Mechanical properties of ignimbrites of Tenerife Island employed as building stone and their correlation with some physical properties

Abstract

The aim of this research work was to determine and analyse the mechanical properties of a group of four varieties of ignimbrites, which are differentiated according to their colour: brown, violet, grey and green. These ignimbrites are mined in the southern part of Tenerife (Canary Islands) and are widely used as building stone. Mechanical characterisation was carried out by determining: ultrasound propagation velocity, uniaxial compressive strength, flexural strength under concentrated load, point load strength, Schmidt hammer rebound number and rupture energy. To evaluate the influence of the anisotropy planes and the degree of correlation between the different directions, all tests were done in more than one direction, except for the rupture energy. A simple regression analysis incorporating the physical properties available in the literature is included, with special emphasis on those with the highest correlation coefficient. Finally, a multiple regression analysis is performed to find the best fitting equations to predict the compressive strength. Of the four varieties studied, the green ignimbrite proved to be the one with the highest mechanical strength, with some properties three times higher than those obtained for the brown ignimbrite and almost twice as high as those obtained for the violet ignimbrite and the grey ignimbrite, the latter varieties having a similar behaviour to each other. In most cases, the behaviour of the ignimbrites studied is consistent with their lithology, but some properties of the green ignimbrite exceed the range reported in the literature, thus providing a new reference for this lithotype. Simple regression analysis revealed strong correlations between uniaxial compressive strength and water absorption (r = −0.89) and point load strength (r = 0.85). Flexural strength showed very strong correlation with ultrasonic velocity (r = 0.91) and point load strength (r = 0.95). Applying the multiple regression method, the best estimators of compressive strength found were a model with four independent variables; apparent density, open porosity, water absorption and Schmidt hammer rebound index (R2 = 0.91) and a model with five variables, adding point load resistance (R2 = 0.93).