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Machine learning based models for predicting compressive strength of geopolymer concrete

Frontiers of Structural and Civil Engineering 2024年第7期18卷 1028-1049页

作者： Quang-Huy LE duy-hung nguyen Thanh SANG-TO Samir KHATIR Hoang LE-MINH Amir H.GANDOMI Thanh CUONG-LE Center for Engineering Application and Technology Solutions Ho Chi Minh City Open UniversityHo Chi Minh 700000Vietnam Institute of Civil Engineering Ho Chi Minh City University of TransportHo Chi Minh 700000Vietnam Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia

Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly *** effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important *** facilitate engineering work on the material,an efficient predicting model is *** this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(SVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting *** standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is *** effectiveness of the models is assessed via statistical metrics including root mean squared error(RMSE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MSE)is used as the loss function to be minimized during the model fitting *** three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and *** numerical results suggest that the DNN model generally outperforms the other two models.

关键词： geopolymer concrete compressive strength prediction machine-learning based model deep neural network K-nearest neighbor support vector machines

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Fully Completed Spherical Fuzzy Approach-Based Z Numbers(PHIModel)for Enhanced Group Expert Consensus

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Computers, Materials & Continua 2024年第7期80卷 1655-1675页

作者： Phi-hung nguyen Research Center of Applied Sciences Faculty of BusinessFPT UniversityHanoi100000Vietnam

This study aims to establish an expert consensus and enhance the efficacy of decision-making processes by integrating Spherical Fuzzy Sets(SFSs)and Z-Numbers(SFZs).A novel group expert consensus technique,the PHImodel,is developed to address the inherent limitations of both SFSs and the traditional Delphi technique,particularly in uncertain,complex *** such contexts,the accuracy of expert knowledge and the confidence in their judgments are pivotal *** study provides the fundamental operational principles and aggregation operators associated with SFSs and Z-numbers,encompassing weighted geometric and arithmetic operators alongside fully developed operators tailored for SFZs ***,a case study and comparative analysis are conducted to illustrate the practicality and effectiveness of the proposed operators and *** the PHI model with SFZs numbers represents a significant advancement in decision-making frameworks reliant on expert ***,this combination serves as a comprehensive tool for decision-makers,enabling them to achieve heightened levels of consensus while concurrently assessing the reliability of expert *** case study results demonstrate the PHI model’s utility in resolving complex decision-making scenarios,showcasing its ability to improve consensus-building processes and enhance decision ***,the comparative analysis highlights the superiority of the integrated approach over traditional methodologies,underscoring its potential to revolutionize decision-making practices in uncertain environments.

关键词： Spherical fuzzy sets Delphi method Z-numbers expert consensus PHI model uncertainty

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An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

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Computers, Materials & Continua 2024年第3期78卷 3585-3602页

作者： duy Quang Tran Huy Q.Tran Minh Van nguyen Faculty of Civil Engineering Nha Trang UniversityNha Trang650000Vietnam Robotics and Mechatronics Research Group Faculty of Engineering and TechnologyNguyen Tat Thanh UniversityHo Chi Minh City700000Vietnam Department of Civil Engineering Ho Chi Minh City University of Technology and EducationHo Chi Minh City700000Vietnam

With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service *** volume is an influential parameter for planning and operating traffic *** study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning *** fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal ***,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition *** second aspect involves predicting traffic volume using the long short-term memory ***,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent ***,the fusion of the obtained results leads to a final traffic volume *** experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and *** achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the *** findings highlight the accuracy of traffic pattern ***,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.

关键词： Ensemble empirical mode decomposition traffic volume prediction long short-term memory optimal hyperparameters deep learning

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Study of relationship between motion of mechanisms in gas operated weapon and its shock absorber

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Defence Technology（防务技术） 2024年第3期33卷 42-54页

作者： Jiri Balla Roman Vitek Dung nguyen Van Zbynek Krist hung nguyen Van Department of Weapons and Ammunition University of Defence BrnoCzech Republic Department of Weapons Le Quy Don Technical UniversityHanoiViet Nam Department of Design and Special Engineering Alexander Dubcek University of TrencinSlovakia

The article deals with the motion of the breech block carrier and the weapon casing of an automatic weapon mounted on a flexible carriage and the base of the *** works,which did not consider the dynamic properties of the base of the weapon,did not allow to reconcile the calculated and experimental results of the weapon casing displacement when shooting from firing *** the analysis of the motion of individual parts,the methods of mathematical modelling and firing experiments using a high-speed camera were *** show the best accord with experiment when modelling the system with 4 degrees of *** oscillation of the system regarding the movement of the breech block carrier and the weapon casing was investigated under changed conditions of rate of fire,the use of a muzzle brake and different types of shock *** velocities and displacements of the weapon casing and the breech block carrier at different values of the impulse of the gases to the breech block carrier were determined.

关键词： Shock absorber Gas-operated weapon Force-impulse diagram Recoil system Breech block carrier

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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of Structural and Civil Engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.nguyen-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

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Forecasting measured responses of structures using temporal deep learning and dual attention

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Frontiers of Structural and Civil Engineering 2024年第6期18卷 832-850页

作者： Viet-hung DANG Trong-Phu nguyen Thi-Lien PHAM Huan X.nguyen Department of Structural Mechanics Hanoi University of Civil EngineeringHanoi 11600Vietnam Faculty of Applied Science University of Economics-Technology for IndustriesHanoi 11600Vietnam London Digital Twin Research Centre Faculty of Science and TechnologyMiddlesex UniversityLondon NW44BTUK International School Vietnam National UniversityHanoi 11600Vietnam

The objective of this study is to develop a novel and efficient model for forecasting the nonlinear behavior of structures in response to time-varying random *** key idea is to design a deep learning architecture to leverage the relationships,between external excitations and structure's vibration signals,and between historical values and future values,within multiple time-series *** proposed method consists of two main steps:the first step applies a global attention mechanism to combine multiple-measured time series and time-varying excitation into a weighted time series before feeding it to a temporal architecture;the second step utilizes a self-attention mechanism followed by a fully connected layer to predict multi-step future *** viability of the proposed method is demonstrated via two case studies involving synthetic data from a three-dimensional(3D)reinforced concrete structure and experimental data from an 18-story steel ***,comparison and robustness studies are carried out,showing that the proposed method outperforms conventional methods and maintains high performance in the presence of noise with an amplitude of less than 10%.

关键词： structural dynamic time-varying excitation deep learning signal processing response forecasting

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Heterogeneous decentralised machine unlearning with seed model distillation

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 608-619页

作者： Guanhua Ye Tong Chen Quoc Viet hung nguyen Hongzhi Yin School of Information Technology and Electrical Engineering The University of QueenslandSt LuciaQueenslandAustralia School of Information and Communication Technology Griffith UniversityBrisbaneQueenslandAustralia

As some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model,personalised IoT service pro-viders have to put unlearning functionality into their *** most straight-forward method to unlearn users'contribution is to retrain the model from the initial state,which is not realistic in high throughput applications with frequent unlearning *** some machine unlearning frameworks have been proposed to speed up the retraining process,they fail to match decentralised learning scenarios.A decentralised unlearning framework called heterogeneous decentralised unlearning framework with seed(HDUS)is designed,which uses distilled seed models to construct erasable en-sembles for all ***,the framework is compatible with heterogeneous on-device models,representing stronger scalability in real-world *** experiments on three real-world datasets show that our HDUS achieves state-of-the-art performance.

关键词： data mining data privacy machine learning

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A Projection and Decomposition Approach for Multi-agent Coordinated Scheduling in Power Systems

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 991-996页

作者： Haifeng Qiu Zhigang Li Hongjun Gao hung Dinh nguyen Veerapandiyan Veerasamy Hoay Beng Gooi the School of Electrical and Electronic Engineering Nanyang Technological University639798 Singapore the School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China the College of Electrical Engineering Sichuan UniversityChengdu 610065China

Aiming at multi-agent coordinated scheduling problems in power systems under uncertainty,a generic projection and decomposition(P&D)approach is proposed in this *** canonical min-max-min two-stage robust optimization(TSRO)model with coupling constraints is equivalent to a concise robust optimization(RO)model in the version of mixed-integer linear programming(MILP)via feasible region *** decentralized decoupling of the non-convex MILP problem is realized through a dual decomposition algorithm,which ensures the fast convergence to a high-quality solution in the distributed *** tests verify the superior performance of the proposed P&D approach over the existing distributed TSRO method.

关键词： Coordinated scheduling multi-agent system distributed robust optimization projection and decomposition

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Comparative Chemical Research in Essential Oils from Six Apiaceae Species Growing in the Northern Region of Vietnam

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Phyton-International Journal of Experimental Botany 2024年第7期93卷 1677-1687页

作者： nguyen Quang hung nguyen Thanh Tung nguyen Phuong Hanh Chu Thi Thu Ha nguyen Thi Nhung nguyen Thai An Vu Xuan Giang Luong Van Hao Oleh Koshovyi Ain Raal Institute of Ecology and Biological Resources Vietnam Academy of Science and Technology(VAST)Cau GiayHa Noi100000Viet Nam Graduate University of Science and Technology Vietnam Academy of Science and Technology(VAST)Cau GiayHa Noi100000Viet Nam Department of Pharmacognosy Faculty of Pharmacognosy and Traditional MedicineHanoi University of PharmacyHoan KiemHa Noi100000Viet Nam Sa Pa Research Center of Medicinal Materials National Institute of Medicinal MaterialsDien Bien PhuSa PaLao Cai330000Viet Nam Institute of Pharmacy Faculty of MedicineUniversity of TartuTartu50411Estonia

Our study aimed to compare the essential oil(EO)concentration and composition of several Apiaceae species growing in the Northern region of *** yields of EOs from materials ranged from 0.03%(root EO of Angelica acutiloba and aerial parts EO of Heracleum bivittatum)-0.27%(leaf EO of Xyloselinum vietnamense).Gas chromatography-mass spectrometry(GC-MS)allowed the identiﬁcation of 74 components in the EOs of six Apiaceae species,making up 94.4%–100.0%of the *** EO from Angelica acutiloba,(Z)-ligustilide accounted for an extremely large proportion(94.9%).EO of Angelica pubescens was dominated by six character-istic components includingα-pinene(21.5%),β-phellandrene(18.1%),p-cymene(12.2%),3-methylnonane(8.7%),o-cymene(8.1%),and D-sylvestrene(6.2%).The EO from Cryptotaenia japonica was characterized by high amounts ofα-selinene(48.7%),β-selinene(23.7%),and trans-β-farnesene(5.4%).The EOs from leaves and stems of Xyloselinum vietnamense were characterized by high concentrations of sabinene(69.8%and 33.8%),4-terpineol(8.7%and 7.4%)andβ-pinene(4.0%and 6.5%)while EOs from aerial parts and root of Xylo-selinum leonidii comprise four characteristic monoterpenes includingα-pinene(28.2%and 52.8%),β-pinene(7.9%and 10.3%),β-phellandrene(7.6%and 15.3%),and sabinene(3.0%and 4.1%).Additionally,cryptone is also one of the major components in the EO of Xyloselinum leonidii(13.2%in the aerial parts oil and 2.8%in the root oil).In the EOs isolated from the aerial parts and root of Heracleum bivittatum,α-pinene(22.5%and 70.2%)andβ-pinene(43.2%and 20.0%)were the predominant *** appeared in the EO from aerial parts of Heracleum bivittatum with a relatively high concentration(13.5%)while bornyl acetate(5.1%)was also one of the main components in the EO from its aerial parts but was not detected in other Apiaceae species in the present *** databases help identify and control the quality of plant material studied from the family Apiaceae growing in Vietnam.

关键词： Apiaceae essential oil terpenoids gaschromatography-mass spectrometry

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Optimal Scheduling of Multiple Rail Cranes in Rail Stations with Interference Crane Areas

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Intelligent Automation & Soft Computing 2024年第1期39卷 15-31页

作者： nguyen Vu Anh duy nguyen Le Thai nguyen Huu Tho Faculty of Engineering and Technology Nguyen Tat Thanh UniversityHo Chi Minh City700000Vietnam

In this paper,we consider a multi-crane scheduling problem in rail stations because their operations directly influence the throughput of the rail *** particular,the job is not only assigned to cranes but also the job sequencing is implemented for each crane to minimize the makespan of cranes.A dual cycle of cranes is used to minimize the number of working cycles of *** rail crane scheduling problems in this study are based on the movement of *** consider not only the gantry moves,but also the trolley moves as well as the rehandle cases are also included.A mathematical model of multi-crane scheduling is *** traditional and parallel simulated annealing(SA)are adapted to determine the optimal scheduling *** examples are conducted to evaluate the applicability of the proposed *** of the proposed parallel SA is done by comparing it to existing previous *** of numerical computation highlighted that the parallel SA algorithm outperformed the SA and gave better solutions than other considered algorithms.

关键词： Multi-crane scheduling logistics containers makespan rail stations

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