Predicting temperature drop rate of mass concrete during an initial cooling period using genetic programming

2017 International Conference on Sensors, Materials and Manufacturing

工业技术;材料科学;机械制造

Bhattarai, Santosh^1 ; Zhou, Yihong^1 ; Zhao, Chunju^1 ; Zhou, Huawei^2

College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang

443002, China^1

School of Water Resources and Hydroelectric Engineering, Wuhan University, Wuhan, China^2

关键词: Coefficient of determination;    Construction phase;    Heat of hydration;    Inlet temperature;    Mean Square Error (MSE);    Prediction model;    Temperature development;    Temperature drops;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/311/1/012018/pdf
DOI  :  10.1088/1757-899X/311/1/012018

来源: IOP

【 摘 要 】

Thermal cracking on concrete dams depends upon the rate at which the concrete is cooled (temperature drop rate per day) within an initial cooling period during the construction phase. Thus, in order to control the thermal cracking of such structure, temperature development due to heat of hydration of cement should be dropped at suitable rate. In this study, an attempt have been made to formulate the relation between cooling rate of mass concrete with passage of time (age of concrete) and water cooling parameters: flow rate and inlet temperature of cooling water. Data measured at summer season (April-August from 2009 to 2012) from recently constructed high concrete dam were used to derive a prediction model with the help of Genetic Programming (GP) software "Eureqa". Coefficient of Determination (R) and Mean Square Error (MSE) were used to evaluate the performance of the model. The value of R and MSE is 0.8855 and 0.002961 respectively. Sensitivity analysis was performed to evaluate the relative impact on the target parameter due to input parameters. Further, testing the proposed model with an independent dataset those not included during analysis, results obtained from the proposed GP model are close enough to the real field data.