Compressive strength of natural hydraulic lime mortars using soft computing techniques

Abstract

In recent years, natural hydraulic lime (NHL) mortars have gained increased attention from researchers, not only as restoration materials for monuments and historical buildings, but also as an eco-friendly material which can be used as binder to formulate mortars for contemporary structures. In the present study, an extended database related to NHL mortars is compiled, related to all three NHL grades (NHL5, NHL3.5, NHL2) and soft computing techniques namely artificial neural networks (ANN) are utilized to reveal the influence of the mortar’s mix design on mechanical strength, as well as to predict the compressive strength of NHL mortar mixes. Influence of the binder to aggregate, water to binder and maximum aggregate size on the compressive strength of a mortar at different mortar ages is revealed, for the three grades of natural hydraulic lime, further highlighting aspects of this “new” material, which has been used as a binder since antiquity.