Green facades are one of the most promising natural-based solutions for buildings. Notwithstanding, in regions with variating weather such as the northern hemisphere, these can be counterproductive for the structures due to humidity retention. For these reasons, this work presents the development of an Artificial Neural Network (ANN) model to estimate the hygrothermal behavior inside a concrete wall protected by a second foliage skin. The database used for model formation was obtained through measurements made in an Accelerated Weathering Laboratory (AWL) to emulate the Nordic climatic conditions for a typical year. The ANN-hygrothermal model was trained in function of the parameters: environment relative humidity, ambient temperature, microclimate's relative humidity, microclimate's temperature, and the separation distance between the vegetation and the wall. The statistical results of the model demonstrated successful adaptability and great generalization capacity for both internal temperature (R² = 99.98% for training and R² = 99.95% for testing) and internal humidity (R² = 99.16% for training and R² = 99.17% for testing). Additionally, a sensitivity analysis was implemented, showing that the most influential variable in the estimation of both hygrothermal parameters is the ambient temperature and that the separation distance has a significant impact on the humidity produced inside the wall. Finally, the presented computational approach can be implemented in non-invasive monitoring systems or as a complementary tool in studies of concrete degradation due to humidity.

绿色外墙是最有前途的天然建筑解决方案之一。尽管如此，在气候变化的地区（如北半球），由于保持湿度，这些结构可能会适得其反。由于这些原因，这项工作提出了一个人工神经网络（ANN）模型的开发，以估计由第二片叶子蒙皮保护的混凝土墙内部的湿热行为。通过在加速老化实验室（AWL）中进行的测量来获得用于模型形成的数据库，以模拟典型年份的北欧气候条件。通过以下参数对ANN湿热模型进行了训练：环境相对湿度，环境温度，小气候的相对湿度，小气候的温度，以及植被和墙壁之间的分隔距离。该模型的统计结果表明，对于内部温度（R²  = 99.98％用于训练，R ²  = 99.95％用于测试）和内部湿度（R ²  = 99.16％用于训练，R ²  = 99.17％用于测试）。此外，还进行了敏感性分析，结果表明，在估算两个湿热参数时，最有影响力的变量是环境温度，并且分隔距离对墙内产生的湿度有重大影响。最后，所提出的计算方法可以在非侵入式监测系统中实施，也可以作为研究湿度引起的混凝土退化的补充工具。