土承受的应力均大幅提高,桩间距大于或等于5倍桩径后,桩顶垫层和桩间土先后达到极限状态,将产生显著的桩顶刺入变形和桩间土横向挤出变形,复合地基整体结构处于不稳定状态;垫层拉筋的受力沿横截面呈M形分布,峰值出现在两侧路肩附近位置的下方,与地基发生变形破坏的位置有较好的一致性。

关键词：路堤荷载高强度桩复合地基离心模型试验桩间距效应