Numerous efforts have been made to determine the five independent elastic constants of transversely isotropic (TI) rocks. Recently, the novel strip load test method combined with the strain inversion method, offering the advantage of requiring only a single-orientation core, was presented (Yim J, Hong S, Lee Y, Min K–B. A novel method to determine five elastic constants of a transversely isotropic rock using a single-orientation core by strip load test and strain inversion. Int J Rock Mech Min Sci. 2022; 154:105115.¹). As a follow-up study, this paper suggests artificial neural networks (ANNs) to replace the strain inversion for determining five elastic constants of TI rocks with a strip load test method. The method comprises three main parts; the first was the strip load test experiment, the second part involves training ANNs using numerous datasets, and the final part is the application of trained ANNs to determine the elastic constants. The proposed method was numerically validated based on homogeneous and heterogeneous TI rocks. Experimental validation using Asan gneiss showed that the elastic constants determined from ANNs are in good agreement with those determined by using strain inversion and conventional method. The ANNs suggested in this study can significantly reduce the computing time required for strain inversion by numerical modelling and can be potentially used for other stress analyses of anisotropic rock.

为了确定横观各向同性 (TI) 岩石的五个独立弹性常数，人们做出了许多努力。最近，提出了一种结合应变反演方法的新型条带载荷测试方法，具有仅需要单向芯的优点（Yim J、Hong S、Lee Y、Min K-B。一种确定五种新方法的方法通过条带载荷试验和应变反演使用单向岩心的横向各向同性岩石的弹性常数。Int J Rock Mech Min Sci . 2022; 154:105115. ¹). 作为后续研究，本文建议用人工神经网络 (ANN) 代替应变反演，用条带载荷试验方法确定 TI 岩石的五个弹性常数。该方法包括三个主要部分；第一部分是条带负载测试实验，第二部分涉及使用大量数据集训练人工神经网络，最后一部分是应用训练好的人工神经网络来确定弹性常数。基于均质和非均质 TI 岩石对所提出的方法进行了数值验证。使用牙山片麻岩的实验验证表明，由 ANN 确定的弹性常数与使用应变反演和常规方法确定的弹性常数非常一致。