Multiple Regression Analysis
Definition of Regressors
As concluded in the previous section, two variables were retained as representative of the mechanical performance of the material: the average compressive strength at 28 days fC, in MPa), and the flexural toughness (T, in MPa).
The load ratio (IFa, in percentage) was considered to account for the sustained load applied.
The fibre material was also considered, as a binary variable (Mat): synthetic or nonsynthetic, as synthetic fibres were found to be significantly different (p value = 0.00125).
Summary of the MLR Models Obtained
Multiple linear regression (MLR) was used to relate each of the creep parameters under consideration to the aforementioned regressors. Initially, models with different formulations including all variables, squared variables, second order interactions and some selected third order interactions were considered. Stepwise and best subsets regression procedures were applied to simplify these models by discarding those terms that were not statistically significant.
Table 3 summarises the MLR models as obtained after this sequential process. The terms that were identified as statistically significant (pvalues up to 0.05) are marked with an asterisk. These models were used as a tool to obtain average
Table 3 Summary of the MLR models
Creep coefficients 
Crack opening ratios 

14 days 
30 days 
90 days 
014 days 
1430 days 
3090 days 

IFa 
* 

IFa^{2} 
* 

fc 

T 
* 
* 

Mat 
* 
* 
* 

T x IFa 
* 

Txfc 
* 
* 
* 
* 

Mat x IFa 
* 
* 
* 
* 
* 
* 
Mat x IFa^{2} 
* 
* 
* 
* 
* 
* 
Mat x T 
* 
* 
* 

Mat x f_{c} 
* 
* 
* 
* 

Mat xf 
* 
* 
* 
* 
* 
* 
Mat x IFa x T 
* 
* 
* 
* 

Mat x IFa x f_{c} 
* 
* 
* 
* 
* 
* 
Mat x IFa^{2} x T 
* 
* 
* 

Mat x IFa^{2} xf 
* 
* 
* 
* 
* 
* 
Rsquared 
0.55 
0.51 
0.46 
0.54 
0.43 
0.41 
estimates and trends for the creep coefficients, which allowed the study of these multivariate relationships as reported in the following sections. Furthermore, it is anticipated that future postprocessing of these models can increase their predictive accuracy.