Historical masonry constructions are difficult to mimic in hygrothermal models. The material properties of the walls are often highly uncertain due to the natural origin of the aggregates and the various, manual production processes used through time. Therefore, sensitivity analyses based on probabilistic simulations are powerful tools to indicate the risks on damage in masonry constructions. Damage criteria for relevant pathologies such as frost damage, potential decay of wooden beam heads and mould growth at the interior surface are used. The assessment methods (Scatter plots, Classification trees and Sobol indices) are based on 1D Heat, Air and Moisture simulations, including realistic variations on climate parameters and wall properties. These methodologies are applied to probabilistic simulations in which a potential damage risk is expected in historic masonries. The application of interior insulation, the use of hydrophobic treatments, and the impact of potential water infiltrations through cracks are discussed. In most of these situations a high dependency of each of the damage criteria on the rain intensity, the trend of the moisture retention/liquid conductivity curve and the absorption coefficient is evident, but also additional insights are found. For example, the thermal impact of interior insulation is negligible compared to its reduction of the first phase drying potential towards the interior. For hydrophobic treatments, the risk for damage typically decreases, but in combination with a rain water infiltration rate above approximately 5% of the wind driven rain the risk on mould growth at the interior surface significantly increases.

在湿热模型中很难模仿历史砌体结构。由于骨料的自然来源以及随着时间的流逝而使用的各种人工生产工艺，墙壁的材料性能通常高度不确定。因此，基于概率模拟的灵敏度分析是指示砌体结构损坏风险的有力工具。使用相关病理的损坏标准，例如霜冻损坏，木梁头的潜在衰减以及内表面的霉菌生长。评估方法（散点图，分类树和Sobol指数）基于一维热，空气和湿度模拟，包括气候参数和墙体特性的实际变化。这些方法被应用于概率模拟中，在这些模拟中历史砖石中可能存在潜在的损坏风险。讨论了内部保温材料的应用，疏水处理的使用以及潜在的水通过裂缝渗入的影响。在大多数情况下，每种破坏标准对降雨强度，水分保持/液体电导率曲线的趋势和吸收系数的依赖性都很高，但也发现了更多的见解。例如，与其减少第一相向内部的干燥势能相比，内部绝热层的热影响可以忽略不计。对于疏水处理，损坏的风险通常会降低，