Explainable Machine Learning Analysis of Temperature Effects on Mechanical Strength of Fiber-Reinforced High-Performance Concrete
Paper recorded by Signals 4 on 2026-10-16 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-16 on arXiv · recorded by Signals 4 on 2026-10-19
Category: cs.LG · 机器学习 · first seen 2026-10-19
Abstract
Predicting the compressive strength of fiber-reinforced high-performance concrete subjected to temperature exposure is challenging because to the intricate interdependencies of mixture composition, fiber properties, curing conditions, and temperature. A data-driven framework to predict compressive strength utilizing 1,204 experimental records containing a diverse array of material designs and expo
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Cite this page: Explainable Machine Learning Analysis of Temperature Effects on Mechanical Strength of Fiber-Reinforced High-Performance Concrete: the #255 most recent of 749 cs.LG papers we have recorded (as of 2026-10-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/explainable-machine-learning-analysis-of-temperature-effects-on-mechanical-stren.html
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