Textural analysis of ancient plasters and mortars: reliability of image analysis approaches

Abstract

AbstractDifferent image analysis (IA) methods have been developed to compute textural parameters of ancient plasters and mortars using standard petrographic thin sections. These IA routines were applied to samples of materials with different technological characteristics from three historical buildings in the city of Pavia (Northern Italy), covering a period from the 12th to the 19th century. The IA techniques tested in this study belong both to classical digital image processing and to neural network modelling. In the first case, analyses were performed by commercial IA software whereas in the second case a Multi-Layer Perceptron neural network (MLP) was tested. Digital image analysis was performed on images taken by means of a petrographic microscope; additionally, analysis of back-scattered electron (BSE) images was performed. Textural data obtained through the IA applied to thin sections were compared with the data from traditional point counting and mechanical sieve analysis of disaggregated samples of the same materials. The results show that the IA of thin sections provides robust results in a fast and easy way. However, the reliability of the analyses can be prejudiced by textural and compositional heterogeneity of the samples.