Explainable hybrid machine learning approach for mechanical performance of 3D-printed strainhardening cementitious composites (3DP-SHCC)

Katlav M., Türk K.

Journal of Sustainable Cement-Based Materials, cilt.15, sa.5, ss.1-31, 2026 (SCI-Expanded, Scopus)

Özet

This paper presents an innovative and explainable AI framework for predicting the mechanical performance of threedimensionally

printed strain-hardening cementitious composites (3DP-SHCC), focusing on compressive strength (CS)

and flexural strength (FS). A rigorously curated database of 202 mechanical performance records collected from stateof-

the-art literature was used to develop AI models. To enhance robustness and overcome the limitations of

conventional tuning methods, GBM was integrated with four metaheuristic optimization algorithms—GWO, WOA,

HHO, and SSA—combined with five-fold cross-validation. The outcomes show that HHO-GBM achieved the highest

accuracy for CS prediction (R2 ¼ 0.951) during the test phase, while SSA-GBM performed best for FS prediction (R2

¼ 0.897). SHAP and ICE analyses identified binder composition, loading direction, and fiber parameters as key

drivers. Additionally, a user-friendly, real-time decision-support interface was developed and validated using

independent unseen mixtures. Overall, the proposed framework offers a reliable and engineering-ready tool for 3DPSHCC

design.