Bayesian optimization algorithm based support vector regression analysis for estimation of shear capacity of FRP reinforced concrete members

2021 ◽  
Vol 105 ◽  
pp. 107281
Author(s):  
Md Shah Alam ◽  
N. Sultana ◽  
S.M. Zakir Hossain
Keyword(s):  
Regression Analysis ◽  
Reinforced Concrete ◽  
Support Vector Regression ◽  
Optimization Algorithm ◽  
Shear Capacity ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Bayesian Optimization Algorithm ◽  
Concrete Members

Research on Shear Bearing Capacity of Reinforced Concrete Short Beam in Salt-Fog Environment

2012 ◽  
Vol 455-456 ◽  
pp. 1079-1083
Author(s):  
Wei Jun Yang ◽  
Hong Jia Huang ◽  
Wen Yu Jiang ◽  
Yi Bin Peng
Keyword(s):  
Reinforced Concrete ◽  
Bearing Capacity ◽  
Salt Spray ◽  
Test Chamber ◽  
Shear Capacity ◽  
Salt Spray Test ◽  
Concrete Members ◽  
Short Beam ◽  
Shear Bearing Capacity ◽  
Salt Fog

Shantou atmospheric salt-fog environment is simulated with the comprehensive salt spray test chamber. By using reinforced concrete short beams under different water-cement radio, different corrosion time, the inclined section degradation rules of the corrosive reinforced concrete members are researched for establishing shear capacity of short beam formulas in salt-fog environment.

An improved 2-pentanone low to high-temperature kinetic model using Bayesian Optimization algorithm

2021 ◽  
Vol 231 ◽  
pp. 111453
Author(s):  
Qianjin Lin ◽  
Chun Zou ◽  
Shibo Liu ◽  
Yunpeng Wang ◽  
Lixin Lu ◽  
...  
Keyword(s):  
High Temperature ◽  
Kinetic Model ◽  
Optimization Algorithm ◽  
Bayesian Optimization ◽  
Bayesian Optimization Algorithm

scholarly journals Application of Artificial Intelligence (AI) for Sustainable Highway and Road System

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Md Arifuzzaman ◽  
Muhammad Aniq Gul ◽  
Kaffayatullah Khan ◽  
S. M. Zakir Hossain
Keyword(s):  
Experimental Data ◽  
High Performance ◽  
Adhesion Force ◽  
Model Development ◽  
Real Life ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Adhesive Properties ◽  
Bayesian Optimization Algorithm ◽  
The Mean

There are several environmental factors such as temperature differential, moisture, oxidation, etc. that affect the extended life of the modified asphalt influencing its desired adhesive properties. Knowledge of the properties of asphalt adhesives can help to provide a more resilient and durable asphalt surface. In this study, a hybrid of Bayesian optimization algorithm and support vector regression approach is recommended to predict the adhesion force of asphalt. The effects of three important variables viz., conditions (fresh, wet and aged), binder types (base, 4% SB, 5% SB, 4% SBS and 5% SBS), and Carbon Nano Tube doses (0.5%, 1.0% and 1.5%) on adhesive force are taken into consideration. Real-life experimental data (405 specimens) are considered for model development. Using atomic force microscopy, the adhesive strength of nanoscales of test specimens is determined according to functional groups on the asphalt. It is found that the model predictions overlap with the experimental data with a high R2 of 90.5% and relative deviation are scattered around zero line. Besides, the mean, median and standard deviations of experimental and the predicted values are very close. In addition, the mean absolute Error, root mean square error and fractional bias values were found to be low, indicating the high performance of the developed model.

A decision-making method based on Bayesian optimization algorithm for small modular reactor

Kerntechnik ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. 109-121 ◽  
Author(s):  
B. Zhang ◽  
M. Peng ◽  
S. Cheng ◽  
L. Sun
Keyword(s):  
Decision Making ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Optimal Operation ◽  
Bayesian Optimization ◽  
Operating Personnel ◽  
Bayesian Optimization Algorithm ◽  
Autonomous Operation ◽  
Small Modular Reactor ◽  
The Cost

Abstract Small modular reactors (SMRs) are suitable for deployment in isolated underdeveloped areas to support highly localized microgrids. In order to achieve almost autonomous operation for reducing the cost of operating personnel, an autonomous control system with decision-making capability is needed. In this paper, a decision-making method based on Bayesian optimization algorithm (BOA) is proposed to explore the optimal operation scheme under fault conditions. BOA is used to adjust exploration strategy of operation scheme according to observations (operation schemes previously explored). To measure the feasibility of each operation scheme, an objective function that considers security and economy is established. BOA attempts to obtain the optimal operation scheme with maximum of the objective function in as few iterations as possible. To verify the proposed method, all main pump powered off fault is simulated by RELAP5 code. The optimal operation scheme of the fault is applied, the transient result shows that all key parameters are within safe limits and SMR is maintained at relatively high power, which means that BOA has the decision-making capability to get an optimal operation scheme on fault conditions.

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