## 1 Introduction
[17] also utilized decision tree algorithms to predict the compressive strength of cementitious systems, evaluating the effects of Portland cement characteristics on the properties and performance of cement pastes and mortars. Despite the promising applications of machine learning in mortar research, studies in this area remain scarce compared to that of other scientific fields. However, an increasing interest in this topic is evident (Figure 2), as observed by the annual publication data in the Scopus database since 2020 using the following search query: (TITLE (mortar) AND TITLE-ABS-KEY (\"Machine learning\") AND (LIMIT-TO (DOCTYPE, \"ar\")). The widespread use of machine learning algorithms in mortars underscores the necessity of a thorough understanding of the material, which allows for predicting behaviors and properties. Standardized laboratory tests characterize mortars; however, these tests do not account for interactions with substrates, as they are conducted in metal molds to assess the mortar's quality and compliance with the requirements. To predict a mortar's performance, some authors have studied the interaction of the mortar with the substrate after its application, revealing that the substrates significantly influence the specific performance of the mortar. [PERSON] [18] evaluated the support/mortar bonds with different moisture contents and mortars, concluding that these factors influence adhesion. The author performed pull-off tests and made observations using a stereomicroscope and a scanning electron microscope to achieve this. [PERSON] et al. [19] used three types of supports and one mortar, concluding that the type of support affects the pore distribution and mortar adhesion. [PERSON] et al. [20] used three supports (brick, concrete, and acrylic), three types of mortars (cement mortar, aerial lime mortar, and pre-dosed cement mortar), with one mix type for each mortar, and evaluated parameters such as open porosity, capillary water absorption, vapor permeability, dynamic modulus of elasticity, and compressive strength. The characteristics of the mortars detached from the supports were compared with those of the mortars molded and hardened  
Figure 2: Articles published per year on the topic of machine learning and mortar. in laboratory holds to analyze the influence of the support. This comparison revealed that various experimental factors significantly influence the mortars' characteristics after their application to the supports. [PERSON] et al. [21] studied the influence of the substrate on the porosimetry of hardened mortars, noticing that the substrate properties induce changes in the pore size, distribution, and apparent density. [PERSON] et al. [22] studied the effect of hollowed ceramic brick substrates on cement mortar with fine sand and regular sand, concluding that the substrate influences the bulk density and compressive strength of the mortar, leading to an increase in compressive strength when compared with the characterization carried out on laboratory models. [PERSON] [23] investigated the adhesion system between a mortar and a substrate utilizing a scanning electron microscope (SEM-EDS) and an X-ray fluorescence spectrometer, aiming to correlate microstructural evaluations with the macrostructural characteristics of adhesion strength. This researcher concluded that the composition of a cement paste and its capacity for penetration into the substrate are interrelated with the adhesion properties. Thus, the porosity of the mortar has an important role in its performance, since it influences other properties, such as mechanical resistance, water vapor permeability, and water absorption by capillarity. Predicting the mortar's porosity is of great value when optimizing its formulation and adapting it to the built environment. This optimization should consider the substrate's influence once it significantly influences the characteristics of the applied mortar.