Prediction of Joint Roughness Coefficient Using Metaheuristic-Optimized Random Forest Model

Reliable estimation of the joint roughness coefficient (JRC) is important for quantitative characterization of joint roughness and provides an important geometric parameter for subsequent joint shear strength assessment. However, single-parameter empirical models cannot fully capture the combined geometric characteristics of joint profiles. In this study, a dataset containing 113 joint profiles was compiled. Seven morphological statistical parameters, namely SF, SDh, iave, SDi, Rmax, Rp, and Z2, were used as model inputs. The Sparrow Search Algorithm (SSA) and Harris Hawks Optimization (HHO) were employed to search for more suitable hyperparameter configurations of Random Forest (RF). Based on this framework, RF, SSA-RF, and HHO-RF models were developed for JRC prediction. HHO-RF model achieved the best test performance, with an R2 of 0.935 and an RMSE of 1.152. Its RMSE was 27.64% lower than that of the RF model, and it also outperformed SSA-RF and three single-parameter empirical models. Shapley Additive Explanations (SHAP) analysis identified SDh, Rmax, and SDi as the most influential inputs, accounting for 62.0% of the total mean absolute SHAP value. This ranking highlights the importance of overall elevation dispersion, maximum local relief, and inclination variability in distinguishing profiles with different roughness levels. By integrating complementary information from multiple morphology descriptors, the proposed HHO-RF model provides an accurate and interpretable approach for rapid JRC estimation from two-dimensional joint profiles. The reported performance applies primarily to two-dimensional joint profiles within the morphological range represented by the present dataset, while broader applicability requires further validation.