APPLICATION OF SOFT COMPUTING TECHNIQUES FOR PREDICTING COOLING TIME REQUIRED DROPPING INITIAL TEMPERATURE OF MASS CONCRETE.

Abstract

Minimizing the thermal cracks in mass concrete at an early age can be achieved by
removing the hydration heat as quickly as possible within initial cooling period before the next lift is
placed. Recognizing the time needed to remove hydration heat within initial cooling period helps to
take an effective and efficient decision on temperature control plan in advance. Thermal properties
of concrete, water cooling parameters and construction parameter are the most influencing factors
involved in the process and the relationship between these parameters are non-linear in a pattern,
complicated and not understood well. Some attempts had been made to understand and formulate
the relationship taking account of thermal properties of concrete and cooling water parameters.
Thus, in this study, an effort have been made to formulate the relationship for the same taking
account of thermal properties of concrete, water cooling parameters and construction parameter,
with the help of two soft computing techniques namely: Genetic programming (GP) software
“Eureqa” and Artificial Neural Network (ANN). Relationships were developed from the data
available from recently constructed high concrete double curvature arch dam. The value of R for
the relationship between the predicted and real cooling time from GP and ANN model is 0.8822
and 0.9146 respectively. Relative impact on target parameter due to input parameters was
evaluated through sensitivity analysis and the results reveal that, construction parameter influence
the target parameter significantly. Furthermore, during the testing phase of proposed models with
an independent set of data, the absolute and relative errors were significantly low, which indicates
the prediction power of the employed soft computing techniques deemed satisfactory as compared
to the measured data.