Ultimate Bearing Capacity of Engineered Bamboo Columns of Varying Lengths Under Eccentric Compression: Data-Driven Modeling

This study investigates the ultimate bearing capacity of engineered bamboo columns of varying lengths under eccentric loading using a data-driven machine learning (ML) approach. Although previous studies have investigated engineered bamboo columns under axial or eccentric compression, few have systematically examined the combined effects of load eccentricity and column length. A database containing 432 records was established, with eccentricity, column length, cross-sectional area, compressive strength, and elastic modulus used as input features. Six tree-based ML algorithms were used to develop predictive models, all of which achieved satisfactory predictive accuracy. Shapley additive explanations analysis indicated that the trained random forest (RF) and extreme gradient boosting (XGBoost) models relied most strongly on eccentricity, followed by compressive strength, cross-sectional area, and column length, whereas elastic modulus made the smallest contribution within the range covered by the database. A new functional form was proposed for predicting the ultimate bearing capacity based on the eccentricity ratio, slenderness ratio, stability factor, compressive strength, and cross-sectional area. Two ML-assisted explicit models were developed using predictions generated by the trained RF and XGBoost models. Validation against both the developed database and an independent dataset showed that the explicit model derived from the XGBoost predictions achieved higher predictive accuracy than the RF-based model.