Resource Type

Journal Article 540

Conference Videos 20

Conference Information 2

Year

2024 2

2023 59

2022 79

2021 60

2020 65

2019 50

2018 39

2017 23

2016 25

2015 23

2014 12

2013 10

2012 7

2011 4

2010 7

2009 6

2008 12

2007 16

2006 7

2005 9

open ︾

Keywords

Artificial intelligence 37

artificial intelligence 36

neural network 32

artificial neural network 21

Deep learning 14

Neural network 11

Machine learning 9

convolutional neural network 7

genetic algorithm 7

neural networks 7

artificial neural network (ANN) 6

deep learning 6

machine learning 6

optimization 5

BP neural network 4

big data 4

ANN 3

Artificial neural network 3

Industry 4.0 3

open ︾

Search scope:

排序： Display mode:

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    

HTML PDF Collect

Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks

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 andThe performance of these networks was evaluated using the coefficient of determination ( ) and the mean

Keywords： oil recovery     artificial intelligence     extraction     neural networks     supercritical extraction    

HTML PDF Collect

The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 3,   Pages 609-622 doi: 10.1007/s11709-020-0623-6

Abstract: This paper discusses the adoption of Artificial Intelligence-based techniques to estimate seismic damagedamage grades obtained resorting to a classic damage formulation and an innovative approach based on ArtificialNeural Networks (ANNs).

Keywords： Artificial Neural Networks     seismic vulnerability     masonry buildings     damage estimation     vulnerability curves    

HTML PDF Collect

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    

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

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    

HTML PDF Collect

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    

HTML PDF Collect

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

Day-ahead electricity price forecasting using back propagation neural networks and weighted least square

S. Surender REDDY,Chan-Mook JUNG,Ko Jun SEOG

Frontiers in Energy 2016, Volume 10, Issue 1,   Pages 105-113 doi: 10.1007/s11708-016-0393-y

Abstract: This paper proposes the day-ahead electricity price forecasting using the artificial neural networks

Keywords： day-ahead electricity markets     price forecasting     load forecasting     artificial neural networks     load serving    

HTML PDF Collect

combustion and emission characteristics of diesel-thermal cracked cashew nut shell liquid blends using artificialneural network

Arunachalam VELMURUGAN,Marimuthu LOGANATHAN,E. James GUNASEKARAN

Frontiers in Energy 2016, Volume 10, Issue 1,   Pages 114-124 doi: 10.1007/s11708-016-0394-x

Abstract: This paper explores the use of artificial neural networks (ANN) to predict performance, combustion and

Keywords： cashew nut shell liquid (CNSL)     artificial neural networks (ANN)     thermal cracking     mean square error (MSE    

HTML PDF Collect

immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neuralnetworks for plug-in hybrid electric vehicles fuel economy

Ahmad MOZAFFARI,Mahyar VAJEDI,Nasser L. AZAD

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 2,   Pages 154-167 doi: 10.1007/s11465-015-0336-z

Abstract: optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificialThe objective function of the optimizer is derived from a computationally efficient artificial neural

Keywords： trip information preview     intelligent transportation     state-of-charge trajectory builder     immune systems     artificialneural network    

HTML PDF Collect

Estimation of optimum design of structural systems via machine learning

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 6,   Pages 1441-1452 doi: 10.1007/s11709-021-0774-0

Abstract: The other used an estimation application that was done via artificial neural networks (ANN) to find out

Keywords： optimization     metaheuristic algorithms     harmony search     structural designs     machine learning     artificialneural networks    

HTML PDF Collect

Penetration Depth of Projectiles Into Concrete Using Artificial Neural Network

Li Jianguang,Li Yongchi,Wang Yulan

Strategic Study of CAE 2007, Volume 9, Issue 8,   Pages 77-81

Abstract: . , and output of penetration depth is established by dimensional analysis and theory of artificial neuralnetworks for problem of penetration depth of projectiles into concrete. Moreover,  a satisfied output about penetration depth from RBF neural network is gotten by

Keywords： neural networks     dimensional analysis     penetration depth of projectiles into concrete     nonlinear mappingrelation     RBF neural networks    

HTML PDF Collect

Novel interpretable mechanism of neural networks based on network decoupling method

Frontiers of Engineering Management 2021, Volume 8, Issue 4,   Pages 572-581 doi: 10.1007/s42524-021-0169-x

Abstract: The lack of interpretability of the neural network algorithm has become the bottleneck of its wide applicationthat a simple linear mapping relationship exists between network structure and network behavior in the neuralnew interpretation mechanism provides not only the potential mathematical calculation principle of neuralhuman brain or animal activities, which can further expand and enrich the interpretable mechanism of artificialneural network in the future.

Keywords： neural networks     interpretability     dynamical behavior     network decouple    

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.

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

HTML PDF Collect

Title Author Date Type Operation

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

T. Chandra Sekhara REDDY

Journal Article

Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks

J. Sargolzaei, A. Hedayati Moghaddam

Journal Article

The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

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

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

Yasser SHARIFI,Sajjad TOHIDI

Journal Article

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

Journal Article

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

Journal Article

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

Sadegh REZAEI,Asskar Janalizadeh CHOOBBASTI

Journal Article

Day-ahead electricity price forecasting using back propagation neural networks and weighted least square

S. Surender REDDY,Chan-Mook JUNG,Ko Jun SEOG

Journal Article

combustion and emission characteristics of diesel-thermal cracked cashew nut shell liquid blends using artificialneural network

Arunachalam VELMURUGAN,Marimuthu LOGANATHAN,E. James GUNASEKARAN

Journal Article

immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neuralnetworks for plug-in hybrid electric vehicles fuel economy

Ahmad MOZAFFARI,Mahyar VAJEDI,Nasser L. AZAD

Journal Article

Estimation of optimum design of structural systems via machine learning

Journal Article

Penetration Depth of Projectiles Into Concrete Using Artificial Neural Network

Li Jianguang,Li Yongchi,Wang Yulan

Journal Article

Novel interpretable mechanism of neural networks based on network decoupling method

Journal Article

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

Journal Article