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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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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    

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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 andThe ANN was used to predict eight different engine-output responses, namely brake thermal efficiencyThe ANN results show that there is a good correlation between the ANN predicted values and the experimentalThus the developed ANN model is fairly powerful for predicting the performance, combustion and exhaust

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

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Research on Forecasting Model of Seismic Disaster Risk Based on GA-ANN

Liu Mingguang,Guo Zhanglin

Strategic Study of CAE 2006, Volume 8, Issue 3,   Pages 83-86

Abstract: disasters risk at first, and then, the forecasting model of seismic risk based on the genetic algorithm and artificialneural networks is proposed.

Keywords： seismic disaster     factors of risk     artificial neural networks     genetic algorithm     forecasting    

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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 ofThe obtained experimental data are trained using ANN which consists of 4 input parameters like PercentageThe predicted values obtained using ANN show a good correlation between the experimental data.It is concluded that ANN is a highly powerful tool suitable for assessing the strength characteristics

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

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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    

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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).Finally, a computer routine that uses the ANN as an approximation function is developed and applied toIn general terms, the ANN developed in this study allowed to obtain much better approximations than those

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

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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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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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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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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    

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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    

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Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificialneural network

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 8,   Pages 976-989 doi: 10.1007/s11709-022-0840-2

Abstract: depth in steel beam structures based on vibration analysis using the Finite Element Method (FEM) and ArtificialNeural Network (ANN) combined with Butterfly Optimization Algorithm (BOA).ANN is quite successful in such identification issues, but it has some limitations, such as reductionThis paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN).

Keywords： damage prediction     ANN     BOA     FEM     experimental modal analysis    

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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    

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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 basedbackpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANNResults 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    

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Title Author Date Type Operation

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

Sadegh REZAEI,Asskar Janalizadeh CHOOBBASTI

Journal Article

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

Yasser SHARIFI,Sajjad TOHIDI

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

Research on Forecasting Model of Seismic Disaster Risk Based on GA-ANN

Liu Mingguang,Guo Zhanglin

Journal Article

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

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

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

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

Yaolin LIN, Wei YANG

Journal Article

Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificialneural network

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

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

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

Journal Article