The present discussion aims at complementing the original work published by Baldovino et al.(2018) by outlining a novel point of view. In light of the inherent limitations associated with the empirical model suggested...The present discussion aims at complementing the original work published by Baldovino et al.(2018) by outlining a novel point of view. In light of the inherent limitations associated with the empirical model suggested in the original article, the dimensional analysis technique was introduced to the soil-lime strength problem, thereby leading to the development of simple and physically meaningful dimensional models capable of predicting the unconfined compressive and splitting tensile strengths of compacted soil-lime mixtures as a function of the mixture's index properties, i.e. lime content, initial placement(or compaction) condition, initial specific surface area and curing time. The predictive capacity of the proposed dimensional models was examined and validated by statistical techniques. The proposed dimensional models contain a limited number of fitting parameters, which can be calibrated by minimal experimental effort and hence implemented for predictive purposes.展开更多
文摘The present discussion aims at complementing the original work published by Baldovino et al.(2018) by outlining a novel point of view. In light of the inherent limitations associated with the empirical model suggested in the original article, the dimensional analysis technique was introduced to the soil-lime strength problem, thereby leading to the development of simple and physically meaningful dimensional models capable of predicting the unconfined compressive and splitting tensile strengths of compacted soil-lime mixtures as a function of the mixture's index properties, i.e. lime content, initial placement(or compaction) condition, initial specific surface area and curing time. The predictive capacity of the proposed dimensional models was examined and validated by statistical techniques. The proposed dimensional models contain a limited number of fitting parameters, which can be calibrated by minimal experimental effort and hence implemented for predictive purposes.
