The presence of artificial intelligence in our lives is increasing and being applied to fields such as medicine, engineering, telecommunications, remote sensing and 3D visualization. Nevertheless, it has never been used for the stratigraphic study of historical buildings. Thus far, archaeologists and architects, the experts in archaeology of architecture, have led this research. The method consisted of visually—and, consequently, subjectively—identifying certain evidence regarding the elevations of such buildings that could be a consequence of the passage of time. In this article, we would like to present the results from one of the research projects pursued by our group, in which we automated the stratigraphic study of some historic buildings using multivariate statistic techniques. To this end, we first measured the building using surveying techniques to create a 3D model, and then, we broke down every stone into qualitative and quantitative variables. To identify the stratigraphic features on the walls, we applied machine learning by conducting different predictive and descriptive analyses. The predictive analyses were used to rule out any blocks of stone with different characteristics, such as rough stones, joint ashlars, and voussoirs of arches; these are irregularities that probably show building processes and whose identification is crucial in ascertaining the structural evolution of the building. In supervised learning, we experimented with decision trees and random forest—and although the results were good in all cases, we ultimately opted to implement the predictive model obtained using the last one. While identifying the evidence on the walls, it was also very important to identify different continuity solutions or interfaces present on them, because although these are elements without materiality, they are of great value in terms of timescale, because they delimit different strata and allow us to deduce the relationship between them.

中文翻译：

---

用于建筑遗产考古研究的机器学习

人工智能在我们生活中的存在越来越多，并被应用于医学、工程、电信、遥感和 3D 可视化等领域。然而，它从未被用于历史建筑的地层学研究。迄今为止，考古学家和建筑师，建筑考古学专家，领导了这项研究。该方法包括在视觉上——因此，在主观上——识别有关此类建筑物高度的某些证据，这些证据可能是时间流逝的结果。在本文中，我们想介绍我们小组进行的一项研究项目的结果，在该项目中，我们使用多元统计技术对一些历史建筑的地层研究进行了自动化处理。为此，我们首先使用测量技术测量建筑物以创建 3D 模型，然后，我们将每块石头分解为定性和定量变量。为了识别墙壁上的地层特征，我们通过进行不同的预测和描述性分析来应用机器学习。预测分析用于排除任何具有不同特征的石头块，例如粗糙的石头、关节方石和拱形的voussoirs；这些违规行为可能显示了建筑过程，其识别对于确定建筑物的结构演变至关重要。在监督学习中，我们尝试了决策树和随机森林——尽管结果在所有情况下都很好，但我们最终选择实施使用最后一个获得的预测模型。