Validation of Adaptive Gaussian Process Regression Model Used for SIF Prediction

2018 ◽  
Author(s):  
Arvind Keprate ◽  
R. M. Chandima Ratnayake ◽  
Shankar Sankararaman
Keyword(s):  
Regression Model ◽  
Gaussian Process ◽  
Prediction Accuracy ◽  
Experimental Validation ◽  
Gaussian Process Regression ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
Maximum Absolute Error ◽  
Data Points

The main aim of this paper is to perform the validation of the adaptive Gaussian process regression model (AGPRM) developed by the authors for the Stress Intensity Factor (SIF) prediction of a crack propagating in topside piping. For validation purposes, the values of SIF obtained from experiments available in the literature are used. Sixty-six data points (consisting of L, a, c and SIF values obtained by experiments) are used to train the AGPRM, while four independent data sets are used for validation purposes. The experimental validation of the AGPRM also consists of the comparison of the prediction accuracy of AGPRM and Finite Element Method (FEM) relative to the experimentally derived SIF values. Four metrics, namely, Root Mean Square Error (RMSE), Average Absolute Error (AAE), Maximum Absolute Error (MAE), and Coefficient of Determination (R2), are used to compare the accuracy. A case study illustrating the development and experimental validation of the AGPRM is presented. Results indicate that the prediction accuracy of the AGPRM is comparable with and even higher than that of the FEM, provided the training points of the AGPRM are aptly chosen.

Experimental Validation of the Adaptive Gaussian Process Regression Model Used for Prediction of Stress Intensity Factor as an Alternative to Finite Element Method

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Arvind Keprate ◽  
R. M. Chandima Ratnayake ◽  
Shankar Sankararaman
Keyword(s):  
Finite Element Method ◽  
Stress Intensity Factor ◽  
Finite Element ◽  
Stress Intensity ◽  
Intensity Factor ◽  
Regression Model ◽  
Prediction Accuracy ◽  
Experimental Validation ◽  
Gaussian Process Regression ◽  
Absolute Error

Currently, in the oil and gas industry, finite element method (FEM)-based commercial software (such as ANSYS and abaqus) is commonly employed for determining the stress intensity factor (SIF). In their earlier work, the authors proposed an adaptive Gaussian process regression model (AGPRM) for the SIF prediction of a crack propagating in topside piping, as an inexpensive alternative to FEM. This paper is the continuation of the earlier work, as it focuses on the experimental validation of the proposed AGPRM. For validation purposes, the values of SIF obtained from experiments available in the literature are used. The experimental validation of AGPRM also consists of the comparison of the prediction accuracy of AGPRM and FEM relative to the experimentally derived SIF values. Five metrics, namely, root-mean-square error (RMSE), average absolute error (AAE), mean absolute percentage error (MAPE), maximum absolute error (MAE), and coefficient of determination (R2), are used to compare the accuracy. A case study illustrating the development and experimental validation of the AGPRM is presented. Results indicate that the prediction accuracy of AGPRM is comparable with and even higher than FEM, provided the training points of AGPRM are chosen aptly. Good prediction accuracy coupled with less time consumption favors AGPRM as an alternative to FEM for SIF prediction.

Comparing Different Metamodelling Approaches to Predict Stress Intensity Factor of a Semi-Elliptic Crack

2017 ◽  
Author(s):  
Arvind Keprate ◽  
R. M. Chandima Ratnayake ◽  
Shankar Sankararaman
Keyword(s):  
Stress Intensity Factor ◽  
Stress Intensity ◽  
Intensity Factor ◽  
Polynomial Regression ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Elliptic Crack ◽  
Maximum Absolute Error ◽  
Data Points

This paper examines the applicability of the different meta-models (MMs) to predict the Stress Intensity Factor (SIF) of a semi-elliptic crack propagating in topside piping, as an inexpensive alternative to the Finite Element Methods (FEM). Five different MMs, namely, multi-linear regression (MLR), second order polynomial regression (PR-2) (with interaction), Gaussian process regression (GPR), neural networks (NN) and support vector regression (SVR) have been tested. Seventy data points (SIF values obtained by FEM) are used to train the aforementioned MMs, while thirty data points are used as the testing points. In order to compare the accuracy of the MMs, four metrics, namely, Root Mean Square Error (RMSE), Average Absolute Error (AAE), Maximum Absolute Error (AAE), and Coefficient of Determination (R2) are used. Although PR-2 emerged as the best fit, GPR was selected as the best MM for SIF determination due to its capability of calculating the uncertainty related to the prediction values. The aforementioned uncertainty representation is quite valuable, as it is used to adaptively train the GPR model, which further improves its prediction accuracy.

scholarly journals Performance assessment of gaussian process regression to predict the bond strength of FRP sheets to concrete

2021 ◽  
Vol 72 (4) ◽  
pp. 411-422
Author(s):  
Nguyen Thuy Anh ◽  
Ly Hai Bang
Keyword(s):  
Bond Strength ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Absolute Error ◽  
Experimental Results ◽  
Coefficient Of Determination ◽  
Lap Shear ◽  
Lap Shear Test ◽  
Externally Bonded ◽  
Efficient Alternative

A Gaussian process regression (GPR) model for predicting the bond strength of FRP-to-concrete is proposed in this study. Published single-lap shear test specimens are used to predict the bond strength of externally bonded FRP systems adhered to concrete prisms. A database of 150 experimental results collected from published works is used for the training and testing phases of the proposed GPR model, containing 6 input parameters (width of concrete prism, concrete compressive strength, FRP thickness, FRP width, FRP length, and FRP modulus of elasticity). The output parameter of the prediction problem is bond strength. Three statistical indicators, namely the coefficient of determination, root mean square error (RMSE), and mean absolute error (MAE) are used to evaluate the performance of the proposed GPR model over 500 simulations. The results of this study indicate that the GPR provides an efficient alternative method for predicting the bond strength of FRP-to-concrete when compared to experimental results.

scholarly journals Application of Gaussian Process Regression (GPR) in Gas Hydrate Mitigation

2021 ◽  
Vol 88 (2) ◽  
pp. 27-37
Author(s):  
Sachin Dev Suresh ◽  
Ali Qasim ◽  
Bhajan Lal ◽  
Syed Muhammad Imran ◽  
Khor Siak Foo
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gas Hydrate ◽  
Learning Algorithm ◽  
Hydrate Formation ◽  
Gaussian Process Regression ◽  
Normal Operation ◽  
Coefficient Of Determination ◽  
Gas Hydrate Formation ◽  
Testing Data

The production of oil and natural gas contributes to a significant amount of revenue generation in Malaysia thereby strengthening the country’s economy. The flow assurance industry is faced with impediments during smooth operation of the transmission pipeline in which gas hydrate formation is the most important. It affects the normal operation of the pipeline by plugging it. Under high pressure and low temperature conditions, gas hydrate is a crystalline structure consisting of a network of hydrogen bonds between host molecules of water and guest molecules of the incoming gases. Industry uses different types of chemical inhibitors in pipeline to suppress hydrate formation. To overcome this problem, machine learning algorithm has been introduced as part of risk management strategies. The objective of this paper is to utilize Machine Learning (ML) model which is Gaussian Process Regression (GPR). GPR is a new approach being applied to mitigate the growth of gas hydrate. The input parameters used are concentration and pressure of Carbon Dioxide (CO2) and Methane (CH4) gas hydrates whereas the output parameter is the Average Depression Temperature (ADT). The values for the parameter are taken from available data sets that enable GPR to predict the results accurately in terms of Coefficient of Determination, R2 and Mean Squared Error, MSE. The outcome from the research showed that GPR model provided with highest R2 value for training and testing data of 97.25% and 96.71%, respectively. MSE value for GPR was also found to be lowest for training and testing data of 0.019 and 0.023, respectively.

Anomaly Detection in Video Surveillance via Gaussian Process

2015 ◽  
Vol 29 (06) ◽  
pp. 1555011 ◽  
Author(s):  
Nannan Li ◽  
Xinyu Wu ◽  
Huiwen Guo ◽  
Dan Xu ◽  
Yongsheng Ou ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Regression Model ◽  
Gaussian Process ◽  
Video Surveillance ◽  
Gaussian Process Regression ◽  
Bayesian Regression ◽  
Current Frame ◽  
Motion Patterns ◽  
Online Clustering ◽  
Efficient Calculation

In this paper, we propose a new approach for anomaly detection in video surveillance. This approach is based on a nonparametric Bayesian regression model built upon Gaussian process priors. It establishes a set of basic vectors describing motion patterns from low-level features via online clustering, and then constructs a Gaussian process regression model to approximate the distribution of motion patterns in kernel space. We analyze different anomaly measure criterions derived from Gaussian process regression model and compare their performances. To reduce false detections caused by crowd occlusion, we utilize supplement information from previous frames to assist in anomaly detection for current frame. In addition, we address the problem of hyperparameter tuning and discuss the method of efficient calculation to reduce computation overhead. The approach is verified on published anomaly detection datasets and compared with other existing methods. The experiment results demonstrate that it can detect various anomalies efficiently and accurately.

Application of Gaussian Process Regression Model to Predict Discharge Coefficient of Gated Piano Key Weir

2019 ◽  
Vol 33 (11) ◽  
pp. 3929-3947 ◽  
Author(s):  
Masood Akbari ◽  
Farzin Salmasi ◽  
Hadi Arvanaghi ◽  
Masoud Karbasi ◽  
Davood Farsadizadeh
Keyword(s):  
Regression Model ◽  
Gaussian Process ◽  
Discharge Coefficient ◽  
Gaussian Process Regression ◽  
Piano Key Weir
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