Program Design of Concrete Thermal Parameters Back Analysis Based on Improved Accelerating Genetic Algorithm

2010 ◽  
Author(s):  
Li HanWu ◽  
Ji Shunwen ◽  
Liu Qiuchang ◽  
Ning HuaiMing
Keyword(s):  
Genetic Algorithm ◽  
Program Design ◽  
Back Analysis ◽  
Thermal Parameters ◽  
Accelerating Genetic Algorithm

Response surface genetic algorithm of back analysis of concrete thermal parameters

2015 ◽  
Vol 19 (sup8) ◽  
pp. S8-840-S8-845
Author(s):  
L. Pei ◽  
J.-K. Chen ◽  
Z.-Y. Wu ◽  
Y.-L. Li ◽  
H. Zhang
Keyword(s):  
Genetic Algorithm ◽  
Response Surface ◽  
Back Analysis ◽  
Thermal Parameters

Study on Numerical Inversion of Concrete Temperature Field with Cooling Pipe

2011 ◽  
Vol 368-373 ◽  
pp. 1939-1942
Author(s):  
Shou Kai Chen ◽  
Wei Wei Zhang
Keyword(s):  
Genetic Algorithm ◽  
Temperature Field ◽  
Back Analysis ◽  
Thermal Parameters ◽  
Numerical Inversion ◽  
Measured Temperature ◽  
Improved Genetic Algorithm ◽  
Cooling Pipe ◽  
Concrete Temperature ◽  
Temperature Test

The accuracy of thermal parameters, which are one of the important conditions to study the problem of temperature control and crack prevention of concrete, directly influence the calculation of concrete temperature and stress field. Based on basic genetic algorithm, the FORTRAN procedure was programmed after improving its defects. According to a project, the temperature test was carried out in one of sections of a prototype concrete structure, and the measured temperature dates were obtained. Based on the improved genetic algorithm and the measured temperature dates, the thermal parameters of concrete were obtained after back analysis, and the calculated and measured values were compared. The results show that the improved genetic algorithm provides a effective way for back analysis of concrete temperature field.

scholarly journals The Application of Improved Genetic Algorithm to the Back Analysis of Foundation Pit Construction

2021 ◽  
Vol 676 (1) ◽  
pp. 012124
Author(s):  
Kai Peng ◽  
Yuefei Zhou ◽  
Yaolai Liu ◽  
Liangliang Ma ◽  
Yi Xie ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Back Analysis ◽  
Improved Genetic Algorithm ◽  
Foundation Pit

scholarly journals Determination of Multilayer Soil Strength Parameters Using Genetic Algorithm

2018 ◽  
Vol 4 (10) ◽  
pp. 2383 ◽  
Author(s):  
Seyyed Mohammad Hashemi ◽  
Iraj Rahmani
Keyword(s):  
Genetic Algorithm ◽  
Lateral Displacement ◽  
Error Function ◽  
Back Analysis ◽  
Soil Strength ◽  
Strength Parameters ◽  
Analysis Process ◽  
Project Monitoring ◽  
The Difference ◽  
Python Programming

This paper employs a back analysis method to determine soil strength parameters of the Mohr-Coulomb model from in situ geotechnical measurements. The lateral displacement of a soil nailed wall retaining an excavation in Tehran city used as a criterion for the back analysis. For this purpose, a genetic algorithm is applied as an optimization algorithm to minimize the error function, which can perform the back analysis process. When the accuracy of modeling is verified, the back analysis is performed automatically by creating a link between genetic algorithm in MATLAB and Abaqus software using Python programming language. This paper demonstrated that the genetic algorithm is a particularly suitable tool to determine 9 soil strength parameters simultaneously for 3 soil layers of the project site to decrease the difference of lateral displacement between the results of project monitoring and numerical analysis. The soil strength parameters have increased, with the most changes in Young's modulus of the first to third layers as the most effective parameter, 49.45%, 61.67% and 64.35% respectively. The results can be used in advanced engineering analyses and professional works.

Back Analysis of Material Parameters of Concrete Dam Using Genetic Algorithm

2012 ◽  
Vol 562-564 ◽  
pp. 1955-1958
Author(s):  
Jin Bao Liu ◽  
Shou Ju Li ◽  
Wei Zhu
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Optimization Problem ◽  
Back Analysis ◽  
Water Level Change ◽  
Concrete Dam ◽  
Material Parameters ◽  
Level Change ◽  
Inversion Procedure ◽  
The Difference

The inverse problem of parameter identification is deal with by minimizing an objective function that contains the difference between observed and calculated dam displacements. The optimization problem of minimizing objective function is solved with genetic algorithm. The calculated dam displacements are simulated by using finite element method according to water level change acting on dam upstream. The practical dam displacements are observed on the dam crest. The investigation shows that the forecasted dam displacements agree well with observed ones. The effectiveness of proposed inversion procedure is validated.

Sign in / Sign up

Export Citation Format

Share Document