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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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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 ofpredicted compatible outputs but the RBF with 79% correct classification rate corresponding to 0.191 network

Keywords： bed load prediction     artificial neural network     modeling     empirical equations    

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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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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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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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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.training of a three-layer perceptron was selected for the calculation of weights and biases of the networkMoreover, experimental validation carried out for the developed model shows that the artificial neuralnetwork has strong potential as a feasible tool to accurately predict the carbonation depth of fly ash

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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Assessing artificial neural network performance for predicting interlayer conditions and layer modulus

Lingyun YOU, Kezhen YAN, Nengyuan LIU

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 2,   Pages 487-500 doi: 10.1007/s11709-020-0609-4

Abstract: The objective of this study is to evaluate the performance of the artificial neural network (ANN) approach

Keywords： asphalt pavement     interlayer conditions     finite element method     artificial neural network     back-calculation    

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Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

Frontiers in Energy doi: 10.1007/s11708-023-0891-7

Abstract: intersection of disciplines deepens, the field of battery modeling is increasingly employing various artificialbranches of AI, to lithium-ion battery state of health (SOH), focusing on the advantages and strengths of neuralnetwork (NN) methods in ML for lithium-ion battery SOH simulation and prediction.

Keywords： machine learning     lithium-ion battery     state of health     neural network     artificial intelligence    

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An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete

Fangyu LIU, Wenqi DING, Yafei QIAO, Linbing WANG

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1299-1315 doi: 10.1007/s11709-020-0712-6

Abstract: This study used an artificial neural network (ANN) model to describe the tensile behavior of HFRC.

Keywords： artificial neural network     hybrid fiber reinforced concrete     tensile behavior     sensitivity analysis     stress-strain    

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Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networksand an adaptive-network-based fuzzy inference system

J. Sargolzaei, A. Hedayati Moghaddam

Frontiers of Chemical Science and Engineering 2013, Volume 7, Issue 3,   Pages 357-365 doi: 10.1007/s11705-013-1336-3

Abstract: Several simulation systems including a back-propagation neural network (BPNN), a radial basis functionneural network (RBFNN) and an adaptive-network-based fuzzy inference system (ANFIS) were tested and

Keywords： oil recovery     artificial intelligence     extraction     neural networks     supercritical extraction    

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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 neuralnetwork.The convolutional neural network was designed after an experimental investigation of a wide number ofTo increase the network robustness compared to images in real-world situations, different image configurationsmodel, with the highest validation accuracy of approximately 94%, was selected as the most suitable network

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

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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 minimumtime consumption, in which data from physical and numerical modeling can be used for network learningIn 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 neuralnetwork

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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Title Author Date Type Operation

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

Journal Article

Prediction of bed load sediments using different artificial neural network models

Reza ASHEGHI, Seyed Abbas HOSSEINI

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

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

Journal Article

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

Sadegh REZAEI,Asskar Janalizadeh CHOOBBASTI

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

Assessing artificial neural network performance for predicting interlayer conditions and layer modulus

Lingyun YOU, Kezhen YAN, Nengyuan LIU

Journal Article

Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

Journal Article

An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete

Fangyu LIU, Wenqi DING, Yafei QIAO, Linbing WANG

Journal Article

Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networksand an adaptive-network-based fuzzy inference system

J. Sargolzaei, A. Hedayati Moghaddam

Journal Article

Automated classification of civil structure defects based on convolutional neural network

Pierclaudio SAVINO, Francesco TONDOLO

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 neuralnetwork

P. ZAKIAN

Journal Article