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An Effective Hybrid Model of ELM and Enhanced GWO for Estimating compressive strength of Metakaolin-Contained Cemented Materials

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1521-1555页

作者： Abidhan Bardhan Raushan Kumar Singh Mohammed Alatiyyah Sulaiman Abdullah Alateyah Department of Civil Engineering National Institute of Technology PatnaPatnaIndia Department of Computer Engineering and Applications GLA UniversityMathuraIndia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science College of Science and ArtsQassim UniversityUnaizahSaudi Arabia

This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

关键词： Metakaolin-contained cemented materials compressive strength extreme learning machine grey wolf optimizer swarm intelligence uncertainty analysis artificial intelligence

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Assessment of compressive strength of jet grouting by machine learning

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第1期16卷 102-111页

作者： Esteban Diaz Edgar Leonardo Salamanca-Medina Roberto Tomas Departamento de Ingeniería Civil Escuela Politécnica SuperiorUniversidad de AlicanteP.O.Box 99E-03080AlicanteSpain

Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.

关键词： Jet grouting Ground improvement compressive strength Machine learning

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An Investigation into the compressive strength,Permeability and Microstructure of Quartzite-Rock-Sand Mortar

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Fluid Dynamics & Materials Processing 2024年第4期20卷 859-872页

作者： Wei Chen Wuwen Liu Yue Liang School of Civil Engineering Architecture and EnvironmentHubei University of TechnologyWuhan430068China

River sand is an essential component used as a fine aggregate in mortar and concrete.Due to unrestrained exploitation,river sand resources are gradually being exhausted.This requires alternative solutions.This study deals with the properties of cement mortar containing different levels of manufactured sand(MS)based on quartzite,used to replace river sand.The river sand was replaced at 20%,40%,60%and 80%with MS(by weight or volume).The mechanical properties,transfer properties,and microstructure were examined and compared to a control group to study the impact of the replacement level.The results indicate that the compressive strength can be improved by increasing such a level.The strength was improved by 35.1%and 45.5%over that of the control mortar at replacement levels of 60%and 80%,respectively.Although there was a weak link between porosity and gas permeability in the mortars with manufactured sand,the gas permeability decreased with growing the replacement level.The microstructure of the MS mortar was denser,and the cement paste had fewer microcracks with increasing the replacement level.

关键词： Manufactured sand quartzite compressive strength gas permeability microstructure

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Prediction of Geopolymer Concrete compressive strength Using Convolutional Neural Networks

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings th

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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Enhancing compressive strength and durability of self-compacting concrete modified with controlled-burnt sugarcane bagasse ash-blended cements

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Frontiers of Structural and Civil Engineering 2022年第2期16卷 161-174页

作者： Duc-Hien LE Yeong-Nain SHEEN Khanh-Hung NGUYEN Sustainable Developments in Civil Engineering Research Group Faculty of Civil EngineeringTon Duc Thang UniversityHo Chi Minh City700000Vietnam Department of Civil Engineering Kaohsiung University of Science and TechnologyKaohsiung80778China Department of Civil Engineering Lac Hong UniversityBien Hoa810000Vietnam

In sugar industries,the growing amount of sugarcane bagasse ash(SBA),a byproduct released after burning bagasse for producing electricity,is currently causing environmental pollution.The residual ash displays a pozzolanic potential;and hence,it has potential as a cement addictive.This study focuses on enhancing suitability of SBA through incorporating ground blast furnace slag(BFS)in manufacturing self-compacting concretes(SCCs).For this purpose,SBA was processed by burning at 700°C for 1 h,before being ground to the cement fineness of 4010 cm2/g.SCC mixtures were prepared by changing the proportions of SBA and BFS(i.e.,10%,20%,and 30%)in blended systems;and their performance was investigated.Test results showed that the presence of amorphous silica was detected for the processed SBA,revealing that the strength activity index was above 80%.The compressive strength of SCC containing SBA(without BFS)could reach 98%–127%of that of the control;combination of SBA and 30%BFS gets a similar strength to the control after 28 d.Regarding durability,the 10%SBA+30%BFS mix exhibited the lowest risk of corrosion.Moreover,the joint use of SBA and BFS enhanced significantly the SCC’s sulfate resistance.Finally,a hyperbolic formula for interpolating the compressive strength of the SBA-based SCC was proposed and validated with error range estimated within±10%.

关键词： sugarcane bagasse ash self-compacting concrete compressive strength sulfate resistance water absorption strength formula

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Effect and mechanism of nano-Ca_(10)(PO_(4))_(6)(OH)_(2)additive on compressive strength of calcium aluminate cement at high temperature

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Journal of Iron and Steel Research(International) 2022年第7期29卷 1063-1072页

作者： Qian Sun En-hui Wang Xin-mei Hou Zhi-jun He Tong-xiang Liang Collaborative Innovation Center of Steel Technology University of Science and Technology BeijingBeijing 100083China School of Material and Metallurgy University of Science and Technology LiaoningAnshan 114501LiaoningChina School of Materials Science and Engineering Jiangxi University of Science and TechnologyGanzhou 341000JiangxiChina

Calcium aluminate cement(CAC)is widely used as a binder for refractory materials,and thus the improvement in compressive strength is of vital importance for CAC applied at high temperature.For this purpose,nano-Ca_(10)(PO_(4))_(6)(OH)_(2)additive with a ratio of 0.5–1.5 mass%was added with the water-cement ratio to be 0.4.X-ray diffraction and isothermal calorimetry analysis demonstrate that nano-Ca_(10)(PO_(4))_(6)(OH)_(2)additive can shorten the hydration process and promote the formation of main hydrates of CaAl2O4·10H2O(CAH10)and Ca2Al2O5·8H2O(C2AH8).In addition,scanning electron microscopy results suggest that nano-Ca_(10)(PO_(4))_(6)(OH)_(2)can protect CAH10 and C2AH8 from being destroyed during the calcination,guaranteeing that these thin lamellar crystals are intertwined to form the denser microstructure.Benefited from above effects,nano-Ca_(10)(PO_(4))_(6)(OH)_(2)can obviously improve the compressive strength of the CAC mortar samples cured for 7 d after calcination at 1100°C,while the improving effect is dependent upon its contents.Especially,compared with the one without the additive,the compressive strength of the sample with 1.0%nano-Ca_(10)(PO_(4))_(6)(OH)_(2)is increased by 14%.

关键词： Nano-Ca_(10)(PO_(4))_(6)(OH)_(2) Cement Calcination Microstructure compressive strength

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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

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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

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Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive strength of concrete using rebound hammer field test

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Frontiers of Structural and Civil Engineering 2022年第8期16卷 1003-1016页

作者： Dung Quang VU Fazal EJALAL Mudassir IQBAL Dam Duc NGUYEN Duong Kien TRONG Indra PRAKASH Binh Thai PHAM University of Transport Technology Hanoi 100000Vietnam Department of Civil Engineering Shanghai Jiao Tong UniversityShanghai 200240China Department of Civil Engineering University of Engineering and TechnologyPeshawar 25120Pakistan DDG(R)Geological Survey of India Gandhinagar 382010India

In this study,we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System(ANFIS)optimized by Shuffled Complex Evolution(SCE)on the one hand and ANFIS with Artificial Bee Colony(ABC)on the other hand.These were used to predict compressive strength(Cs)of concrete relating to thirteen concrete-strength affecting parameters which are easy to determine in the laboratory.Field and laboratory tests data of 108 structural elements of 18 concrete bridges of the Ha Long-Van Don Expressway,Vietnam were considered.The dataset was randomly divided into a 70:30 ratio,for training(70%)and testing(30%)of the hybrid models.Performance of the developed fuzzy metaheuristic models was evaluated using standard statistical metrics:Correlation Coefficient(R),Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).The results showed that both of the novel models depict close agreement between experimental and predicted results.However,the ANFIS-ABC model reflected better convergence of the results and better performance compared to that of ANFIS-SCE in the prediction of the concrete Cs.Thus,the ANFIS-ABC model can be used for the quick and accurate estimation of compressive strength of concrete based on easily determined parameters for the design of civil engineering structures including bridges.

关键词： shuffled complex evolution artificial bee colony ANFIS concrete compressive strength Vietnam

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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

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