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Examining unobserved factors associated with red light running in Vietnam:A latent class model analysis

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Transportation Safety and Environment 2022年第1期4卷 110-122页

作者： Tien Dung Chu Tomio Miwa tuan anh bui Quang Phuc Nguyen Quang Huy Vu Department of Highway and Traffic Engineering University of Transport and CommunicationsNo.3 Cau Giay StreetLang Thuong WardDong Da DistrictHanoi 10001Vietnam Institute of Materials and Systems for Sustainability Nagoya UniversityFuro-choChikusa-kuNagoya cityAichi Prefecture464-8603Japan

Red-light running(RLR)is a crucial violation that causes traffic accidents and *** factors that affect RLR is very significant to reduce the potential of this *** studies have paid considerable attention to the observable factors,but not to unobservable *** study aims to examine the effects of observable and unobservable factors on *** study uses a latent class model(LCM)to assign individuals into two classes—red-light-respectful and red-light-disrespectful road users—by surveying 751 respondents who use private transportation *** study incorporates psychological determinants into the LCM to account for unobservable *** contribution of this study is the in-depth investigation into law-respectful and law-disrespectful behaviours and intentional and unintentional *** a study has not yet been conducted in the existing *** addition,a comprehensive comparison of the LCM and a traditional ordered probit model was ***,the results suggest that the LCM is superior to the model that does not consider latent *** estimation results are in alignment with previous studies on RLR:males,younger drivers/riders,less educated road users and motorcyclists are more likely to run red *** analysis of the latent variables shows that surrounding conditions—the behaviour of other violators,the absence of traffic police,and long waiting times—increase the possibility of *** on these results,we provide suggestions to policymakers and traffic engineers:the implementation of enforcement cameras and penalties for violators are critical countermeasures to minimize the potential of RLR.

关键词： red light running(RLR) developing country latent class model(LCM) multiple indicator multiple cause(MIMIC)model latent variables(LVs) motorcycles(MCs) traffic violation

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)

An electrochemical and microstructural characterization of steel-mortar admixed with corrosion inhibitors

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Science China(Technological Sciences) 2009年第1期52卷 52-66页

作者： tuan anh Nguyen Recep AVCI Corrosion and Sustainable Infrastructure Laboratory Western Transportation InstitutePO Box 174250College of Engineering Montana State University Bozeman Imaging and Chemical Analysis Laboratory Department of Physics Montana State University Bozeman

The present research brings new insights on the role of admixed corrosion inhibitors in the processes of cement hydration and rebar corrosion. The admixing of NaCl and the corrosion inhibitors in fresh mortar was found to alter the morphology and microstructure of the hardened mortar at the steel-mortar interfacial region. The admixing of the inhibitors increased the risk of carbonation of cement hydrates at the steel-mortar interfacial region, but partially displaced chloride ions. Chloride and the admixed inhibitors facilitated the formation of different cement hydrates and affected chloride binding at the steel-mortar interfacial region. The admixing of all three inhibitors was found to increase the polarization resistance of steel, indicating reduced corrosion rate of the steel over 48-day exposures to salt ponding.

关键词： corrosion inhibitor rebar corrosion steel-mortar interface FESEM EDX XPS EIS

An Efficient Differential Evolution for Truss Sizing Optimization Using AdaBoost Classifier

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Computer Modeling in Engineering & Sciences 2023年第1期134卷 429-458页

作者： Tran-Hieu Nguyen anh-tuan Vu Hanoi University of Civil Engineering Hanoi100000Vietnam

Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses thatmust be *** a surrogatemodel to approximate the behavior of structures instead of the exact structural analyses is a possible solution to tackle this ***,most existing surrogate models have been designed based on regression *** paper proposes a novel method,called CaDE,which adopts a machine learning classification technique for enhancing the performance of the Differential Evolution(DE)*** proposed method is separated into two *** the first optimization stage,the original DE is implemented as usual,but all individuals produced in this phase are stored as inputs of the training *** on design constraints verification,these individuals are labeled as“safe”or“unsafe”and their labels are saved as outputs of the training *** collecting enough data,an AdaBoost model is trained to evaluate the safety state of *** model is then used in the second stage to preliminarily assess new individuals,and unpromising ones are rejected without checking design *** method reduces unnecessary structural analyses,thereby shortens the optimization *** benchmark truss sizing optimization problems are solved using the proposed method to demonstrate its *** obtained results show that the CaDE finds good optimal designs with less structural analyses in comparison with the original DE and four other DE *** reduction rate of five examples ranges from 18 to over 50%.Moreover,the proposed method is applied to a real-size transmission tower design problem to exhibit its applicability in practice.

关键词： Structural optimization machine learning surrogate model differential evolution AdaBoost classifier

DEVELOPING AN ENERGY BENCHMARKING SYSTEM FOR HOTEL buiLDINGS USING THE STATISTICAL METHOD AND THE SIMULATION-BASED APPROACH

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Journal of Green building 2019年第3期14卷 3-22页

作者： anh tuan Nguyen David Rockwood the Faculty of Architecture University of Science and Technology–The University of DanangViet Nam School of Architecture University of HawaiiU.S.A

Due to increased tourist activity,many cities now have a large number of hotel *** is necessary to establish measures to evaluate energy use intensity to effectively manage energy consumption in this *** study uses a combined strategy to establish an energy benchmark for hotel buildings in ***,a survey and analysis of actual building stock data of 50 hotels in Danang,Vietnam,was *** survey-based benchmark and its related data was then used to build a reference energy model to estimate an energy benchmark for other climatic regions in Vietnam by using the energy simulation *** results reveal that the average energy use intensity for hotels in Danang was 87.4 kWh/*** or 8628.6 kWh/***,this study proposes that because of the differing expectations of comfort standards,hotels of different grades should have separate *** study also proposes an energy intensity-based rating scale,including 7 grades from the least energy intensive(grade A)to the most energy intensive(grade G),which can be used to manage,label,or encourage sustainable energy use in hotel *** relationship between the energy use intensity and the occupancy rate of the hotels was reported,compared,and *** was found that occupancy rate has no significant impact on the energy use *** the survey result,some predictive models were developed to estimate annual energy consumption of hotel buildings based on their *** simulated benchmarks for other regions were also *** results demonstrate many potential applications in the management,design and construction,and renovation of this building type.

关键词： energy benchmarking hotel energy use intensity energy labeling energy rating scale simulation-based benchmarking

Spherical Fuzzy WASPAS-based Entropy Objective Weighting for International Payment Method Selection

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Computers, Materials & Continua 2022年第7期72卷 2055-2075页

作者： Phi-Hung Nguyen Thanh-tuan Dang Kim-anh Nguyen Hong-anh Pham Faculty of Business FPT UniversityHanoi100000Vietnam Department of Business Management National Taipei University of TechnologyTaipei1068Taiwan Department of Industrial Engineering and Management National Kaohsiung University of Science and TechnologyKaohsiung80778Taiwan

In international trade,exporters prefer to receive payments as quickly as possible,and importers want to make payments as late as *** this respect,the payment field,an essential condition for trade transactions,also represents the positions of exporters and importers *** addition,there are many cases in which various variables must be considered rather than only one specific variable representatively affecting payment,particularly in the case of import-export Small and Medium-Sized Enterprises(SMEs)from emerging countries.A selection of proper payment methods can be categorized as a Multi-Criteria Decision-Making(MCDM)***,this study aims to propose a novel and efficient Spherical Fuzzy Weighted Aggregated Sum Product Assessment based Entropy Objective Weighting method(SF-EW and WASPAS-SF)to evaluate international payment methods with uncertain ***,SF-EW model is applied to determine the relative weights of critical ***,international payment method alternatives are prioritized by the WASPAS-SF *** essential factors for four international payment methods are proposed based on experts’opinions and the existing literature.A real-world case study from Vietnamese import-export SMEs is presented to validate the applicability of the proposed *** results indicated that“Characteristics of payment method(PA)”had the most significant impact on international payment method *** comparison,“Letters of Credit(L/C)”was the most reliable payment method with the highest ranking available to international ***,a sensitivity analysis was performed to examine the validity and robustness of the proposed decision support ***,this study could contribute to international payment services and in the context of globalization and international trade.

关键词： International payment Vietnam MCDM spherical fuzzy theory entropy WASPAS

Real-time security margin control using deep reinforcement leamning

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Energy and AI 2023年第3期13卷 52-63页

作者： Hannes Hagmar Robert Eriksson Le anh tuan Chalmers University of Technology Gothenburg 41296Sweden Svenska kraftnat(Swedish National Grid) Sundbyberg 17224Sweden

This paper develops a real-time control method based on deep reinforcement learning aimed to determine the optimal control actions to maintain a sufficient secure operating *** secure operating limit refers to the limit to the most stressed pre-contingency operating point of an electric power system that can withstand a set of credible contingencies without violating stability *** developed deep reinforcement learning method uses a hybrid control scheme that is capable of simultaneously adjusting both discrete and continuous action *** performance is evaluated on a modified version of the Nordic32 test *** results show that the developed deep reinforcement learning method quickly learns an effective control policy to ensure a sufficient secure operating limit for a range of different system *** performance is also compared to a control based on a rule-based look-up table and a deep reinforcement learning control adapted for discrete action *** hybrid deep reinforcement learning control managed to achieve significantly better on all of the defined test sets,indicating that the possibility of adjusting both discrete and continuous action variables resulted in a more flexible and efficient control policy.

关键词： Deep reinforcement learning Preventive control Proximal policy optimization Secure operating limit

Measurement investigation on the feasibility of shallow geothermal energy for heating and cooling applied in agricultural greenhouses of Shouguang City: Ground temperature profiles and geothermal potential

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Information Processing in Agriculture 2021年第2期8卷 251-269页

作者： anh tuan Le Liang Wang Yang Wang Daoliang Li National Innovation Center for Digital Fishery China Agricultural UniversityBeijing 100083China Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture Beijing 100083China Key Laboratory of Agricultural Information Acquisition Technology Ministry of AgricultureBeijing 100083China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Department of Electronics Nam Dinh University of Technology EducationNam Dinh 07000Viet Nam

The use of electrical energy for heating and cooling systems to control the temperature in greenhouses will lead to high production and product *** solve this problem,shallow geothermal energy as a local source of energy could be *** this study,a measurement model,the distribution profiles of temperature,and a preliminary assessment of the geothermal potential in the shallow zone at depths of 0.1 m to 3.6 m in Shouguang City,Shandong Province,eastern China were *** measurement results showed that the annual average temperature at depths of 0.1–3.6 m ranged from 13.1℃ to 17.6℃.Preliminary assessment results of the geothermal potential showed that the daily average temperature difference between the air and at depths of 1.5–3.6 m was mainly from 10℃ to 25℃ during the winter months and between-15℃ and-5℃ during the summer ***,the heating systems could operate during January,February,November,and *** May,June,and July,the cooling systems could be ***,the measurement model gave good stability results,and it could be used in combination with the monitoring of the groundwater table,a survey of the thermal conductivity of the soil,climate change studies,which helps reduce unnecessary time and costs.

关键词： Ground temperature profile Geothermal energy Ground heat exchanger Measurement model Shouguang