Missingness-Aware Heterogeneous Ensemble Learning for Compression Index Prediction Across Predefined Incomplete-Input Scenarios and Unseen Marine-Clay Sites

Abstract

Reliable estimation of the compression index (Cc) is essential for settlement assessment, yet geotechnical databases often contain incomplete soil-index measurements. This study developed a missingness-aware heterogeneous ensemble that combined masked variables with binary availability indicators to predict Cc across eight predefined incomplete-input scenarios. The database comprised 1524 marine-clay specimens from eight coastal sites in South Korea. Five sites were used for model development and internal testing, while three sites were reserved for independent testing. A 12-dimensional representation allowed five artificial neural network seed models and four tree-based learners to process all scenarios using validation-derived weights. The proposed model achieved mean root mean square errors of 0.166 and 0.177 in the internal and independent tests, with corresponding coefficients of determination of 0.772 and 0.723. In the internal test, the proposed model produced more favorable point-estimate metric values than the case-specific artificial neural network and random forest baselines in all 32 comparisons and than XGBoost in 30 comparisons. The corresponding differences were less consistent in the independent test, and only six of the 24 unadjusted bootstrap confidence intervals remained entirely below zero. The framework provides a unified tool for preliminary Cc screening under the predefined incomplete-input scenarios evaluated in this study.