Uniaxial compressive strength (UCS) is the most fundamental physico–mechanical parameter used for any rock mass classification in geotechnical and geological engineering. However, determining UCS is a very tough, expensive, time consuming and destructive method and requires experienced workers. On the other hand, P-wave velocity (V_P) determination is cheap, precise, non-destructive and easy. There are many established relationships between UCS and V_P but mostly are low in range or proposed for multiple rock types of different origin. In this paper, the correlation of UCS with V_P has been assessed based on the rocks' lithology. The methodology used in this analysis was centred on the previous studies database, lithology-based data disintegration and data integration to establish lithology based simple regression (SR) equations. A total of 37 previous studies databases were processed, and 12 characteristic regression equations have been determined based on the lithology. The lithological control was also determined using the principal component analysis (PCA), which categorised the data into diverse rock types. Artificial neural network (ANN) has been used as a robust predictive tool to estimate the UCS using the V_P and rock type information.

单轴抗压强度 (UCS) 是岩土工程和地质工程中用于任何岩体分类的最基本的物理力学参数。然而，确定 UCS 是一种非常困难、昂贵、耗时且具有破坏性的方法，并且需要有经验的工人。另一方面，P 波速度 ( V _P ) 的测定便宜、精确、无损且容易。UCS 和V _P之间有许多既定的关系，但大多是范围较小或针对不同来源的多种岩石类型提出的。在本文中，UCS 与V _{P的相关性}已根据岩石的岩性进行评估。该分析中使用的方法以先前的研究数据库、基于岩性的数据分解和数据集成为中心，以建立基于岩性的简单回归 (SR) 方程。共处理了37个前期研究数据库，根据岩性确定了12个特征回归方程。岩性控制也使用主成分分析 (PCA) 确定，该分析将数据分类为不同的岩石类型。人工神经网络 (ANN) 已被用作使用V _P和岩石类型信息来估计 UCS 的稳健预测工具。