## 2 Background  
The estimated service life that a building or its components are expected to withstand under specific service conditions is reflected in the Reference Service Life (RSL) of building components. The methodology for determining RSL was developed by ISO 15686-1 [14]. As indicated by the associated international rules and regulations [15; 16], there is significant interest in this subject on a global scale. The performance of exterior finishing components subjected to various service conditions has been the subject of substantial fieldwork based on visual observations (empirical technique) in previous research [13; 17]. Furthermore, over the past decade, several studies have used accelerated laboratory tests (mineralogical-petrographic and chemical analyses, physical tests of porosity, hydride tests and durability, mechanical tests, and so on) to imitate the natural weathering of stone claddings in normal conditions or a marine environment [18; 19; 20]. These analyses are time-consuming and demand high resources, but they shed some light on the correlations between the micro- and macrostructure of stone and its degradation. To address this issue, statistical and probabilistic service life prediction  
Figure 1: Schematic of a typical wet- and dry-fixed stone cladding. methods offer a way to assess the data gathered during fieldwork at a higher degree of complexity due to the simultaneous examination of several degradation elements [17; 21; 22]. To determine the degradation patterns that most closely resemble the data points gathered in the field and calculate the average change over time, simple regression analysis has been employed extensively. The deterioration degree of each observed building component may be determined by averaging the visual and physical performance values, according to a prior study by [PERSON] et al. [11; 12]. A prediction interval is applied for the statistical analysis of errors related to estimating the anticipated service life of a building component at a specific age. The service life of several types of facades, including wet-fixed stone cladding exposed to typical circumstances, was investigated by [PERSON] et al. [13] using this approach. Typically, the deteriorating paths led to linear deterioration patterns. Regression analysis was also used by other researchers, such as [PERSON] and [PERSON] [23], [PERSON] et al. [24], [PERSON] [25], and [PERSON] [26]. The only distinction in these cases was that the intensity of the overall degradation of the different types of building claddings was determined by dividing the ratio between the size of the degraded area, which was weighted according to the severity of the anomalies, and the degree of degradation by the maximum amount of degradation of the total cladding area. [PERSON] et al. [24] used this technique to assess the deterioration of 142 structures with natural stone cladding that was directly adhered to the substrate. A third-degree polynomial line was used to correct the point distribution. The report by [PERSON] et al. [24], in contrast to [PERSON] and [PERSON] [13], did not classify their study buildings according to the service conditions to which they were exposed. [PERSON] et al. [17; 27] applied multiple regression analysis based on the same idea as simple regression analysis to estimate the service life of building components by analyzing the relationship between more than two independent variables [28]. According to [PERSON] et al. [28], the following factors are to be held responsible for the deterioration of stone claddings: (a) less than 5 km from the coast, which was the least favorable condition; (b) carved finishings, which were more susceptible to degradation than smooth and polished surfaces; and (c) larger stone plates, which were more susceptible to mechanical deformation and, as a result, to degradation because of their larger dimensions and weight.