A Review of Three Common Concrete Multiaxial Strength Criteria from 2010 to 2020

Chen, Xingqiao; Zheng, Dongjian; Wu, Xin; Li, Chenyang

doi:10.1007/s11831-022-09822-8

Cited by 9 publications

(1 citation statement)

References 61 publications

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“…The multiaxial strength criteria of concrete has attracted significant attention in the literature. The failure of concrete materials under multiaxial stress was discussed in [26], underscoring the substantial attention this problem has received from the scientific community leading to several experimental studies and research. Exploration into the multiaxial compressive strength of hybrid fiber reinforced concrete (HFRC) provided insights into the uniaxial compressive and tensile strength of HFRC, emphasizing the role of steel and polypropylene fibers [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Comprehensive analysis of machine learning models for predicting concrete compressive strength

Saidjon Kamolov

2024

AMCS

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Predicting the compressive strength of concrete provides several benefits including insights into its durability, potential lifespan, and its suitability for specific construction projects. Recent advances in machine learning algorithms have prompted their application to compressive strength modeling and prediction. In this paper, we carry out a comprehensive comparative analysis of the existing machine learning algorithms. Unlike, other current studies in this domain, we consider the entire spectrum of algorithms ranging from basic linear models to advanced meta-learning methods. The results show that there exists a trade-off between speed and accuracy of the models. We find that while ensemble models such as stacking and gradient boost provide superior accuracy, they are significantly slower than linear models.

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Section: Literature Reviewmentioning

confidence: 99%