Resource Type

Journal Article 742

Conference Videos 32

Year

2024 3

2023 82

2022 93

2021 86

2020 84

2019 56

2018 52

2017 46

2016 27

2015 30

2014 20

2013 15

2012 16

2011 9

2010 7

2009 9

2008 19

2007 19

2006 12

2005 17

open ︾

Keywords

Artificial intelligence 37

artificial intelligence 36

neural network 32

artificial neural network 21

Deep learning 16

Neural network 11

Machine learning 10

network 10

optimization 10

genetic algorithm 9

convolutional neural network 7

machine learning 7

neural networks 7

artificial neural network (ANN) 6

deep learning 6

ANN 5

Intelligent manufacturing 4

6G 3

BP neural network 3

open ︾

Search scope:

排序： Display mode:

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    

HTML PDF Collect

QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 1,   Pages 25-36 doi: 10.1007/s11709-022-0908-z

Abstract: Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neuralnetwork (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein.Inspired by the regularization technique, a custom artificial neural network (ANN) loss function basedTBM performance indicators is developed in the form of a penalty function to adjust the output of the networkResults show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases

Keywords： tunnel boring machine     control parameter optimization     quantum particle swarm optimization     artificialneural network     tunneling energy efficiency    

HTML PDF Collect

An ANN-exhaustive-listing method for optimization of multiple building shapes and envelope properties

Yaolin LIN, Wei YANG

Frontiers in Energy 2021, Volume 15, Issue 2,   Pages 550-563 doi: 10.1007/s11708-019-0607-1

Abstract: This paper attempts to develop an innovative ANN (artificial neural network)-exhaustive-listing methodtreated separately to achieve sufficient accuracy of prediction of thermal performance and that the ANN

Keywords： ANN (artificial neural network)     exhaustive-listing     building shape     optimization     thermal load     thermal comfort    

HTML PDF Collect

Optimal generation scheduling in power system using frequency prediction through ANN under ABT environment

Simarjit KAUR, Yajvender Pal VERMA, Sunil AGRAWAL

Frontiers in Energy 2013, Volume 7, Issue 4,   Pages 468-478 doi: 10.1007/s11708-013-0282-6

Abstract: the GS problem is solved to perform the unit commitment (UC) based on frequency prediction by using artificialneural network (ANN) with the objective to minimize the overall system cost of the state utility.

Keywords： artificial neural network (ANN)     frequency prediction     availability-based tariff (ABT)     generation scheduling    

HTML PDF Collect

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 The calculated subsidence is derived from the ANN objective function which is defined as the resultingThe ANN was found to have minimized the bone surface VMS, thereby optimizing the ALIF cage given theTherefore, the Taguchi-FEA-ANN approach can serve as an effective procedure for designing a spinal fusion

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

HTML PDF Collect

Lateral-torsional buckling capacity assessment of web opening steel girders by artificial neural networks

Yasser SHARIFI,Sajjad TOHIDI

Frontiers of Structural and Civil Engineering 2014, Volume 8, Issue 2,   Pages 167-177 doi: 10.1007/s11709-014-0236-z

Abstract: Artificial neural network (ANN) approach has been also employed to derive empirical formulae for predicting

Keywords： steel I-beams     lateral-torsional buckling     finite element (FE) method     artificial neural network (ANN) approach    

HTML PDF Collect

Experimental investigation and ANN modeling on improved performance of an innovative method of using

Srinivasan CHANDRASEKARAN, Arunachalam AMARKARTHIK, Karuppan SIVAKUMAR, Dhanasekaran SELVAMUTHUKUMARAN, Shaji SIDNEY

Frontiers in Energy 2013, Volume 7, Issue 3,   Pages 279-287 doi: 10.1007/s11708-013-0268-4

Abstract: The device was modeled in artificial neural network (ANN), the heave response for various parametersIt was found that the ANN model could predict the heave response with an accuracy of 99%.

Keywords： ocean wave energy     point absorbers     heaving body     non-floating object     heave response ratio     artificial neuralnetwork (ANN)    

HTML PDF Collect

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.ANN models are prepared with Levenberg–Marquardt (LM) algorithm for load sharing and interaction factorsThe factor of safety (FS) of PRF is also estimated using the proposed NMR and ANN models, which can be

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

HTML PDF Collect

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 offor this river by conducted comparison between measured and predicted values was approved where the ANNAlthough the ANN models predicted compatible outputs but the RBF with 79% correct classification ratecorresponding to 0.191 network error was outperform than others.

Keywords： bed load prediction     artificial neural network     modeling     empirical equations    

HTML PDF Collect

RBF-ANN-Based forecast method of transmutation of wall rock on multi-arch tunne

Xiao Zhiwang,Zhong Denghua

Strategic Study of CAE 2008, Volume 10, Issue 7,   Pages 77-81

Abstract: According to the characteristics of feed forward neural network of radial basis function to construct

Keywords： multi-arch tunnel     deformation of wall rock     deformation forecast     radial basis function (RBF)     artificialneural network (ANN)    

HTML PDF Collect

Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 2,   Pages 133-136 doi: 10.1007/s11709-013-0202-1

Abstract: A comparative study has been carried out between the developed GPR and Artificial Neural Network (ANN

Keywords： unsaturated soil     effective stress parameter     Gaussian process regression (GPR)     artificial neural network(ANN)     variance    

HTML PDF Collect

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, andFinally, these three models are compared with each other and resulted in the fact that ANN and ANFIS

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

HTML PDF Collect

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)The results confirm the ability of ANN and SVM models in prediction processes.

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

HTML PDF Collect

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    

HTML PDF Collect

Comparison of modeling methods for wind power prediction: a critical study

Rashmi P. SHETTY, A. SATHYABHAMA, P. Srinivasa PAI

Frontiers in Energy 2020, Volume 14, Issue 2,   Pages 347-358 doi: 10.1007/s11708-018-0553-3

Abstract: developed based on wind power equation, concept of power curve, response surface methodology (RSM) and artificialneural network (ANN), and the results have been compared.based on the concept of power curve, the manufacturer’s power curve, and to develop RSM as well as ANNblade pitch angle, rotor speed and wind direction have been considered as input variables for RSM and ANNProper selection of input variables and capability of ANN to map input-output relationships have resulted

Keywords： power curve     method of least squares     cubic spline interpolation     response surface methodology     artificialneural network (ANN)    

HTML PDF Collect

Title Author Date Type Operation

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

Sadegh REZAEI,Asskar Janalizadeh CHOOBBASTI

Journal Article

QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

Journal Article

An ANN-exhaustive-listing method for optimization of multiple building shapes and envelope properties

Yaolin LIN, Wei YANG

Journal Article

Optimal generation scheduling in power system using frequency prediction through ANN under ABT environment

Simarjit KAUR, Yajvender Pal VERMA, Sunil AGRAWAL

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

Lateral-torsional buckling capacity assessment of web opening steel girders by artificial neural networks

Yasser SHARIFI,Sajjad TOHIDI

Journal Article

Experimental investigation and ANN modeling on improved performance of an innovative method of using

Srinivasan CHANDRASEKARAN, Arunachalam AMARKARTHIK, Karuppan SIVAKUMAR, Dhanasekaran SELVAMUTHUKUMARAN, Shaji SIDNEY

Journal Article

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

RBF-ANN-Based forecast method of transmutation of wall rock on multi-arch tunne

Xiao Zhiwang,Zhong Denghua

Journal Article

Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

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

Comparison of modeling methods for wind power prediction: a critical study

Rashmi P. SHETTY, A. SATHYABHAMA, P. Srinivasa PAI

Journal Article