The evaluation of geological suitability for urban underground space (UUS) development is an indispensable prerequisite for its optimal utilization. As the actual carrier of underground facilities, the evaluation of rock mass quality plays a crucial role in assessing geological suitability. However, it is notable that the evaluation of rock mass quality has regrettably remained somewhat marginalized within the broader framework of the geological suitability assessment in recent years. The selection of pertinent indicators for the evaluation of rock mass quality inherently presents an appreciable degree of subjectivity. Predominantly subjective evaluation methods continue to dominate the field, while the application of objective algorithms, such as unsupervised clustering, remains in its nascent stage. Furthermore, there is a lack of comprehensive investigations into distinct combinations of attributes. This limitation confines the broader applicability of the evaluation outcomes in the context of urban underground space. Within this study, we meticulously amassed rock core test data from over 40 boreholes of engineering geological significance within the urban planning ambit of Guang'An City. Utilizing the K-means unsupervised clustering algorithm and the Principal Component Analysis (PCA) algorithm. We successfully conducted an unsupervised clustering procedure with nine distinct physical and mechanical attributes. This yielded an aggregation into five discernible clusters. Building upon the derived clustering outcomes, a stratification of rock mass quality was effectuated into three distinct tiers: Level 1 (characterized by pure sandstone), Level 2 (primarily dominated by sandstone), and Level 3 (denoting fair conditions predominantly influenced by mudstone). This structured stratification facilitates a relatively objective and comprehensive evaluation of rock mass quality within the context of the red-bed hilly terrain. In the course of this analytical trajectory, we conducted a dissection of the clustering efficacy. For strongly correlated attributes, we propose a preliminary dimensionality reduction procedure prior to the clustering endeavor. Moreover, we recommend intervals of 10 m for the stratified evaluation in red bed hilly urban terrains.

中文翻译：

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数据驱动的红层丘陵区岩体质量评价——以广安市为例

城市地下空间开发地质适宜性评价是城市地下空间优化利用不可或缺的前提。岩体作为地下设施的实际载体，其质量评价对于地质适宜性评价起着至关重要的作用。然而，值得注意的是，近年来，岩体质量评价在地质适宜性评价的更广泛框架内仍然处于边缘化状态。岩体质量评价相关指标的选择本质上具有相当大的主观性。以主观评估为主的方法继续在该领域占据主导地位，而客观算法（例如无监督聚类）的应用仍处于萌芽阶段。此外，缺乏对不同属性组合的全面研究。这种局限性限制了评价结果在城市地下空间背景下更广泛的适用性。在这项研究中，我们精心收集了广安市城市规划范围内 40 多个具有工程地质意义的钻孔的岩心测试数据。利用K-means无监督聚类算法和主成分分析（PCA）算法。我们成功地进行了具有九个不同物理和机械属性的无监督聚类过程。这产生了五个可辨别的簇的聚合。基于导出的聚类结果，岩体质量分层分为三个不同的等级：1 级（以纯砂岩为特征）、2 级（主要以砂岩为主）和 3 级（表示主要受泥岩影响的良好条件） 。这种结构化分层有利于对红层丘陵地形背景下的岩体质量进行相对客观和全面的评价。在这个分析轨迹的过程中，我们对聚类效果进行了剖析。对于强相关属性，我们在聚类工作之​​前提出了初步的降维程序。此外，我们建议在红层丘陵城市地形中分层评价的间隔为10 m。