Modelling of Asphalt’s Adhesive Behaviour Using Classification and Regression Tree (CART) Analysis

<table class="table-group" id="tab2"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Variable</td><td class="align_center">Explanation</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">Fresh</td><td class="align_center">Binary variable: 1 for fresh samples or otherwise 0</td></tr><tr><td class="align_left">Si<sub>3</sub>N<sub>4</sub>, NH<sub>3</sub>, OH, CH<sub>3</sub></td><td class="align_center">Binary variables for tip type: 1 for a specific tip type or otherwise 0. Tip type is 0 for COOH</td></tr><tr><td class="align_left">SB4, SB5, SBS4, SBS5</td><td class="align_center">Binary variables for binder type: 1 for a specific binder type or otherwise 0</td></tr><tr><td class="align_left">CNT%</td><td class="align_center">% of carbon nanotubes</td></tr><tr><td class="align_left">SWNT (or CNT)</td><td class="align_center">Binary variable for CNT type: 1 for SWNT type and 0 for MWNT type</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

Computational Intelligence and Neuroscience

tab2

Table 2

Table 2: Modelling of Asphalt’s Adhesive Behaviour Using Classification and Regression Tree (CART) Analysis 