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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)

Shear strength model of the reinforced concrete beams with embedded through-section strengthening bars

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Frontiers of Structural and Civil Engineering 2022年第7期16卷 843-857页

作者： Linh van Hong bui Phuoc Trong NGUYEN Faculty of Civil Engineering Ho Chi Minh City Open UniversityHo Chi Minh City 700000Vietnam

In this study,finite element(FE)analysis is utilized to investigate the shear capacity of reinforced concrete(RC)beams strengthened with embedded through-section(ETS)*** of critical variables on the beam shear strength,including the compressive strength of concrete,stiffness ratio between ETS bars and steel stirrups,and use of ETS strengthening system alone,are parametrically investigated.A promising method based on the bond mechanism between ETS strengthening and concrete is then proposed for predicting the shear resistance forces of the strengthened *** expression for the maximum bond stress of the ETS bars to concrete is *** new expression eliminates the difficulty in the search and selection of appropriate bond parameters from adhesion *** results obtained from the FE models and analytical models are validated by comparison with those measured from the ***,the model proposed in this study demonstrates better performance and more accuracy for prediction of the beam shear-carrying capacity than those of existing *** results obtained from this study can also serve researchers and engineers in selection of the proper shear strength models for design of ETS-strengthened RC beams.

关键词： embedded through-section strengthening fiber-reinforced polymer finite element shear strength model bond mechanism

A New Framework for Software Vulnerability Detection Based on an Advanced Computing

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Computers, Materials & Continua 2024年第6期79卷 3699-3723页

作者： bui van Cong Cho Do Xuan Department of Information Technology University of Economics and Technical IndustriesHanoi100000Vietnam Faculty of Information Security Posts and Telecommunications Institute of TechnologyHanoi100000Vietnam

The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest *** paper proposes a new framework called DrCSE to improve software vulnerability *** uses an intelligent computation technique based on the combination of two methods:Rebalancing data and representation learning to analyze and evaluate the code property graph(CPG)of the source code for detecting abnormal behavior of software *** do that,DrCSE performs a combination of 3 main processing techniques:(i)building the source code feature profiles,(ii)rebalancing data,and(iii)contrastive *** which,the method(i)extracts the source code’s features based on the vertices and edges of the *** method of rebalancing data has the function of supporting the training process by balancing the experimental ***,contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers *** experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabilities using the Verum *** a result,the method proposed in the article has brought a pretty good performance in all metrics,especially the Precision and Recall scores of 39.35%and 69.07%,respectively,proving the efficiency of the DrCSE *** performs better than other approaches,with a 5%boost in Precision and a 5%boost in ***,this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date.

关键词： Source code vulnerability source code vulnerability detection code property graph feature profile contrastive learning data rebalancing

Dam deformation analysis based on BPNN merging models

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Geo-Spatial Information Science 2018年第2期21卷 149-157页

作者： Jingui Zou Kien-Trinh Thi bui Yangxuan Xiao Chinh van Doan School of Geodesy and Geomatics Wuhan UniversityWuhanChina Geomatics Centre Thuyloi UniversityHanoiVietnam Institute of Technique Special Engineering Le Quy Don Technical UniversityHanoiVietnam

Hydropower has made a significant contribution to the economic development of Vietnam,thus it is important to monitor the safety of hydropower dams for the good of the country and the *** this paper,dam horizontal displacement is analyzed and then forecasted using three methods:the multi-regression model,the seasonal integrated auto-regressive moving average(SARIMA)model and the back-propagation neural network(BPNN)merging *** monitoring data of the Hoa Binh Dam in Vietnam,including horizontal displacement,time,reservoir water level,and air temperature,are used for the *** results indicate that all of these three methods can approximately describe the trend of dam deformation despite their different forecast ***,their short-term forecasts can provide valuable references for the dam safety.

关键词： Dam deformation analysis multi-regression model Back-propagation Neural Network(BPNN) Seasonal Integrated Auto-regressive Moving Average(SARIMA)model merging model

Advances in scalable gas-phase manufacturing and processing of nanostructured solids： A review

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Particuology 2017年第1期15卷 15-39页

作者： Samir Salameh Jesus Gomez-Hernandez Aristeidis Goulas Hao van bui J. Ruud van Ommen Delft University ofTechnology Julianalaan 136 2628 BL Delft The Netherlands Carlos III University of Madrid Avda. de la Universidad 30 28911 Leganes Madrid Spain Delft IMP B.V. Mekelweg 2 2628 CD Delft The Netherlands

Although the gas-phase production of nanostructured solids has already been carried out in industry for decades, only in recentyears has research interest in this topic begun to increase. Nevertheless, despite the remarkable scientific progress made recently, many long-established processes are still used in industry. Scientific advancements can potentially lead to the improvement of existing industrial processes, but also to the development of completely new routes. This paper aims to review state-of-the-art synthesis and processing technologies, as well as the recent developments in academic research. Flame reactors that produce inorganic nanoparticles on industrial- and lab-scales are described, alongside a detailed overview of the different systems used for the production of carbon nanotubes and graphene. We discuss the problems of agglomeration and mixing of nanoparticles, which are strongly related to synthesis and processing. Finally, we focus on two promising processing techniques, namely nanoparticle fluidization and atomic layer deposition.

关键词： Nanoparticle synthesis Gas-phase process Nanoparticle fluidization Atomic layer deposition Agglomeration

Static analysis of corrugated panels using homogenization models and a cell-based smoothed mindlin plate element (CS-MIN3)

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Frontiers of Structural and Civil Engineering 2019年第2期13卷 251-272页

作者： Nhan NGUYEN-MINH Nha TRAN-van Thang bui-XUAN Trung NGUYEN-THOI Faculty of Applied Science Bach Khoa University (BKU)Ho Chi Minh CityVietnam Faculty of Mathematics and Computer Science Ho Chi Minh City University of Science (HCMUS)Ho Chi Minh CityVietnam Department of Mathematics L ouisiana State UniversityBaton RougeLA 70803USA Division of Computational Mathematics and Engineering Institute of Computational ScienceTon Due Thang UniversityHo Chi Minh CityVietnam Faculty of Civil Engineering Ton Due Thang UniversityHo Chi Minh CityVietnam

Homogenization is a promising approach to capture the behavior of complex structures like corrugated panels. It enables us to replace high-cost shell models with stiffness-equivalent orthotropic plate alternatives. Many homogenization models for corrugated panels of different shapes have been proposed. However, there is a lack of investigations for verifying their accuracy and reliability. In addition, in the recent trend of development of smoothed finite element methods, the cell-based smoothed three-node Mindlin plate element (CS-MIN3) based on the first-order shear deformation theory (FSDT) has been proposed and successfully applied to many analyses of plate and shell structures. Thus, this paper further extends the CS-MIN3 by integrating itself with homogenization models to give homogenization methods. In these methods, the equivalent extensional, bending, and transverse shear stiftriess components which constitute the equivalent orthotropic plate models are represented in explicit analytical expressions. Using the results of ANSYS and ABAQUS shell simulations as references, some numerical examples are conducted to verify the accuracy and reliability of the homogenization methods for static analyses of trapezoidally and sinusoidally corrugated panels.

关键词： homogenization corrugated panel asymptotic analysis smoothed finite element method (S-FEM) cell-based smoothed three-node Mindlin plate element (CS-MIN3)

Velocity equation for grenades while impacting on dry sand media

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Defence Technology（防务技术） 2024年第5期35卷 168-173页

作者： Martin Macko Xuan Son bui Kongsathit Phanthavong Duc Hung Pham van Gion Do van Minh Do Jiri Skala University of Defence BrnoCzech Republic Le Quy Don Technical University Ha NoiViet Nam

This paper deals with the collision of sphere shape grenades with sand *** central issue of the article is the establishing of an empirical velocity equation of the grenade while impacting on sand that is used to solve motion equations of the mechanical mechanism inside the impact grenade *** paper focuses on impact velocities that are lower than 5 m s^(-1).An experiment was conducted to study the velocity of the grenade while impacting on dry sand.A high-speed camera video was used to capture the grenade *** grenade velocity in the impact process was generated from these video *** types of fitting curves are used to regress the velocity equation of the grenade while interacting with the sand media and the best-fitting model is *** result shows the regression curve has a high correlation with the experiment data for grenade velocities below 5 m s^(-1).The received regression equation is useful for analyzing the working ability of the inertial mechanism inside the impact grenade or analyzing and choosing the appropriate parameters of each part in the inertial mechanism to meet the required characteristics of the mechanism.

关键词： Dynamic response Impact velocity Impact grenade Low-velocity impact

Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based optimization algorithms

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Journal of Rock Mechanics and Geotechnical Engineering 2021年第6期13卷 1452-1465页

作者： Chengyu Xie Hoang Nguyen Xuan-Nam bui van-Thieu Nguyen Jian Zhou School of Environment and Resources Xiangtan UniversityXiangtan411105China Mining Faculty Hanoi University of Mining and GeologyHanoi100000Viet Nam Innovations for Sustainable and Responsible Mining(ISRM)Group Hanoi University of Mining and GeologyHanoi100000Viet Nam Artificial Intelligence Independent Research Group Hanoi100000Viet Nam School of Resources and Safety Engineering Central South UniversityChangsha410083China

Due to the rapid industrialization and the development of the economy in each country,the demand for energy is increasing *** coal mines have to pace up the mining operations with large production to meet the energy *** requirement has led underground coal mines to go deeper with more difficult conditions,especially the mining hazards,such as large deformations,rockburst,coal burst,roof collapse,to name a ***,this study aims at investigating and predicting the stability of the roadways in underground coal mines exploited by longwall mining method,using various novel intelligent techniques based on physics-based optimization algorithms(***-verse optimizer(MVO),equilibrium optimizer(EO),simulated annealing(SA),and Henry gas solubility optimization(HGSO)) and adaptive neuro-fuzzy inference system(ANFIS),named as MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSOANFIS ***,162 roof displacement events were investigated based on the characteristics of surrounding rocks,such as cohesion,Young’s modulus,density,shear strength,angle of internal friction,uniaxial compressive strength,quench durability index,rock mass rating,and tensile *** MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSO-ANFIS models were then developed and evaluated based on this dataset for predicting roof displacements in roadways of underground *** results indicated that the proposed intelligent techniques could accurately predict the roof displacements in roadways of underground mines with an accuracy in the range of 83%-92%.Remarkably,the SA-ANFIS model yielded the most dominant accuracy(i.e.92%).Based on the accurate predictions from the proposed techniques,the reinforced solutions can be timely suggested to ensure the stability of roadways during exploiting coal,especially in the underground coal mines exploited by the longwall mining.

关键词： Roof displacement Longwall mining Underground mine Physics-based optimization Risk assessment Mining hazards