Modeling and analysis of creep in concrete containing supplementary cementitious materials based on machine learning

Liu, Yunze; Li, Yue; Mu, Jinlei; Li, Hongwen; Shen, Jiale

doi:10.1016/j.conbuildmat.2023.131911

Cited by 18 publications

(7 citation statements)

References 56 publications

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“…Interestingly, there are differences in the significance rankings of parameters 𝑡, 𝐴 and 𝑉/𝑆. Aggregate content was reported to have a major influence on creep compliance prediction, with the volumeto-surface ratio showing low importance in [27]. The opposite effect of these two parameters is observed herein.…”

Section: Shapley Additive Explanation (Shap) Analysismentioning

confidence: 60%

“…The opposite effect of these two parameters is observed herein. Furthermore, time since loading was only the 6th most influential parameter in[27], whereas in this analysis, it is determined to be the most significant. These differences may be due to the adoption of different input variables and datasets adopted for model training.…”

mentioning

confidence: 82%

“…The impact of adopting relative humidity H0 during preconditioning as an input variable on model accuracy is explored in the Sensitivity Analysis presented in Section 5. Table 2 compares the input variables adopted herein with those considered in studies in the literature on training ML models to predict the creep in concrete containing SCMs [24,25,27] and the ACI and B3 creep equations. It can be seen that mix proportions, compressive strength, t 0 , RH, and V/S are commonly considered.…”

Section: Concrete Creep Database and Input Variablesmentioning

confidence: 99%

“…Slag content S was important for predicting creep in slag concrete, whereas fly ash content showed a low impact on creep for fly ash concrete. Liu et al [27] also observed the low importance of SCM content when compared to other variables on creep prediction in concrete through Shapley value additive analysis. However, the significance of slag content appears to be higher in the analysis presented herein.…”

Section: Shapley Additive Explanation (Shap) Analysismentioning

confidence: 99%

“…A modified ACI model that considers the effect of SCMs on creep strain was developed by Chen et al [26] using particle-swarm optimisation. Liu et al [27] analysed creep in concrete containing SCMs using linear regression, support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGB) techniques. A large database was constructed using experimental results reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Utilisation of Machine Learning Techniques to Model Creep Behaviour of Low-Carbon Concretes

Bouras,

2023

Buildings

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Low-carbon concrete mixes that incorporate high volumes of fly ash and slag as cement replacements are becoming increasingly more common as part of efforts to decarbonise the construction industry. Though environmental benefits are offered, concretes containing supplementary cementitious materials exhibit different creep behaviour when compared to conventional concrete. Creep can significantly impact long-term structural behaviour and influence the overall serviceability and durability of concrete structures. This paper develops a creep compliance prediction model using supervised machine learning techniques for concretes containing fly ash and slag as cement substitutes. Gaussian process regression (GPR), artificial neural networks (ANN), random forest regression (RFR) and decision tree regression (DTR) models were all considered. The dataset for model training was developed by mining relevant data from the Infrastructure Technology Institute of Northwestern University’s comprehensive creep dataset in addition to extracting data from the literature. Holdout validation was adopted with the data partitioned into training (70%) and validation (30%) sets. Based on statistical indicators, all machine learning models can accurately model creep compliance with the RFR and GPR found to be the best-performing models. The sensitivity of the GPR model’s performance to training repetitions, input variable selection and validation methodology was assessed, with the results indicating small variability. The importance of the selected input variables was analysed using the Shapley additive explanation. It was found that time was the most significant parameter, with loading age, compressive strength, elastic modulus, volume-to-surface ratio and relative humidity also showing high importance. Fly ash and silica fume content featured the least influence on creep prediction. Furthermore, the predictions of the trained models were compared to experimental data, which showed that the GPR, RFR and ANN models can accurately reflect creep behaviour and that the DTR model does not give accurate predictions.

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Section: Shapley Additive Explanation (Shap) Analysismentioning

confidence: 60%

mentioning

confidence: 82%