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Frontiers of Structural and Civil Engineering 2022年第10期16卷 1267-1286页

作者： Thuy-Anh NGUYEN hai-bang ly Van Quan TRAN Department of Civil Engineering University of Transport TechnologyHanoi 100000Vietnam

Shear failure of slender reinforced concrete beams without stirrups has surely been a complicated occurrence that has proven challenging to adequately understand.The primary purpose of this work is to develop machine learning models capable of reliably predicting the shear strength of non-shear-reinforced slender beams(SB).A database encompassing 1118 experimental findings from the relevant literature was compiled,containing eight distinct factors.Gradient Boosting(GB)technique was developed and evaluated in combination with three different optimization algorithms,namely Particle Swarm Optimization(PSO),Random Annealing Optimization(RA),and Simulated Annealing Optimization(SA).The findings suggested that GB-SA could deliver strong prediction results and effectively generalizes the connection between the input and output variables.Shap values and two-dimensional PDP analysis were then carried out.Engineers may use the findings in this work to define beam’s geometrical components and material used to achieve the desired shear strength of SB without reinforcement.

关键词： slender beam shear strength gradient boosting optimization algorithms

Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete

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Frontiers of Structural and Civil Engineering 2022年第7期16卷 928-945页

作者： Van Quan TRAN hai-Van Thi MAI Thuy-Anh NGUYEN hai-bang ly Faculty of Civil Engineering University of Transport TechnologyHanoi 100000Vietnam

The compressive strength of self-compacting concrete(SCC)needs to be determined during the construction design process.This paper shows that the compressive strength of SCC(CS of SCC)can be successfully predicted from mix design and curing age by a machine learning(ML)technique named the Extreme Gradient Boosting(XGB)algorithm,including non-hybrid and hybrid models.Nine ML techniques,such as Linear regression(LR),K-Nearest Neighbors(KNN),Support Vector Machine(SVM),Decision Trees(DTR),Random Forest(RF),Gradient Boosting(GB),and Artificial Neural Network using two training algorithms LBFGS and SGD(denoted as ANN_LBFGS and ANN_SGD),are also compared with the XGB model.Moreover,the hybrid models of eight ML techniques and Particle Swarm Optimization(PSO)are constructed to highlight the reliability and accuracy of SCC compressive strength prediction by the XGB_PSO hybrid model.The highest number of SCC samples available in the literature is collected for building the ML techniques.Compared with previously published works’performance,the proposed XGB method,both hybrid and non-hybrid models,is the most reliable and robust of the examined techniques,and is more accurate than existing ML methods(R2=0.9644,RMSE=4.7801,and MAE=3.4832).Therefore,the XGB model can be used as a practical tool for engineers in predicting the CS of SCC.

关键词： compressive strength self-compacting concrete machine learning techniques particle swarm optimization extreme gradient boosting

Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete

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结构与土木工程前沿:英文版 2023年第2期17卷 284-305页

作者： hai-Van Thi MAI May Huu NGUYEN Son Hoang TRINH hai-bang ly Civil Engineering Department University of Transport TechnologyHanoi 100000Vietnam Civil and Environmental Engineering Program Graduate School of Advanced Science and EngineeringHiroshima UniversityHiroshima 739-8527Japan

Fiber-reinforced self-compacting concrete(FRSCC)is a typical construction material,and its compressive strength(CS)is a critical mechanical property that must be adequately determined.In the machine learning(ML)approach to estimating the CS of FRSCC,the current research gaps include the limitations of samples in databases,the applicability constraints of models owing to limited mixture components,and the possibility of applying recently proposed models.This study developed different ML models for predicting the CS of FRSCC to address these limitations.Artificial neural network,random forest,and categorical gradient boosting(CatBoost)models were optimized to derive the best predictive model with the aid of a 10-fold cross-validation technique.A database of 381 samples was created,representing the most significant FRSCC dataset compared with previous studies,and it was used for model development.The findings indicated that CatBoost outperformed the other two models with excellent predictive abilities(root mean square error of 2.639 MPa,mean absolute error of 1.669 MPa,and coefficient of determination of 0.986 for the test dataset).Finally,a sensitivity analysis using a partial dependence plot was conducted to obtain a thorough understanding of the effect of each input variable on the predicted CS of FRSCC.The results showed that the cement content,testing age,and superplasticizer content are the most critical factors affecting the CS.

关键词： compressive strength self-compacting concrete artificial neural network decision tree CatBoost

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 factors.The 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 error.In addition,the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence plots.The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS.Following 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

Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling with Relief algorithm

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Frontiers of Structural and Civil Engineering 2022年第2期16卷 224-238页

作者： hai-bang ly Huong-Lan Thi VU Lanh Si HO Binh Thai PHAM Department of Civil Engineering University of Transport TechnologyHanoi100000Vietnam Civil and Environmental Engineering Program Hiroshima UniversityHiroshima739-8527Japan Department of Science Technology and International CooperationUniversity of Transport TechnologyHanoi100000Vietnam

The consolidation coefficient of soil(C_(v))is a crucial parameter used for the design of structures leaned on soft soi.In general,the C_(v) is determined experimentally in the laboratory.However,the experimental tests are time-consuming as well as expensive.Therefore,researchers tried several ways to determine C_(v) via other simple soil parameters.In this study,we developed a hybrid model of Random Forest coupling with a Relief algorithm(RF-RL)to predict the C_(v) of soil.To conduct this study,a database of soil parameters collected from a case study region in Vietnam was used for modeling.The performance of the proposed models was assessed via statistical indicators,namely Coefficient of determination(R^(2)),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE).The proposal models were constructed with four sets of soil variables,including 6,7,8,and 13 inputs.The results revealed that all models performed well with a high performance(R^(2)>0.980).Although the RF-RL model with 13 variables has the highest prediction accuracy(R^(2)=0.9869),the difference compared with other models was negligible(i.e.,R^(2)=0.9824,0.9850,0.9825 for the cases with 6,7,8 inputs,respectively).Thus,it can be concluded that the hybrid model of RF-RL can be employed to predict C_(v) based on the basic soil parameters.

关键词： soil consolidation coefficient machine learning random forest Relief

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 soil.In 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 Samples.Validation 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 comparison.Results 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

Restimate:Recovery Estimation Tool for Resilience Planning

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Journal of Safety Science and Resilience 2024年第1期5卷 47-63页

作者： Scott Miles Megan ly Nick Terry Youngjun Choe Department of Human Centered Design&Engineering Campus Box 352315University of WashingtonSeattleWA 98195United States of America Department of Industrial&Systems Engineering Box 352650University of WashingtonSeattleWA 98195United States of America

The U.S.National Institute of Standards and Technology(NIST)published the Community Resilience Planning Guide in 2016.The NIST Guide advocates for a participatory process for developing a performance measurement framework for the jurisdiction’s resilience against a scenario hazard.The framework centers around tables of expected and desired recovery times for selected community assets,such as electricity,water,and natural gas infrastructures.The NIST Guide does not provide a method for estimating the expected recovery times.However,building high-fidelity computer models for such estimations requires substantial resources that even larger ju-risdictions cannot cost-justify.The most promising approach to recovery time estimation is to systematically use data elicited from people to tap into the wisdom of the(knowledgeable)crowd.This paper describes a novel research-through-design project to enable the computer-supported elicitation of recovery time series data.This work is the first in the literature to examine people’s ability to estimate recovery curves and how design in-fluences such estimations.Its main contribution to resilience planning is three-fold:development of a new elicitation tool called Restimate,understanding its potential user base,and providing insights into how it can facilitate resilience planning.Restimate is the first tool to enable evidence-based expert elicitation in any community with limited resources for resilience planning.Beyond resilience planning,those who facilitate high-stakes planning activities under large uncertainties(e.g.,mission-critical system design and planning)will benefit from a similar research-through-design process.

关键词： expert elicitation disaster natural hazard infrastructure community resilience restoration user-centered design computer-supported cooperative work

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 loop.The surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement devices.The proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential scenarios.To 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 process.The approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical examples.The study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured data.Overall,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally intensive.This 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

Self-assembly of tau fragments as a key pathologic event in tauopathies

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Neural Regeneration Research 2024年第12期19卷 2565-2566页

作者： ly Thi Huong Luu Le Jung Hoon Lee Min Jae Lee Department of Biochemistry and Molecular Biology Seoul National University College of MedicineSeoulKorea

Tau protein is encoded by the microtubuleassociated protein tau(MAPT)gene and stabilizes microtubules in the neurons.Tau has unique biophysical properties that make it both highly water-soluble and positively charged.Although tau is an intrinsically disordered protein lacking defined secondary structures,it can acquire highly organized-sheet structures and stack into a filamentous inclusion such as a paired helical filament(PHF).

关键词： tau pathologic assembly

OsbZIP53 Negatively Regulates Immunity Response by Involving in Reactive Oxygen Species and Salicylic Acid Metabolism in Rice

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Rice science 2024年第2期31卷 190-202,I0022-I0028页

作者： WU Lijuan HAN Cong WANG Huimei HE Yuchang LIN hai WANG Lei CHEN Chen E Zhiguo State Key Laboratory of Rice Biology and Breeding China National Rice Research InstituteHangzhou 310006China Jiangsu Key Laboratory of Crop Genetics and Physiology/Key Laboratory of Plant Functional Genomics of the Ministry of Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops Agricultural College of Yangzhou UniversityYangzhou 225009China

The basic region/leucine zipper(bZIP)transcription factors play important roles in plant development and responses to abiotic and biotic stresses.OsbZIP53 regulates resistance to Magnaporthe oryzae in rice by analyzing APIP5-RNAi transgenic plants.To further investigate the biological functions of OsbZIP53,we generated osbzip53 mutants using CRISPR/Cas9 editing and also constructed OsbZIP53 over-expression transgenic plants.Comprehensive analysis of phenotypical,physiological,and transcriptional data showed that knocking-out OsbZIP53 not only improved disease resistance by inducing a hypersensitivity response in plants,but also regulated the immune response through the salicylic acid pathway.Specifically,disrupting OsbZIP53 increased H2O2 accumulation by promoting reactive oxygen species generation through up-regulation of several respiratory burst oxidase homologs(Osrboh genes)and weakened H2O2 degradation by directly targeting OsMYBS1.In addition,the growth of osbzip53 mutants was seriously impaired,while OsbZIP53 over-expression lines displayed a similar phenotype to the wild type,suggesting that OsbZIP53 has a balancing effect on rice immune response and growth.

关键词： OsbZIP53 hypersensitive response reactive oxygen species metabolism rice immunity salicylic acid transcription factor