Weathering is one of the most common causes of building sandstone damage. The evolution of building sandstone in various weathering behaviors is critical for research. An intelligent assessment approach for classifying weathering degree of building sandstone in a humid environment is presented in this study. This synthesis method relates to three parts: microscopic observation of weathering characteristics, hyperspectral acquisition of weathered samples, and machine learning technology for a classification model. At first, weathering process is divided into initial weathered stage, accelerated weathered stage, and stable weathered stage according to the causes and mechanisms of weathering. Secondly, a novel classification method of weathering degree is proposed based on the weathering stage. Then, the mapping relationship between microscopic characteristics and hyperspectral image of shedding samples can be established in the visible and near-infrared spectral ranges (400–1000 nm) according to the change law of spectral absorption feature. Next, the spectral data of building sandstone with different weathering degrees are classified using Random Forest model. Furthermore, the hyperparameters of Random Forest model are optimized by Gray Wolf Optimizer algorithm for better performance. The trained model is finally applied to evaluate the weathering degree of large-scale sandstone walls quantitatively. The whole weathering assessment process is worth recommending for diagnosing and monitoring the building sandstone.

风化是建筑砂岩损坏的最常见原因之一。建筑砂岩在各种风化行为中的演变对于研究至关重要。提出了一种潮湿环境下建筑砂岩风化程度分级的智能评价方法。该合成方法涉及三个部分：风化特征的显微观察、风化样品的高光谱采集和分类模型的机器学习技术。首先，风化过程按风化成因和机制分为初始风化阶段、加速风化阶段和稳定风化阶段。其次，提出了一种基于风化阶段的风化程度分类新方法。然后，根据光谱吸收特征的变化规律，可以在可见光和近红外光谱范围（400-1000 nm）建立脱落样品的显微特征与高光谱图像之间的映射关系。接下来，利用随机森林模型对不同风化程度建筑砂岩的光谱数据进行分类。此外，随机森林模型的超参数通过灰狼优化算法进行优化以获得更好的性能。最后将训练好的模型应用于定量评价大型砂岩墙风化程度。整个风化评估过程值得推荐用于建筑砂岩的诊断和监测。