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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 hybrid machine learning model to estimate self-compacting concrete compressive strength

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Frontiers of Structural and Civil Engineering 2022年第8期16卷 990-1002页

作者： hai-Bang LY Thuy-Anh nguyen binh Thai PHAM May Huu nguyen Civil Engineering Department University of Transport TechnologyHanoi 100000Vietnam Civil and Environmental Engineering Program Graduate School of Advanced Science and EngineeringHiroshima UniversityHiroshima 739-8527Japan

This study examined the feasibility of using the grey wolf optimizer(GWO)and artificial neural network(ANN)to predict the compressive strength(CS)of self-compacting concrete(SCC).The ANN-GWO model was created using 115 samples from different sources,taking into account nine key SCC *** validation of the proposed model was evaluated via six indices,including correlation coefficient(R),mean squared error,mean absolute error(MAE),IA,Slope,and mean absolute percentage *** addition,the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence *** results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s *** that,an examination of the parameters impacting the CS of SCC was provided.

关键词： artificial neural network grey wolf optimize algorithm compressive strength self-compacting concrete

Building 3-D Human Data Based on Handed Measurement and CNN

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Computers, Materials & Continua 2023年第2期74卷 2431-2441页

作者： Bich nguyen binh nguyen hai Tran Vuong Pham Le Nhi Lam Thuy Pham The Bao Faculty of Information Science Sai Gon UniversityHo Chi Minh70000Vietnam Faculty of Information Technology University of EducationHo Chi Minh70000Vietnam

3-dimension(3-D)printing technology is growing strongly with many applications,one of which is the garment *** application of human body models to the garment industry is necessary to respond to the increasing personalization demand and still guarantee *** paper proposes amethod to construct 3-D human models by applying deep *** calculate the location of the main slices of the human body,including the neck,chest,belly,buttocks,and the rings of the extremities,using pre-existing ***,on the positioning frame,we find the key points(fixed and unaltered)of these key slices and update these points tomatch the current *** add points to a star slice,we use a deep learning model tomimic the form of the human body at that slice *** use interpolation to produce sub-slices of different body sections based on the main slices to create complete body parts *** combine all slices to construct a full 3-D representation of the human body.

关键词： 3-D human model deep learning interpolation

Multilevel Threshold Based Image Denoising in Curvelet Domain

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Journal of Computer Science & Technology 2010年第3期25卷 632-640页

作者： nguyen Thanh binh Ashish Khare Department of Electronics and Communication University of Allahabad

In this paper, we propose a multilevel thresholding technique for noise removal in curvelet transform domain which uses cycle-spinning. Most of uncorrelated noise gets removed by thresholding curvelet coefficients at lowest level, while correlated noise gets removed by only a fraction at lower levels, so we used multilevel thresholding on curvelet coefficients. The threshold in the proposed method depends on the variance of curvelet coefficients, the mean and the median of absolute curvelet coefficients at a particular level which makes it adaptive in nature. Results obtained for 2-D images demonstrate an improved performance over other recent related methods available in literature.

关键词： curvelet transform denoising multilevel thresholding cycle-spinning

A comparative analysis of weight-based machine learning methods for landslide susceptibility mapping in Ha Giang area

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Big Earth Data 2023年第4期7卷 1005-1034页

作者： Thanh Trinh binh Thanh Luu Trang Ha Thi Le Duong Huy nguyen Trong Van Tran Thi hai Van nguyen Khanh Quoc nguyen Lien Thi nguyen Faculty of Computer Science Phenikaa UniversityYen NghiaHa DongHanoi 12116Vietnam Phenikaa Research and Technology Institute(PRATI) A&A Green Phoenix Group JSCNo.167 Hoang NganTrung HoaCau GiayHanoi 11313Vietnam Northwestern Geological Division General Department of Geology and Minerals of VietnamHanoiVietnam Youth union office Thuyloi University175 Tay SonDong DaHanoiVietnam Center of Remote Sensing and Geohazards Vietnam Institute of Geosciences and Mineral ResourcesHanoiVietnam

Landslide susceptibility maps (LSMs) are very crucial for planningpolicies in hazardous areas. However, the accuracy and reliability ofLSMs depend on available data and the selection of suitable *** study is conducted to produce LSMs by combinations of machinelearning methods and weighting techniques for Ha Giang province,Vietnam, where has limited data. In study area, we gather 11 landslideconditioning factors and establish a landslide inventory *** the weights of classes (or factors) is very important toprepare data for machine learning methods to generate LSMs. Wefrst use frequency ratio (FR) and analytic hierarchy process (AHP)techniques to generate the weights. Then, random forest (RF), supportvector machine (SVM), logistic regression (LR), and AHP methods arecombined with FR and AHP weights to yield accurate and reliableLSMs. Finally, the performance of these methods is evaluated by fivestatistical metrics, ROC and R-index. The empirical results have shownthat RF is the best method in terms of R-index and the five metrics, *** rate (0.9661), FP rate (0.0), ACC (0.9835), MAE (0.0046), and RMSE(0.0350) for this study area. This study opens the perspective of weightbasedmachine learning methods for landslide susceptibility mapping.

关键词： Landslide logistic regression random forest SvM Vietnam

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

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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

Transient responses of double-curved sandwich two-layer shells resting on Kerr's foundations with laminated three-phase polymer/GNP/fiber surface and auxetic honeycomb core subjected to the blast load

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Defence Technology（防务技术） 2024年第5期35卷 222-247页

作者： nguyen Thi hai Van Thi Hong nguyen Researcher&Lecturer Faculty of Industrial EducationThe University of Danang-University of Technology and Education48 Cao ThangDanang550000Viet Nam Researcher&Lecturer Faculty of Mechanical EngineeringThuyloi University175 Tay SonDong DaHanoi100000Viet Nam

This work uses refined first-order shear theory to analyze the free vibration and transient responses of double-curved sandwich two-layer shells made of auxetic honeycomb core and laminated three-phase polymer/GNP/fiber surface subjected to the blast *** of the two layers that make up the double-curved shell structure is made up of an auxetic honeycomb core and two laminated sheets of three-phase polymer/GNP/*** exterior is supported by a Kerr elastic foundation with three *** key innovation of the proposed theory is that the transverse shear stresses are zero at two free surfaces of each *** contrast to previous first-order shear deformation theories,no shear correction factor is ***'s exact solution was used to treat the double-curved shell problem with a single title boundary,while the finite element technique and an eight-node quadrilateral were used to address the other boundary *** ensure the accuracy of these results,a thorough comparison technique is employed in conjunction with credible *** problem model's edge cases allow for this kind of *** study's findings may be used in the post-construction evaluation of military and civil works structures for their ability to sustain explosive *** addition,this is also an important basis for the calculation and design of shell structures made of smart materials when subjected to shock waves or explosive loads.

关键词： Blast load Two-layer shell Polymer/GNP/Fiber surface Auxetic honeycomb Shear connectors

Locally weighted learning based hybrid intelligence models for groundwater potential mapping and modeling: A case study at Gia Lai province, Vietnam

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Geoscience Frontiers 2021年第5期12卷 54-68页

作者： Hoang Phan hai Yen binh Thai Pham Tran Van Phong Duong hai Ha Romulus Costache Hiep Van Le Huu Duy nguyen Mahdis Amiri nguyen Van Tao Indra Prakash Department of Geography School of Social Sciences EducationVinh University182 Le DuanVinhNghe AnVietnam University of Transport Technology Hanoi 100000Viet Nam Institute of Geological Sciences Vietnam Academy of Science and Technology(VAST)84 Chua LangDong DaHanoiViet Nam Institute for Water and Environment Hanoi 100000Viet Nam Research Institute of the University of Bucharest 90-92 Sos.Panduri5th DistrictBucharestRomania National Institute of Hydrology and Water Management București-Ploiești Road97E1st District013686 BucharestRomania Faculty of Geography VNU University of ScienceVietnam National University334 Nguyen TraiHanoi 100000Viet Nam Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan ***Iran DDG(R)Geological Survey of India Gandhinagar 382010India

The groundwater potential map is an important tool for a sustainable water management and land use planning,particularly for agricultural countries like *** this article,we proposed new machine learning ensemble techniques namely AdaBoost ensemble(ABLWL),Bagging ensemble(BLWL),Multi Boost ensemble(MBLWL),Rotation Forest ensemble(RFLWL)with Locally Weighted Learning(LWL)algorithm as a base classifier to build the groundwater potential map of Gia Lai province in *** this study,eleven conditioning factors(aspect,altitude,curvature,slope,Stream Transport Index(STI),Topographic Wetness Index(TWI),soil,geology,river density,rainfall,land-use)and 134 wells yield data was used to create training(70%)and testing(30%)datasets for the development and validation of the *** statistical indices were used namely Positive Predictive Value(PPV),Negative Predictive Value(NPV),Sensitivity(SST),Specificity(SPF),Accuracy(ACC),Kappa,and Receiver Operating Characteristics(ROC)curve to validate and compare performance of *** show that performance of all the models is good to very good(AUC:0.75 to 0.829)but the ABLWL model with AUC=0.89 is the *** the models applied in this study can support decision-makers to streamline the management of the groundwater and to develop economy not only of specific territories but also in other regions across the world with minor changes of the input parameters.

关键词： Locally weighted learning Hybrid models Groundwater potential GIS Vietnam