An active learning framework for modeling compressive strength of concrete: A case study on ultra-high performance geopolymer

Ho Anh Thu Nguyen

doi:10.1016/j.conbuildmat.2026.147654

An active learning framework for modeling compressive strength of concrete: A case study on ultra-high performance geopolymer

2026-08-12

0

PRE

AI

H

Ho Anh Thu Nguyen

M

Mingu Jeong

S

Simchoen Yuk

Y

Yonghan Ahn

H

Han‐Seung Lee

D

Duy Hoang Pham *

DOI：10.1016/j.conbuildmat.2026.147654

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• Effective sampling strategies for compressive strength modeling are developed based on active machine learning. • Uncertainty and Greedy X significantly reduce samples compared to traditional methods. • The framework is validated on ultra-high-performance geopolymer (UHPG). • The framework significantly reduces CO2 emissions, waste, and experimental duration.

An active learning framework for modeling compressive strength of concrete: A case study on ultra-high performance geopolymer

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