Predicting the 28-day compressive strength by mix proportions: Insights from a large number of observations of industrially produced concrete

Zhang, Xueqing; Akber, Muhammad Zeshan; Poon, Chi Sun; Zheng, Wei

doi:10.1016/j.conbuildmat.2023.132754

Cited by 6 publications

(2 citation statements)

References 46 publications

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“…For instance, the data distributions of input variables, such as the quantities of mixture constituents, can vary substantially between these two contexts. 64 As illustrated in Figure 10, the field data present a more spread-out distribution than the laboratory data, indicating more variability or a broader range of characteristics in its input variables. Furthermore, the target variables like compressive strength exhibit greater uncertainty in field data than in laboratory data due to less controlled conditions involving diverse local material resources and highly variable environmental conditions.…”

Section: Extrapolation Capabilitymentioning

confidence: 94%

Can domain knowledge benefit machine learning for concrete property prediction?

Li,

Pei,

Ying

et al. 2023

J Am Ceram Soc.

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Understanding and predicting process–structure–property–performance relationships for concrete materials is key to designing resilient and sustainable infrastructure. While machine learning has emerged as a powerful tool to supplement empirical analysis and physical modeling, its capabilities are yet to be fully realized due to the massive data requirements and generalizability challenges. To address these limitations, we propose a knowledge‐informed machine learning framework for concrete property prediction that aggregates the wealth of domain knowledge condensed in empirical formulas and physics‐based models. By integrating the knowledge through data augmentation, feature enhancement, and model pre‐training, we demonstrate that this framework has the potential to (i) accelerate model convergence, (ii) improve model performance with limited training data, and (iii) increase generalizability to real‐world scenarios (including extrapolation capability to other datasets and robustness against data outliers). The overall improvement of machine learning models by knowledge integration is particularly critical when these models are scaled up to tackle the increasing complexity of modern concrete and deployed in practical applications. While demonstrated for predicting concrete strength, this versatile framework is applicable to a wide range of properties of concrete and other composite materials, paving the way for accelerated materials design and discovery.

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Section: Extrapolation Capabilitymentioning

confidence: 94%

Can domain knowledge benefit machine learning for concrete property prediction?

Li,

Pei,

Ying

et al. 2023

J Am Ceram Soc.

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“…Several researchers have attempted to assess the varied performances of concrete using ML techniques. Zhang et al [29] predicted 28-day strength using industrial data for 12,107 concrete mixes, finding ML models outperformed previous laboratory-based models. Li et al [30] provided a comprehensive review on ML applications in concrete science, discussing implementation, strengths, limitations, and future directions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Progressive Frost Damage Development of Concrete Using Machine-Learning Algorithms

Atasham ul haq,

Xu,

Abid

et al. 2023

Buildings

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Understanding the deterioration mechanism of concrete structures in cold climates that are susceptible to frost damage from repeated freezing and thawing cycles is imperative for ensuring their durability and serviceability. This study analyzed the impact of freeze–thaw (FT) exposure on concrete structural behavior by developing three machine-learning approaches—artificial neural networks (ANN), random forests (RF), and support vector machines (SVM)—to quantify the progressive loss in compressive strength after repeated FT cycles. The results demonstrate that all of the proposed models can predict the deteriorated compressive strength of concrete and align closely with the experimental results. The ANN model demonstrated the highest prediction accuracy with an R2 of 0.924, exhibiting a higher prediction accuracy than RF and SVM models. Sensitivity analysis using Shapley additive explanations (SHAP) revealed that concrete with an initially high strength, along with a lower water–cement ratio and air entrainment, exhibited the least reduction in compressive strength after freezing–thawing cycles, underlining the positive impact of these factors on the FT durability of concrete. The proposed modeling approach accurately predicts the residual compressive strength after FT exposure, enabling the selection of optimal concrete materials and structural designs for cold climates.

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