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.

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.

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.

Estimation of municipal waste generation of Turkey using socio-economic indicators by Bayesian optimization tuned Gaussian process regression

2020 ◽  
Vol 38 (8) ◽  
pp. 840-850 ◽  
Author(s):  
Zeynep Ceylan
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Kernel Functions ◽  
Machine Learning Algorithms ◽  
Economic Indicators ◽  
Bayesian Optimization ◽  
Superior Performance ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Support Vector

Accurate estimation of municipal solid waste (MSW) generation has become a crucial task in decision-making processes for the MSW planning and management systems. In this study, the Gaussian process regression (GPR) model tuned by Bayesian optimization was used to forecast the MSW generation of Turkey. The Bayesian optimization method, which can efficiently optimize the hyperparameters of kernel functions in the machine learning algorithms, was applied to reduce the computation redundancy and enhance the estimation performance of the models. Four socio-economic indicators such as population, gross domestic product per capita, inflation rate, and the unemployment rate were used as input variables. The performance of the Bayesian GPR (BGPR) model was compared with the multiple linear regression (MLR) and Bayesian support vector regression (BSVR) models. Different performance measures such as mean absolute deviation (MAD), root mean square error (RMSE), and coefficient of determination (R2) values were used to evaluate the performance of the models. The exponential-GPR model tuned by Bayesian optimization showed superior performance with minimum MAD (0.0182), RMSE (0.0203), and high R2 (0.9914) values in the training phase and minimum MAD (0.0342), RMSE (0.0463), and high R2 (0.9841) values in the testing phase. The results of this study can help decision-makers to be aware of social-economic factors associated with waste management and ensure optimal usage of their resources in future planning.

scholarly journals Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
George Kopsiaftis ◽  
Eftychios Protopapadakis ◽  
Athanasios Voulodimos ◽  
Nikolaos Doulamis ◽  
Aristotelis Mantoglou
Keyword(s):  
Gaussian Process ◽  
Seawater Intrusion ◽  
Surrogate Model ◽  
Gaussian Process Regression ◽  
Variable Density ◽  
Regression Method ◽  
Bayesian Optimization ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Pumping Rate

Accurate prediction of the seawater intrusion extent is necessary for many applications, such as groundwater management or protection of coastal aquifers from water quality deterioration. However, most applications require a large number of simulations usually at the expense of prediction accuracy. In this study, the Gaussian process regression method is investigated as a potential surrogate model for the computationally expensive variable density model. Gaussian process regression is a nonparametric kernel-based probabilistic model able to handle complex relations between input and output. In this study, the extent of seawater intrusion is represented by the location of the 0.5 kg/m3 iso-chlore at the bottom of the aquifer (seawater intrusion toe). The initial position of the toe, expressed as the distance of the specific line from a number of observation points across the coastline, along with the pumping rates are the surrogate model inputs, whereas the final position of the toe constitutes the output variable set. The training sample of the surrogate model consists of 4000 variable density simulations, which differ not only in the pumping rate pattern but also in the initial concentration distribution. The Latin hypercube sampling method is used to obtain the pumping rate patterns. For comparison purposes, a number of widely used regression methods are employed, specifically regression trees and Support Vector Machine regression (linear and nonlinear). A Bayesian optimization method is applied to all the regressors, to maximize their efficiency in the prediction of seawater intrusion. The final results indicate that the Gaussian process regression method, albeit more time consuming, proved to be more efficient in terms of the mean absolute error (MAE), the root mean square error (RMSE), and the coefficient of determination (R2).

scholarly journals Study on model for cutting force when milling SCM440 steel

2021 ◽  
pp. 23-35
Author(s):  
Nguyen Van Thien ◽  
Do Duc Trung
Keyword(s):  
Cutting Force ◽  
Cutting Speed ◽  
Absolute Error ◽  
Experimental Results ◽  
Parameter Analysis ◽  
Coefficient Of Determination ◽  
Data Transformations ◽  
Study Results ◽  
Input Parameters ◽  
Cutting Insert

This article presents empirical study results when milling SCM440 steel. The cutting insert to be used was a TiN coated cutting insert with tool tip radius of 0.5 mm. Experimental process was carried out with 18 experiments according to Box-Behnken matrix, in which cutting speed, feed rate and cutting depth were selected as the input parameters of each experiment. In addition, cutting force was selected as the output parameter. Analysis of experimental results has determined the influence of the input parameters as well as the interaction between them on the output parameters. From the experimental results, a regression model showing the relationship between cutting force and input parameters was built. Box-Cox and Johnson data transformations were applied to construct two other models of cutting force. These three regression models were used to predict cutting force and compare with experimental results. Using parameters including coefficient of determination (R-Sq), adjusted coefficient of determination (R-Sq(adj)) and percentage mean absolute error (% MAE) between the results predicted by the models and the experimental results are the criteria to compare the accuracy of the cutting force models. The results have determined that the two models using two data transformations have higher accuracy than model not using two data transformations. A comparison of the model using the Box-Cox transformation and the model using the Johnson transformation was made with a t-test. The results confirmed that these two models have equal accuracy. Finally, the development direction for the next study is mentioned in this article

scholarly journals Wavelet-Gaussian Process Regression Model for Regression Daily Solar Radiation in Ghardaia, Algeria

2021 ◽  
Vol 20 (2) ◽  
pp. 113-119
Author(s):  
Khaled Ferkous ◽  
Farouk Chellali ◽  
Abdalah Kouzou ◽  
Belgacem Bekkar
Keyword(s):  
Solar Radiation ◽  
Root Mean Square Error ◽  
Gaussian Process ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Gaussian Process Regression ◽  
Absolute Error ◽  
Horizontal Surface ◽  
Total Solar Radiation ◽  
Mean Square

Several methods have been used to predict daily solar radiation in recent years, such as artificial intelligence and hybrid models. In this paper, a Wavelet coupled Gaussian Process Regression (W-GPR) model was proposed to predict the daily solar radiation received on a horizontal surface in Ghardaia (Algeria). A statistical period of four years (2013 -2016) was used where the first three years (2013-2015) are used to train model and the last year (2016) to test the model for predicting daily total solar radiation. Different types of wave mother and different combinations of input data were evaluated based on the minimum air temperature, relative humidity and extraterrestrial solar radiation on a horizontal surface. The results demonstrated the effectiveness of the new hybrid model W-GPR compared to the classical GPR model in terms of Root Mean Square Error (RMSE), relative Root Mean Square Error (rRMSE), Mean Absolute Error (MAE) and determination coefficient (R2).

BOND STRENGTH OF STEEL-CONCRETE COMPOSITE ELEMENTS USING A CEMENTITIOUS ADHESIVE

2014 ◽  
Vol 1 (1) ◽  
Author(s):  
Yoshihiro Asada ◽  
Isamu Yoshitake ◽  
Atsushi Ogawa ◽  
Yuji Fujimoto
Keyword(s):  
Bond Strength ◽  
Shear Bond Strength ◽  
Shear Test ◽  
Composite System ◽  
Traffic Load ◽  
Shear Connectors ◽  
Lap Shear ◽  
Lap Shear Test ◽  
Composite Slabs ◽  
High Fatigue

Steel-concrete composite slabs have been used for bridge deck construction in Japan because of several advantages, e.g., safety construction and high fatigue durability. On the other hand, these slabs may cause negative influences such as overweight, increasing costs and low constructability, due to many mechanical shear connectors. The quantities of shear connectors may be reduced by gluing steel-plate and concrete with a cementitious adhesive. The present study aims at examining shear bond strength and quantifying the dispersion of the strength. To investigate the dispersion, a double-lap shear test is conducted, in addition to a simple direct shear test, to examine surface treatment. A Monte Carlo Simulation using the Weibull distribution of the strength is performed to evaluate the effect of dispersion. The simulation implies that the shear stress due to the traffic load may be negligible, indicating the applicability of the composite system for highway bridge decks.

scholarly journals Influence of Surface Preparation Method on the Bond Behavior of Externally Bonded CFRP Reinforcements in Concrete

Materials ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 414 ◽  
Author(s):  
Sérgio Soares ◽  
José Sena-Cruz ◽  
José Ricardo Cruz ◽  
Pedro Fernandes
Keyword(s):  
Bond Strength ◽  
Surface Preparation ◽  
Concrete Surface ◽  
Experimental Program ◽  
Laser Sensor ◽  
Bond Behavior ◽  
Lap Shear ◽  
Maximum Shear Force ◽  
Externally Bonded ◽  
Analytical Expressions

In last decades significant investigation has been carried out in order to predict the bond strength of externally bonded reinforcement (EBR) systems with carbon fiber reinforced polymer (CFRP) materials in concrete and, as consequence of that, many analytical expressions can be found in the literature, including in standards. However, these expressions do not account for the influence of several parameters on bond behavior such as the type of surface preparation which is a mandatory and critical task in the strengthening application. The present work gives contributions to reduce this lack of knowledge. For this purpose, an experimental program composed of single-lap shear tests was carried out, the main parameters studied being: (i) the type of concrete surface preparation (i.e., grinding and sandblasting) and (ii) the bond length. Prior to the application of the EBR CFRP system, the roughness level provided by the different methods of surface preparation was characterized by a laser sensor. Test results revealed that sandblasting concrete surface preparation yielded higher values, in terms of maximum shear force and fracture energy. Finally, existing expressions in standards were upgraded in order to account for the concrete surface roughness level in the estimation of the bond strength.

Direct Active Soldering of PEO Coated and Uncoated AA6061 Aluminium Alloy

2018 ◽  
Vol 1148 ◽  
pp. 152-158
Author(s):  
R. Sokkalingam ◽  
M.P. Shankar ◽  
Anoop K. Unni ◽  
K. Sivaprasad ◽  
Veerappan Muthupandi
Keyword(s):  
Mechanical Properties ◽  
Bond Strength ◽  
Aluminium Alloy ◽  
Solder Alloy ◽  
Shear Test ◽  
Emerging Technology ◽  
Lap Shear ◽  
Lap Shear Test ◽  
Aa6061 Aluminium Alloy ◽  
Active Soldering

Active soldering is an emerging technology of joining a range of materials, which utilizes Sn-Ag composition solder alloy other than conventional Sn-Pb solder, along with reactive element additions (like Ti, Ta, Hf and Zr). In the present study, active soldering is employed for bonding AA6061 aluminium alloy using S Bond 220-50 and S Bond 220M active solders. Soldering was achieved by direct active soldering of AA6061 plates each other using S Bond 220-50 and PEO coated AA6061 aluminium alloy using S Bond 220M, taking into consideration of the application of active solder alloy in joining ceramics. Mechanical properties were evaluated using hardness and lap shear test. Microstructure analysis is carried out by SEM microscopy. The soldered area is examined using EDS giving fair idea regarding the composition of elements present in the solder. It is observed that direct bonding of aluminium alloy using S Bond 220-50 showed better bond strength and hardness when compared with joining using S Bond 220M of the PEO coated aluminium plate.

scholarly journals Wavelet-Gaussian process regression model for forecasting daily solar radiation in the Saharan climate

Clean Energy ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 316-328
Author(s):  
Khaled Ferkous ◽  
Farouk Chellali ◽  
Abdalah Kouzou ◽  
Belgacem Bekkar
Keyword(s):  
Solar Radiation ◽  
Root Mean Square Error ◽  
Gaussian Process ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Gaussian Process Regression ◽  
Absolute Error ◽  
Horizontal Surface ◽  
Mean Square ◽  
Model Training

Abstract Forecasting solar radiation is fundamental to several domains related to renewable energy where several methods have been used to predict daily solar radiation, such as artificial intelligence and hybrid models. Recently, the Gaussian process regression (GPR) algorithm has been used successfully in remote sensing and Earth sciences. In this paper, a wavelet-coupled Gaussian process regression (W–GPR) model was proposed to predict the daily solar radiation received on a horizontal surface in Ghardaia (Algeria). For this purpose, 3 years of data (2013–15) have been used in model training while the data of 2016 were used to validate the model. In this work, different types of mother wavelets and different combinations of input data were evaluated based on the minimum air temperature, relative humidity and extraterrestrial solar radiation on a horizontal surface. The results demonstrated the effectiveness of the new hybrid W–GPR model compared with the classical GPR model in terms of root mean square error (RMSE), relative root mean square error (rRMSE), mean absolute error (MAE) and determination coefficient (R2).

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