Abstract

This study, based on the temperature monitoring data of jiangya RCCD, uses principle and method of partial least-squares regression to analyze and predict temperature variation of RCCD. By founding partial least-squares regression model, multiple correlations of independent variables is overcome, organic combination on multiple linear regressions, multiple linear regression and canonical correlation analysis is achieved. Compared with general least-squares regression model result, it is more advanced and accurate, had more practical explanation. It is proved feasible and practical, so, it can be used to predict concrete temperature. By calculating, the result shows that rock temperature is the most important factor which affects RCCD temperature. RCCD temperature is decreasing with rock temperature. We suggest that rock temperature should be monitored as emphasis in the future; this can provide some scientific basis for temperature controlling and preventing RCCD crack.

Keywords:

Multiple linear regressions, partial least-squares regression, RCCD, temperature analysis and prediction,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.