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Predicting the capacity of perfobond rib shear connector using an ANN model and GSA method

Guorui SUN; Jun SHI; Yuang DENG

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 10,   Pages 1233-1248 doi: 10.1007/s11709-022-0878-1

Abstract: Due to recent advances in the field of artificial neural networks (ANN) and the global sensitivity analysisperformance of perfobond rib shear connectors (PRSCs) is predicted based on the back propagation (BP) ANNThe results predicted by the ANN models and empirical equations were compared, and the factors affectingThe results show that the use of ANN model optimization by GA method has fewer errors compared to the

Keywords: perfobond rib shear connector     shear strength     ANN model     global sensitivity analysis    

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    

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: perform the unit commitment (UC) based on frequency prediction by using artificial neural network (ANN

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

Food Safety and Health

Martin Cole, Mary Ann Augustin

Engineering 2020, Volume 6, Issue 4,   Pages 391-392 doi: 10.1016/j.eng.2020.01.010

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: energy     point absorbers     heaving body     non-floating object     heave response ratio     artificial neural network (ANN    

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: optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANNInspired 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 ANNRock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-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     artificial neural network     tunneling energy efficiency    

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:

The key of forecasting transmutation of wall rock correctly is to construct the reasonable mathematics model of time-distance curve from measuring data when distorting, which is hard to describe accurately with traditional method of recursive analysis. According to the characteristics of feed forward neural network of radial basis function to construct the forecast model of deformation of wall rock in multi-arch tunnel and cllso uses Matlab tool to solve the optimal problem. The engineering case at the end of this paper validates the method. For its fast solving the problem,more optimal results,and better forecasting effects,this method shows its advantages and feasibility.

Keywords: deformation of wall rock     deformation forecast     radial basis function (RBF)     artificial neural network (ANN    

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:

This paper discerns and analyzes some main factors influencing seismic disasters risk at first, and then, the forecasting model of seismic risk based on the genetic algorithm and artificial neural networks is proposed. The case shows that the model is practical and effective. A kind of forecasting method of seismic disaster risk is presented to decision making departments.

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

Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II

Ravindra Nath YADAV, Vinod YADAVA, G.K. SINGH

Frontiers of Mechanical Engineering 2013, Volume 8, Issue 3,   Pages 319-332 doi: 10.1007/s11465-013-0269-3

Abstract: The combined approach of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm-IIElectrical Discharge Diamond face Grinding (EDDFG) have been studied using a hybrid methodology of ANN-NSGA-IIIn this study, ANN has been used for modeling while NSGA-II is used to optimize the control parametersThe results have shown that the developed ANN model is capable to predict the output responses withinIt has also been found that hybrid approach of ANN-NSGA-II gives a set of optimal solutions for getting

Keywords: machining processes (HMPs)     electrical discharge diamond grinding (EDDG)     artificial neural network (ANN    

The Power of an Idea: The International Impacts of the Grand Challenges for Engineering Views & Comments

., Dame Ann Dowling, Ji Zhou

Engineering 2016, Volume 2, Issue 1,   Pages 4-7 doi: 10.1016/J.ENG.2016.01.025

Delivering food safety

Kaye BASFORD,Richard BENNETT,Joanne DALY,Mary Ann AUGUSTIN,Snow BARLOW,Tony GREGSON,Alice LEE,Deli CHEN

Frontiers of Agricultural Science and Engineering 2017, Volume 4, Issue 1,   Pages 1-4 doi: 10.15302/J-FASE-2016123

Abstract: A delegation from the Australian Academy of Technological Sciences and Engineering traveled to Beijing in April 2016 to jointly run a workshop on technology advances in food safety with the Chinese Academy of Engineering. This brief summary from the Australian delegation identifies the pyramid of inter- locking issues which must be addressed to deliver food safety. Systems and technology provide the necessary base, on which culture and then trust can be built to facilitate the delivery of food safety now and in the future.

Keywords: culture     food safety     systems     technology     trust    

Processing and analysis of data from microwave humidity sounder onboard FY-3A satellite

He Jieying,Zhang Shengwei

Strategic Study of CAE 2013, Volume 15, Issue 10,   Pages 47-53

Abstract: The paper constructs an inversion model using artificial neural network (ANN) algorithm, and makes comparison

Keywords: MWHS     FY-3A     ANN     water vapor density    

Nitrogen removal efficiencies and microbial communities in full-scale IFAS and MBBR municipal wastewater treatment plants at high COD:N ratio

Naluporn Kangwannarakul, Pongsak (Lek) Noophan, Tamao Kasahara, Akihiko Terada, Junko Munakata-Marr, Linda Ann

Frontiers of Environmental Science & Engineering 2020, Volume 14, Issue 6, doi: 10.1007/s11783-020-1374-2

Abstract: Abstract • Two IFAS and two MBBR full-scale systems (high COD:N ratio 8:1) were characterized. • High specific surface area carriers grew and retained slow-growing nitrifiers. • High TN removal is related to high SRT and low DO concentration in anoxic tanks. The relative locations of AOB, NOB, and DNB were examined for three different kinds of carriers in two types of hybrid biofilm process configurations: integrated fixed-film activated sludge (IFAS) and moving bed biofilm reactor (MBBR) processes. IFAS water resource recovery facilities (WRRFs) used AnodkalnessTM K1 carriers (KC) at Broomfield, Colorado, USA and polypropylene resin carriers (RC) at Fukuoka, Japan, while MBBR WRRFs used KC carriers at South Adams County, Colorado, USA and sponge carriers (SC) at Saga, Japan. Influent COD to N ratios ranged from 8:1 to 15:1. The COD and BOD removal efficiencies were high (96%–98%); NH4+-N and TN removal efficiencies were more varied at 72%–98% and 64%–77%, respectively. The extent of TN removal was higher at high SRT, high COD:N ratio and low DO concentration in the anoxic tank. In IFAS, RC with high specific surface area (SSA) maintained higher AOB population than KC. Sponge carriers with high SSA maintained higher overall bacteria population than KC in MBBR systems. However, the DNB were not more abundant in high SSA carriers. The diversity of AOB, NOB, and DNB was fairly similar in different carriers. Nitrosomonas sp. dominated over Nitrosospira sp. while denitrifying bacteria included Rhodobacter sp., Sulfuritalea sp., Rubrivivax sp., Paracoccus sp., and Pseudomonas sp. The results from this work suggest that high SRT, high COD:N ratio, low DO concentration in anoxic tanks, and carriers with greater surface area may be recommended for high COD, BOD and TN removal in WRRFs with IFAS and MBBR systems.

Keywords: IFAS     MBBR     AnodkalnessTM K1 carrier     Polypropylene resin carrier     Sponge carrier    

Optimization of electrochemically synthesized Cu

Kasra Pirzadeh, Ali Asghar Ghoreyshi, Mostafa Rahimnejad, Maedeh Mohammadi

Frontiers of Chemical Science and Engineering 2020, Volume 14, Issue 2,   Pages 233-247 doi: 10.1007/s11705-019-1893-1

Abstract: Cu (BTC) , a common type of metal organic framework (MOF), was synthesized through electrochemical route for CO capture and its separation from N . Taguchi method was employed for optimization of key parameters affecting the synthesis of Cu (BTC) . The results indicated that the optimum synthesis conditions with the highest CO selectivity can be obtained using 1 g of ligand, applied voltage of 25 V, synthesis time of 2 h, and electrode length of 3 cm. The single gas sorption capacity of the synthetized microstructure Cu (BTC) for CO (at 298 K and 1 bar) was a considerable value of 4.40 mmol·g . The isosteric heat of adsorption of both gases was calculated by inserting temperature-dependent form of Langmuir isotherm model in the Clausius-Clapeyron equation. The adsorption of CO /N binary mixture with a concentration ratio of 15/85 vol-% was also studied experimentally and the result was in a good agreement with the predicted value of IAST method. Moreover, Cu (BTC) showed no considerable loss in CO adsorption after six sequential cycles. In addition, artificial neural networks (ANNs) were also applied to predict the separation behavior of CO /N mixture by MOFs and the results revealed that ANNs could serve as an appropriate tool to predict the adsorptive selectivity of the binary gas mixture in the absence of experimental data.

Keywords: sub>3(BTC)2 electrochemical synthesis     CO2 adsorption     Taguchi optimization     ANN    

Liquefaction assessment using microtremor measurement, conventional method and artificial neural network (Case study: Babol, Iran)

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    

Title Author Date Type Operation

Predicting the capacity of perfobond rib shear connector using an ANN model and GSA method

Guorui SUN; Jun SHI; Yuang DENG

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

Food Safety and Health

Martin Cole, Mary Ann Augustin

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

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

Journal Article

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

Xiao Zhiwang,Zhong Denghua

Journal Article

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

Liu Mingguang,Guo Zhanglin

Journal Article

Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II

Ravindra Nath YADAV, Vinod YADAVA, G.K. SINGH

Journal Article

The Power of an Idea: The International Impacts of the Grand Challenges for Engineering

., Dame Ann Dowling, Ji Zhou

Journal Article

Delivering food safety

Kaye BASFORD,Richard BENNETT,Joanne DALY,Mary Ann AUGUSTIN,Snow BARLOW,Tony GREGSON,Alice LEE,Deli CHEN

Journal Article

Processing and analysis of data from microwave humidity sounder onboard FY-3A satellite

He Jieying,Zhang Shengwei

Journal Article

Nitrogen removal efficiencies and microbial communities in full-scale IFAS and MBBR municipal wastewater treatment plants at high COD:N ratio

Naluporn Kangwannarakul, Pongsak (Lek) Noophan, Tamao Kasahara, Akihiko Terada, Junko Munakata-Marr, Linda Ann

Journal Article

Optimization of electrochemically synthesized Cu

Kasra Pirzadeh, Ali Asghar Ghoreyshi, Mostafa Rahimnejad, Maedeh Mohammadi

Journal Article

Liquefaction assessment using microtremor measurement, conventional method and artificial neural network (Case study: Babol, Iran)

Sadegh REZAEI,Asskar Janalizadeh CHOOBBASTI

Journal Article