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Journal Articles

Research on Fatigue Stress Reconstruction of Major Equipment Based on Neural Network

Bowen Yang, Chenxu Yang, Hua Li, Fan Yang, Jian Gao, Junzhou Huo

Publisher: ASME

J. Pressure Vessel Technol. October 2024, 146(5): 051302.

Paper No: PVT-23-1155

https://doi.org/10.1115/1.4065615

Published Online: June 17, 2024

View Article titled, Research on Fatigue Stress Reconstruction of Major Equipment Based on Neural Network

Topics: Stress, Tunnels, Boring machines, Artificial neural networks

Journal Articles

Artificial Neural Network Technique for Seismic Fragility Analysis of a Storage Tank Supported by Multi-Storey Frame

Gianluca Quinci, Fabrizio Paolacci, Hoang Nam Phan

Publisher: ASME

J. Pressure Vessel Technol. December 2023, 145(6): 061901.

Paper No: PVT-23-1074

https://doi.org/10.1115/1.4063242

Published Online: October 19, 2023

View Article titled, Artificial Neural Network Technique for Seismic Fragility Analysis of a Storage Tank Supported by Multi-Storey Frame

Topics: Artificial neural networks

Journal Articles

Condition Monitoring of Nuclear Equipment-Piping Systems Subjected to Normal Operating Loads Using Deep Neural Networks

Harleen Kaur Sandhu, Saran Srikanth Bodda, Serena Sauers, Abhinav Gupta

Publisher: ASME

J. Pressure Vessel Technol. August 2023, 145(4): 041901.

Paper No: PVT-23-1010

https://doi.org/10.1115/1.4062462

Published Online: May 25, 2023

View Article titled, Condition Monitoring of Nuclear Equipment-Piping Systems Subjected to Normal Operating Loads Using Deep Neural Networks

Topics: Artificial neural networks, Condition monitoring, Fatigue, Pipes, Piping systems, Pumps, Sensors, Stress, Vibration, Design

Journal Articles

Guided Wave Damage Location of Pressure Vessel Based on Optimized Explainable Convolutional Neural Network for Multivariate Time Series Classification Neural Network

Junxuan Zhang, Chaojie Hu, Jianjun Yan, Yue Hu, Yang Gao, Fuzhen Xuan

Publisher: ASME

J. Pressure Vessel Technol. August 2023, 145(4): 041302.

Paper No: PVT-22-1145

https://doi.org/10.1115/1.4062276

Published Online: April 21, 2023

View Article titled, Guided Wave Damage Location of Pressure Vessel Based on Optimized Explainable Convolutional Neural Network for Multivariate Time Series Classification Neural Network

Topics: Artificial neural networks, Damage, Pressure vessels, Time series, Waves, Signals, Optimization

Journal Articles

Convolutional Neural Network Based Defect Recognition Model for Phased Array Ultrasonic Testing Images of Electrofusion Joints

Yangji Tao, Jianfeng Shi, Weican Guo, Jinyang Zheng

Publisher: ASME

J. Pressure Vessel Technol. April 2023, 145(2): 024502.

Paper No: PVT-21-1178

https://doi.org/10.1115/1.4056836

Published Online: February 22, 2023

View Article titled, Convolutional Neural Network Based Defect Recognition Model for Phased Array Ultrasonic Testing Images of Electrofusion Joints

Topics: Artificial neural networks, Flaw detection, Inspection, Ultrasonic testing, Welding, Wire

Journal Articles

Damage Localization in Pressure Vessel by Guided Waves Based on Convolution Neural Network Approach

Chaojie Hu, Bin Yang, Jianjun Yan, Yanxun Xiang, Shaoping Zhou, Fu-Zhen Xuan

Publisher: ASME

J. Pressure Vessel Technol. December 2020, 142(6): 061601.

Paper No: PVT-19-1244

https://doi.org/10.1115/1.4047213

Published Online: June 12, 2020

View Article titled, Damage Localization in Pressure Vessel by Guided Waves Based on Convolution Neural Network Approach

Topics: Damage, Neural network models, Pressure vessels, Signals, Waves, Artificial neural networks, Errors

Journal Articles

Burst Pressure Prediction of Cylindrical Vessels Using Artificial Neural Network

Abolfazl Zolfaghari, Moein Izadi

Publisher: ASME

J. Pressure Vessel Technol. June 2020, 142(3): 031303.

Paper No: PVT-19-1142

https://doi.org/10.1115/1.4045729

Published Online: March 18, 2020

View Article titled, Burst Pressure Prediction of Cylindrical Vessels Using Artificial Neural Network

Topics: Artificial neural networks, Pressure, Pressure vessels, Vessels, Fracture (Materials), Design, Leakage

Journal Articles

Development of Bolted Flange Design Tool Based on Artificial Neural Network

Alper Yıldırım, Ahmet Arda Akay, Hasan Gülaşık, Demirkan Çoker, Ercan Gürses, Altan Kayran

Publisher: ASME

J. Pressure Vessel Technol. October 2019, 141(5): 051203.

Paper No: PVT-17-1186

https://doi.org/10.1115/1.4043915

Published Online: July 17, 2019

View Article titled, Development of Bolted Flange Design Tool Based on Artificial Neural Network

Topics: Artificial neural networks, Bolted flanges, Design, Finite element analysis, Flanges, Stress

Journal Articles

Development of an Optimized Neural Network for the Detection of Pipe Defects Using a Microwave Signal

Wissam M. Alobaidi, Entidhar A. Alkuam, Eric Sandgren

Publisher: ASME

J. Pressure Vessel Technol. August 2018, 140(4): 041501.

Paper No: PVT-17-1098

https://doi.org/10.1115/1.4040360

Published Online: June 18, 2018

View Article titled, Development of an Optimized Neural Network for the Detection of Pipe Defects Using a Microwave Signal

Topics: Artificial neural networks, Pipes, Microwaves, Signals, Topology, Optimization

Journal Articles

An Artificial Neural Network Model to Predict Material Characteristics From the Results of Miniature Disk Bending Tests

A. K. Ghosh, Vishnu Verma, G. Behera

Publisher: ASME

J. Pressure Vessel Technol. February 2015, 137(1): 011404.

Paper No: PVT-13-1195

https://doi.org/10.1115/1.4027320

Published Online: October 13, 2014

View Article titled, An Artificial Neural Network Model to Predict Material Characteristics From the Results of Miniature Disk Bending Tests

Topics: Artificial neural networks, Deflection, Stress, Disks, Errors, Displacement, Finite element analysis, Materials properties

Journal Articles

Applying Neural Networks to the Solution of the Inverse Heat Conduction Problem in a Gun Barrel

Y. Hwang, S. Deng

Publisher: ASME

J. Pressure Vessel Technol. August 2008, 130(3): 031203.

https://doi.org/10.1115/1.2937763

Published Online: June 12, 2008

View Article titled, Applying Neural Networks to the Solution of the Inverse Heat Conduction Problem in a Gun Barrel

Topics: Artificial neural networks, Gun barrels, Heat conduction, Heat flux, Temperature

Journal Articles

Erratum: “Incorporating Neural Network Material Models Within Finite Element Analysis for Rheological Behavior Prediction” [ Journal of Pressure Vessel Technology, 2007, 129(1), pp. 58–65]

B. Scott Kessler, A. Sherif El-Gizawy, Douglas E. Smith

Publisher: ASME

J. Pressure Vessel Technol. May 2007, 129(2): 342.

https://doi.org/10.1115/1.2728894

Published Online: May 1, 2007

View Article titled, Erratum: “Incorporating Neural Network Material Models Within Finite Element Analysis for Rheological Behavior Prediction” [ Journal of Pressure Vessel Technology, 2007, 129(1), pp. 58–65]

Topics: Artificial neural networks, Finite element analysis, Pressure vessels, Rheology

Journal Articles

Incorporating Neural Network Material Models Within Finite Element Analysis for Rheological Behavior Prediction

B. Scott Kessler, A. Sherif El-Gizawy, Douglas E. Smith

Publisher: ASME

J. Pressure Vessel Technol. February 2007, 129(1): 58–65.

https://doi.org/10.1115/1.2389004

Published Online: February 25, 2006

View Article titled, Incorporating Neural Network Material Models Within Finite Element Analysis for Rheological Behavior Prediction

Topics: Artificial neural networks, Finite element analysis, Stress, Flow (Dynamics), Rheology

Journal Articles

Condition Monitoring of a Nuclear Power Plant Check Valve Based on Acoustic Emission and a Neural Network

Min-Rae Lee, Joon-Hyun Lee, Jung-Teak Kim

Publisher: ASME

J. Pressure Vessel Technol. August 2005, 127(3): 230–236.

https://doi.org/10.1115/1.1991880

Published Online: April 11, 2005

View Article titled, Condition Monitoring of a Nuclear Power Plant Check Valve Based on Acoustic Emission and a Neural Network

Topics: Artificial neural networks, Condition monitoring, Disks, Failure mechanisms, Valves, Wear, Acoustic emissions, Failure, Nuclear power stations, Signals

Journal Articles

Defect Classification in Pipes by Neural Networks Using Multiple Guided Ultrasonic Wave Features Extracted After Wavelet Processing

Piervincenzo Rizzo, Ivan Bartoli, Alessandro Marzani, Francesco Lanza di Scalea

Publisher: ASME

J. Pressure Vessel Technol. August 2005, 127(3): 294–303.

https://doi.org/10.1115/1.1990213

Published Online: January 27, 2005

View Article titled, Defect Classification in Pipes by Neural Networks Using Multiple Guided Ultrasonic Wave Features Extracted After Wavelet Processing

Topics: Artificial neural networks, Damage, Inspection, Pipes, Signals, Wavelets, Waves, Feature extraction, Reflection, Steel

Journal Articles

Analysis of Eddy Current Data Using a Probabilistic Neural Network (PNN)

M. K. Au-Yang, Fellow ASME, J. C. Griffith

Publisher: ASME

J. Pressure Vessel Technol. August 2002, 124(3): 261–264.

https://doi.org/10.1115/1.1480826

Published Online: July 26, 2002

View Article titled, Analysis of Eddy Current Data Using a Probabilistic Neural Network (PNN)

Topics: Artificial neural networks, Eddies (Fluid dynamics), Eddy currents (Electricity), Resolution (Optics)

Journal Articles

Analyses of Possible Failure Mechanisms and Root Failure Causes in Power Plant Components Using Neural Networks and Structural Failure Database

S. Yoshimura, A. S. Jovanovic

Publisher: ASME

J. Pressure Vessel Technol. May 1996, 118(2): 237–246.

https://doi.org/10.1115/1.2842186

Published Online: May 1, 1996

View Article titled, Analyses of Possible Failure Mechanisms and Root Failure Causes in Power Plant Components Using Neural Networks and Structural Failure Database

Topics: Artificial neural networks, Databases, Failure, Failure mechanisms, Power stations, Structural failures, Creep, Design, Manufacturing, Structural elements (Construction)

Journal citation

NARROW

Research on Fatigue Stress Reconstruction of Major Equipment Based on Neural Network

Artificial Neural Network Technique for Seismic Fragility Analysis of a Storage Tank Supported by Multi-Storey Frame

Condition Monitoring of Nuclear Equipment-Piping Systems Subjected to Normal Operating Loads Using Deep Neural Networks

Guided Wave Damage Location of Pressure Vessel Based on Optimized Explainable Convolutional Neural Network for Multivariate Time Series Classification Neural Network

Convolutional Neural Network Based Defect Recognition Model for Phased Array Ultrasonic Testing Images of Electrofusion Joints

Damage Localization in Pressure Vessel by Guided Waves Based on Convolution Neural Network Approach

Burst Pressure Prediction of Cylindrical Vessels Using Artificial Neural Network

Development of Bolted Flange Design Tool Based on Artificial Neural Network

Development of an Optimized Neural Network for the Detection of Pipe Defects Using a Microwave Signal

An Artificial Neural Network Model to Predict Material Characteristics From the Results of Miniature Disk Bending Tests

Applying Neural Networks to the Solution of the Inverse Heat Conduction Problem in a Gun Barrel

Erratum: “Incorporating Neural Network Material Models Within Finite Element Analysis for Rheological Behavior Prediction” [ Journal of Pressure Vessel Technology, 2007, 129(1), pp. 58–65]

Incorporating Neural Network Material Models Within Finite Element Analysis for Rheological Behavior Prediction

Condition Monitoring of a Nuclear Power Plant Check Valve Based on Acoustic Emission and a Neural Network

Defect Classification in Pipes by Neural Networks Using Multiple Guided Ultrasonic Wave Features Extracted After Wavelet Processing

Analysis of Eddy Current Data Using a Probabilistic Neural Network (PNN)

Analyses of Possible Failure Mechanisms and Root Failure Causes in Power Plant Components Using Neural Networks and Structural Failure Database

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