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A constrained neural network model for soil liquefaction assessment with global applicability

Yifan ZHANG, Rui WANG, Jian-Min ZHANG, Jianhong ZHANG

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 5,   Pages 1066-1082 doi: 10.1007/s11709-020-0651-2

Abstract: A constrained back propagation neural network (C-BPNN) model for standard penetration test based soilThe C-BPNN model design procedure for liquefaction assessment is established by considering appropriatecorrection and fines content adjustment are shown to be able to improve the prediction success rate of the neuralnetwork model, and are thus adopted as constraints for the C-BPNN model.The C-BPNN liquefaction model is shown to have improved prediction accuracy and high global adaptability

Keywords： soil liquefaction assessment     case history dataset     constrained neural network model     existing knowledge    

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Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1285-1298 doi: 10.1007/s11709-020-0691-7

Abstract: The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methodsThis article intends to model the multiscale constitution using feedforward neural network (FNN) andrecurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict

Keywords： multiscale method     constitutive model     feedforward neural network     recurrent neural network    

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Pulse Transiently Chaotic Neural Network for Nonlinear Constrained Optimization

Li Ying,Cao Hongzhao,Hu Yunchang,Shang Xiuming

Strategic Study of CAE 2004, Volume 6, Issue 5,   Pages 45-48

Abstract:

Pulse Transiently Chaotic Neural Network (PTCNN) can find almost all optima including the part optimaand the global with its abundance dynamical characteristic, when is used in nonlinear non-constrained

Keywords： PTCNN     penalty function     nonlinear constrained optimization    

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Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

Frontiers in Energy doi: 10.1007/s11708-023-0891-7

Abstract: branches of AI, to lithium-ion battery state of health (SOH), focusing on the advantages and strengths of neuralnetwork (NN) methods in ML for lithium-ion battery SOH simulation and prediction.Reports so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has theby, first, utilizing more field data to play a more practical role in health feature screening and model

Keywords： machine learning     lithium-ion battery     state of health     neural network     artificial intelligence    

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Development of deep neural network model to predict the compressive strength of FRCM confined columns

Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 10,   Pages 1213-1232 doi: 10.1007/s11709-022-0880-7

Abstract: The present study describes a reliability analysis of the strength model for predicting concrete columnsinfluence with Fabric-Reinforced Cementitious Matrix (FRCM). through both physical models and Deep NeuralNetwork model (artificial neural network (ANN) with double and triple hidden layers).The database of 330 samples collected for the training model contains many important parameters, i.e.The ANN model with double hidden layers (APDL-1) was shown to be the best to predict the compressive

Keywords： FRCM     deep neural networks     confinement effect     strength model     confined concrete    

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Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regressionand M5 model tree

Tanvi SINGH, Mahesh PAL, V. K. ARORA

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 3,   Pages 674-685 doi: 10.1007/s11709-018-0505-3

Abstract: M5 model tree, random forest regression (RF) and neural network (NN) based modelling approaches wereM5 model tree provides simple linear relation which can be used for the prediction of oblique load forModel developed using RF regression approach with smooth pile group data was found to be in good agreement

Keywords： batter piles     oblique load test     neural network     M5 model tree     random forest regression     ANOVA    

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A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 2, doi: 10.1007/s11465-022-0736-9

Abstract: First, an MSR model is developed to learn MSRs automatically and further obtain fault recognition resultsSecond, centrality measures are employed to analyze the MSR graphs learned by the MSR model, and fault

Keywords： fault recognition     fault localization     multi-sensor relations     network analysis     graph neural network    

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Optimal design of double-layer barrel vaults using genetic and pattern search algorithms and optimized neuralnetwork as surrogate model

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 3,   Pages 378-395 doi: 10.1007/s11709-022-0899-9

Abstract: This paper presents a combined method based on optimized neural networks and optimization algorithmsThe main idea is to utilize an optimized artificial neural network (OANN) as a surrogate model to reduce

Keywords： optimization     surrogate models     artificial neural network     SAP2000     genetic algorithm    

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An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete

Fangyu LIU, Wenqi DING, Yafei QIAO, Linbing WANG

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1299-1315 doi: 10.1007/s11709-020-0712-6

Abstract: This study used an artificial neural network (ANN) model to describe the tensile behavior of HFRC.This ANN model can describe well the tensile stress-strain curve of HFRC with the consideration of 23In the model, three methods to process output features (no-processed, mid-processed, and processed) areMoreover, a traditional equation-based model is also established and compared with the ANN model.The results show that the ANN model has a better prediction than the equation-based model in terms of

Keywords： artificial neural network     hybrid fiber reinforced concrete     tensile behavior     sensitivity analysis     stress-strain    

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Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neuralnetwork and Gaussian process regression

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

Abstract: improve the prediction accuracy of the RUL of LIBs, a two-phase RUL early prediction method combining neuralnetwork and Gaussian process regression (GPR) is proposed.the initial phase, the features related to the capacity degradation of LIBs are utilized to train the neuralnetwork model, which is used to predict the initial cycle lifetime of 124 LIBs.considered to have a similar degradation pattern, which is used to determine the initial Dual Exponential Model

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

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A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 2, doi: 10.1007/s11783-023-1622-3

Abstract:

● A novel deep learning framework for short-term water demand forecasting.

Keywords： Short-term water demand forecasting     Long-short term memory neural network     Convolutional Neural Network    

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Fuzzy force control of constrained robot manipulators based on impedance model in an unknown environment

LIU Hongyi, WANG Fei, WANG Lei

Frontiers of Mechanical Engineering 2007, Volume 2, Issue 2,   Pages 168-174 doi: 10.1007/s11465-007-0028-4

Abstract: environment, a control strategy based on fuzzy prediction of the reference trajectory in the impedance modelThe experimental results show that the desired trajectory in the impedance model is predicted exactly

Keywords： predictive     tracking     corresponding     stiffness     algorithm    

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7

Abstract: Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis.However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search.used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based

Keywords： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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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 applicationnetwork.Result shows that a simple linear mapping relationship exists between network structure and network behaviorin the neural network with high-dimensional and nonlinear characteristics.which can further expand and enrich the interpretable mechanism of artificial neural network in the future

Keywords： neural networks     interpretability     dynamical behavior     network decouple    

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Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 214-223 doi: 10.1007/s11709-021-0800-2

Abstract: Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential

Keywords： concrete structure     GPR     damage classification     convolutional neural network     transfer learning    

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Title Author Date Type Operation

A constrained neural network model for soil liquefaction assessment with global applicability

Yifan ZHANG, Rui WANG, Jian-Min ZHANG, Jianhong ZHANG

Journal Article

Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Journal Article

Pulse Transiently Chaotic Neural Network for Nonlinear Constrained Optimization

Li Ying,Cao Hongzhao,Hu Yunchang,Shang Xiuming

Journal Article

Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

Journal Article

Development of deep neural network model to predict the compressive strength of FRCM confined columns

Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO

Journal Article

Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regressionand M5 model tree

Tanvi SINGH, Mahesh PAL, V. K. ARORA

Journal Article

A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

Journal Article

Optimal design of double-layer barrel vaults using genetic and pattern search algorithms and optimized neuralnetwork as surrogate model

Journal Article

An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete

Fangyu LIU, Wenqi DING, Yafei QIAO, Linbing WANG

Journal Article

Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neuralnetwork and Gaussian process regression

Journal Article

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Journal Article

Fuzzy force control of constrained robot manipulators based on impedance model in an unknown environment

LIU Hongyi, WANG Fei, WANG Lei

Journal Article

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Journal Article

Novel interpretable mechanism of neural networks based on network decoupling method

Journal Article

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Journal Article