Resource Type

Journal Article 1185

Conference Videos 13

Year

2024 47

2023 111

2022 106

2021 84

2020 79

2019 52

2018 47

2017 55

2016 50

2015 56

2014 45

2013 44

2012 43

2011 38

2010 52

2009 58

2008 41

2007 42

2006 28

2005 29

open ︾

Keywords

prediction 20

numerical simulation 18

model test 16

model 15

mathematical model 14

Machine learning 13

Deep learning 10

kinetic model 10

machine learning 10

COVID-19 8

sensitivity analysis 8

optimization 7

artificial intelligence 6

artificial neural network 6

neural network 6

numerical model 6

Artificial intelligence 5

constitutive model 5

finite element method 5

open ︾

Search scope:

排序： Display mode:

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Frontiers of Medicine 2022, Volume 16, Issue 3,   Pages 496-506 doi: 10.1007/s11684-021-0828-7

Abstract: In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fractureA total of 147 raw input features are considered in our model.The presented model is compared with several benchmarks based on various metrics to prove its effectiveness

Keywords： XGBoost     deep neural network     healthcare     risk prediction    

HTML PDF Collect

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 1,   Pages 72-82 doi: 10.1007/s11709-013-0185-y

Abstract: A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibilityThis paper examines the potential of SVM model in prediction of liquefaction using actual field coneUsing cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefactionhorizontal acceleration ), for prediction of liquefaction.model predicts with accuracy of 89%.

Keywords： earthquake     cone penetration test     liquefaction     support vector machine (SVM)     prediction    

HTML PDF Collect

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 7, doi: 10.1007/s11783-023-1688-y

Abstract:

● A novel VMD-IGOA-LSTM model has proposed for the prediction of

Keywords： Water quality prediction     Grasshopper optimization algorithm     Variational mode decomposition     Long short-term    

HTML PDF Collect

A 3D sliced-soil–beam model for settlement prediction of tunnelling using the pipe roofing method in

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 12,   Pages 1934-1948 doi: 10.1007/s11709-023-0038-2

Abstract: However, the pipe roofing method has rarely been applied in soft ground, where the prediction and controlThis study proposes a sliced-soil–beam (SSB) model to predict the settlement of ground due to tunnellingThe model comprises a sliced-soil module based on the virtual work principle and a beam module basedThe model was verified in a case study conducted in Shanghai, China, in which it provided the efficientand accurate prediction of settlement.

Keywords： pipe roofing method     soft ground     numerical simulation     settlement prediction     simplified calculation    

HTML PDF Collect

A hybrid spatial-temporal deep learning prediction model of industrial methanol-to-olefins process

Frontiers of Chemical Science and Engineering 2024, Volume 18, Issue 4, doi: 10.1007/s11705-024-2403-7

Abstract: The accurate prediction of process variables can yield significant benefits for advanced process controlIn this paper, we propose a novel hybrid spatial-temporal deep learning prediction model to address theseMeanwhile, a multi-graph convolutional network is leveraged to model the spatial interactions.temporal and spatial features are fused as input into a fully connected neural network to complete the predictionnot only achieves superior prediction performance but also can reveal complex spatial-temporal relationships

Keywords： methanol-to-olefins     process variables prediction     spatial-temporal     self-attention mechanism     graph    

HTML PDF Collect

Improved analytical model for residual stress prediction in orthogonal cutting

Zhaoxu QI,Bin LI,Liangshan XIONG

Frontiers of Mechanical Engineering 2014, Volume 9, Issue 3,   Pages 249-256 doi: 10.1007/s11465-014-0310-1

Abstract: for residual stress prediction in orthogonal cutting.In application of the model, a problem of low precision of the surface residual stress prediction isThese shortages may directly lead to the low precision of the surface residual stress prediction.To eliminate these shortages and make the prediction more accurate, an improved model is proposed.Also, Jiann’s model and the improved model are simulated under the same conditions with cutting

Keywords： residual stress     analytical model     orthogonal cutting     cutting force     cutting temperature    

HTML PDF Collect

Fracture model for the prediction of the electrical percolation threshold in CNTs/Polymer composites

Yang SHEN, Pengfei HE, Xiaoying ZHUANG

Frontiers of Structural and Civil Engineering 2018, Volume 12, Issue 1,   Pages 125-136 doi: 10.1007/s11709-017-0396-8

Abstract: In this paper, we propose a 3D stochastic model to predict the percolation threshold and the effectiveWe consider the tunneling effect in our model so that the unrealistic interpenetration can be avoided

Keywords： electrical percolation     CNTs/Polymer composites     fracture model     electric conductivity     tunnelling effects    

HTML PDF Collect

Enhanced wear prediction of tunnel boring machine disc cutters for accurate remaining useful life estimationusing a hybrid model

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 4,   Pages 642-662 doi: 10.1007/s11709-024-1058-2

Abstract: In tunnel construction with tunnel boring machines (TBMs), accurate prediction of the remaining usefulwear prediction of TBM disc cutters and enable accurate RUL estimation., showcasing superior prediction accuracy compared to single-mechanism models.the improved fundamental model, leading to a high-performance wear prediction model.impacts of geological conditions on prediction accuracy.

Keywords： tunnel boring machine     disc cutter     wear prediction     remaining useful life     field data     hybrid model    

HTML PDF Collect

Performance prediction of switched reluctance generator with time average and small signal models

Jyoti KOUJALAGI, B. UMAMAHESWARI, R. ARUMUGAM

Frontiers in Energy 2013, Volume 7, Issue 1,   Pages 56-68 doi: 10.1007/s11708-012-0216-8

Abstract: This paper presents the complete mathematical model and predicts the performance of switched reluctanceThe complete mathematical model is developed in three stages.First, a switching model is developed based on quasi-linear inductance profile.Finally, to track control voltage and current wave shapes, a small signal model is designed.The effectiveness of the complete multilevel model combining electrical machine, power converter, load

Keywords： generator     reluctance     switching model     small signal model     time average model    

HTML PDF Collect

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

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: This paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN).

Keywords： damage prediction     ANN     BOA     FEM     experimental modal analysis    

HTML PDF Collect

Prediction method of foundation vibration responses induced by impact loading using modified andersonmodel

Fang Bo

Strategic Study of CAE 2014, Volume 16, Issue 11,   Pages 96-102

Abstract:

A synthetic method, which combines theoretical model and field measurementThe Anderson model was modified and verified by the data measured in field hammer impact tests.Then the impact induced vibration was predicted using the modified Anderson model.Finally, the prediction results were compared with the measured results.The results indicates that the prediction results approximately approach to the measured results.

Keywords： prediction method     impact loading     vibration effects     anderson model    

HTML PDF Collect

Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markovmodel

Frontiers of Engineering Management doi: 10.1007/s42524-024-0082-1

Abstract: This study proposes a method, the online hidden Markov model (OHMM), which combines online learning withthe hidden Markov model to estimate geological risks.The OHMM outperforms traditional methods, including the hidden Markov model, long short-term memory networkThis research advances geological risk prediction models by offering an online updating capability forIt enables early-stage risk prediction and provides long-term forecasts with minimal historical data

Keywords： geological risk prediction     machine learning     online learning     hidden Markov model     borehole logging    

HTML PDF Collect

A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0688-0

Abstract: In this study, we develop a predictive model of the dimensional accuracy for precision milling of thin-walledBased on the experimental data collected during the milling experiments, the proposed model proved toThe average classification accuracy obtained using the proposed deep learning model was 9.55% higherHence, the proposed hybrid model provides an efficient way of fusing different sources of process dataand can be adopted for prediction of the machining quality in noisy environments.

Keywords： precision milling     dimensional accuracy     cutting force     convolutional neural networks     coherent noise    

HTML PDF Collect

Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neural

Frontiers in Energy doi: 10.1007/s11708-023-0906-4

Abstract: The prediction of the remaining useful life (RUL) of lithium batteries not only provides a referenceIn order to improve the prediction accuracy of the RUL of LIBs, a two-phase RUL early prediction method, the features related to the capacity degradation of LIBs are utilized to train the neural network modelconsidered to have a similar degradation pattern, which is used to determine the initial Dual Exponential ModelExperiments show that the method does not need human intervention and has high prediction accuracy.

Keywords： lithium-ion batteries     RUL prediction     double exponential model     neural network     Gaussian process regression    

HTML PDF Collect

Regional seismic-damage prediction of buildings under mainshock–aftershock sequence

Xinzheng LU, Qingle CHENG, Zhen XU, Chen XIONG

Frontiers of Engineering Management 2021, Volume 8, Issue 1,   Pages 122-134 doi: 10.1007/s42524-019-0072-x

Abstract: Thus, the accurate and efficient prediction of aftershock-induced damage to buildings on a regional scale

Keywords： regional seismic damage prediction     city-scale nonlinear time-history analysis     mainshock–aftershock sequence     multiple degree-of-freedom (MDOF) model     2014 Ludian earthquake    

HTML PDF Collect

Title Author Date Type Operation

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Journal Article

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Journal Article

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

Journal Article

A 3D sliced-soil–beam model for settlement prediction of tunnelling using the pipe roofing method in

Journal Article

A hybrid spatial-temporal deep learning prediction model of industrial methanol-to-olefins process

Journal Article

Improved analytical model for residual stress prediction in orthogonal cutting

Zhaoxu QI,Bin LI,Liangshan XIONG

Journal Article

Fracture model for the prediction of the electrical percolation threshold in CNTs/Polymer composites

Yang SHEN, Pengfei HE, Xiaoying ZHUANG

Journal Article

Enhanced wear prediction of tunnel boring machine disc cutters for accurate remaining useful life estimationusing a hybrid model

Journal Article

Performance prediction of switched reluctance generator with time average and small signal models

Jyoti KOUJALAGI, B. UMAMAHESWARI, R. ARUMUGAM

Journal Article

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

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

Journal Article

Prediction method of foundation vibration responses induced by impact loading using modified andersonmodel

Fang Bo

Journal Article

Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markovmodel

Journal Article

A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

Journal Article

Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neural

Journal Article

Regional seismic-damage prediction of buildings under mainshock–aftershock sequence

Xinzheng LU, Qingle CHENG, Zhen XU, Chen XIONG

Journal Article