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Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

T. Chandra Sekhara REDDY

Frontiers of Structural and Civil Engineering 2018, Volume 12, Issue 4,   Pages 490-503 doi: 10.1007/s11709-017-0445-3

Abstract: This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the strength properties of

Keywords： artificial neural networks     root mean square error     SIFCON     silica fume     metakaolin     steel fiber    

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Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based artificialneural network

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 5,   Pages 1181-1198 doi: 10.1007/s11709-021-0744-6

Abstract: The obtained results are then checked statistically with nonlinear multiple regression (NMR) and artificialneural network (ANN) modeling, and some prediction models are proposed.

Keywords： interaction     load sharing ratio     piled raft     nonlinear regression     artificial neural network    

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Service life prediction of fly ash concrete using an artificial neural network

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3,   Pages 793-805 doi: 10.1007/s11709-021-0717-9

Abstract: estimates the lifetime of fly ash concrete by developing a carbonation depth prediction model that uses an artificialneural network technique.Moreover, experimental validation carried out for the developed model shows that the artificial neural

Keywords： concrete     fly ash     carbonation     neural networks     experimental validation     service life    

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Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificialneural networks

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 1, doi: 10.1007/s11783-023-1606-3

Abstract:

● Reducting the sampling frequency can enhance the modelling process.

Keywords： HDPE     Pyrolysis     Kinetics     Thermogravimetric     ANOVA     Artificial neural network    

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Prediction of bed load sediments using different artificial neural network models

Reza ASHEGHI, Seyed Abbas HOSSEINI

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 2,   Pages 374-386 doi: 10.1007/s11709-019-0600-0

Abstract: In this paper, three different artificial neural networks (ANNs) including multilayer percepterons, radialbased function (RBF), and generalized feed forward neural network using five dominant parameters of

Keywords： bed load prediction     artificial neural network     modeling     empirical equations    

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Liquefaction assessment using microtremor measurement, conventional method and artificial neural network

Sadegh REZAEI,Asskar Janalizadeh CHOOBBASTI

Frontiers of Structural and Civil Engineering 2014, Volume 8, Issue 3,   Pages 292-307 doi: 10.1007/s11709-014-0256-8

Abstract: Also, the results obtained by the artificial neural network (ANN) were compared with microtremor measurement

Keywords： liquefaction     microtremor     vulnerability index     artificial neural networks (ANN)     microzonation    

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Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

Frontiers of Structural and Civil Engineering 2017, Volume 11, Issue 1,   Pages 90-99 doi: 10.1007/s11709-016-0363-9

Abstract: considering the experimental results, three different models of multiple linear regression model (MLR), artificialneural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and

Keywords： concrete     28 days compressive strength     multiple linear regression     artificial neural network     ANFIS     sensitivity    

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Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 1,   Pages 215-239 doi: 10.1007/s11709-018-0489-z

Abstract: This paper aims to explore two machine learning algorithms including artificial neural network (ANN)

Keywords： bentonite/sepiolite plastic concrete     compressive strength     artificial neural network     support vector machine    

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Innovative piled raft foundations design using artificial neural network

Meisam RABIEI, Asskar Janalizadeh CHOOBBASTI

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 1,   Pages 138-146 doi: 10.1007/s11709-019-0585-8

Abstract: In the meantime, the technique of artificial neural networks can be used to achieve this goal with minimumIn this paper, a model is presented based on multi-layer perceptron artificial neural network.Results of neural network indicate its proper ability in identifying the piled raft behavior.

Keywords： innovative design     piled raft foundation     neural network     optimization    

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An efficient stochastic dynamic analysis of soil media using radial basis function artificial neural

P. ZAKIAN

Frontiers of Structural and Civil Engineering 2017, Volume 11, Issue 4,   Pages 470-479 doi: 10.1007/s11709-017-0440-8

Abstract: In this research, artificial neural network is proposed and added to Monte Carlo method for sake of reducingThen, the effects of the proposed artificial neural network are illustrated on decreasing computational

Keywords： stochastic analysis     random seismic excitation     finite element method     artificial neural network     frequency    

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Automated classification of civil structure defects based on convolutional neural network

Pierclaudio SAVINO, Francesco TONDOLO

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 305-317 doi: 10.1007/s11709-021-0725-9

Abstract: this paper presents a method for automating concrete damage classification using a deep convolutional neuralThe convolutional neural network was designed after an experimental investigation of a wide number of

Keywords： concrete structure     infrastructures     visual inspection     convolutional neural network     artificial intelligence    

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Real-time tool condition monitoring method based on temperature measurement and artificial neural network

Frontiers of Mechanical Engineering doi: 10.1007/s11465-021-0661-3

Abstract: The spectrum features are then selected and input into the artificial neural network for classification

Keywords： tool condition monitoring     cutting temperature     neural network     learning rate adaption    

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of spinal lumbar interbody fusion cage subsidence using Taguchi method, finite element analysis, and artificialneural network

Christopher John NASSAU, N. Scott LITOFSKY, Yuyi LIN

Frontiers of Mechanical Engineering 2012, Volume 7, Issue 3,   Pages 247-255 doi: 10.1007/s11465-012-0335-2

Abstract: No previous studies have utilized an artificial neural network (ANN) for the design of a spinal interbodyIn this study, the neural network was applied after initiation from a Taguchi L18 

Keywords： anterior lumbar interbody fusion (ALIF)     artificial neural network (ANN)     finite element     interbody cage    

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Prediction of selected biodiesel fuel properties using artificial neural network

Solomon O. GIWA,Sunday O. ADEKOMAYA,Kayode O. ADAMA,Moruf O. MUKAILA

Frontiers in Energy 2015, Volume 9, Issue 4,   Pages 433-445 doi: 10.1007/s11708-015-0383-5

Abstract: To solve these problems, artificial neural network (ANN) has been considered as a vital tool for estimating

Keywords： biodiesel     fuel properties     artificial neural network     fatty acid     prediction    

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Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 520-536 doi: 10.1007/s11709-021-0689-9

Abstract: soils using additives as well as by predicting the strength behavior of stabilized soils using two artificial-intelligence-basedTwo artificial-intelligence-based models including artificial neural networks and support vector machinesperformance of support vector machines in predicting the strength of the investigated soils compared with artificialneural networks.

Keywords： unconfined compressive strength     artificial neural network     support vector machine     predictive models     regression    

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Title Author Date Type Operation

Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

T. Chandra Sekhara REDDY

Journal Article

Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based artificialneural network

Journal Article

Service life prediction of fly ash concrete using an artificial neural network

Journal Article

Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificialneural networks

Journal Article

Prediction of bed load sediments using different artificial neural network models

Reza ASHEGHI, Seyed Abbas HOSSEINI

Journal Article

Liquefaction assessment using microtremor measurement, conventional method and artificial neural network

Sadegh REZAEI,Asskar Janalizadeh CHOOBBASTI

Journal Article

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

Journal Article

Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

Journal Article

Innovative piled raft foundations design using artificial neural network

Meisam RABIEI, Asskar Janalizadeh CHOOBBASTI

Journal Article

An efficient stochastic dynamic analysis of soil media using radial basis function artificial neural

P. ZAKIAN

Journal Article

Automated classification of civil structure defects based on convolutional neural network

Pierclaudio SAVINO, Francesco TONDOLO

Journal Article

Real-time tool condition monitoring method based on temperature measurement and artificial neural network

Journal Article

of spinal lumbar interbody fusion cage subsidence using Taguchi method, finite element analysis, and artificialneural network

Christopher John NASSAU, N. Scott LITOFSKY, Yuyi LIN

Journal Article

Prediction of selected biodiesel fuel properties using artificial neural network

Solomon O. GIWA,Sunday O. ADEKOMAYA,Kayode O. ADAMA,Moruf O. MUKAILA

Journal Article

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Journal Article