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

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Bouras, Yanni ORCID: 0000-0001-7954-8754 and Li, Le ORCID: 0000-0003-4600-2100 (2023) Utilisation of Machine Learning Techniques to Model Creep Behaviour of Low-Carbon Concretes. Buildings, 13 (9). ISSN 2075-5309


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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Item type Article
URI https://vuir.vu.edu.au/id/eprint/47204
DOI 10.3390/buildings13092252
Official URL https://www.mdpi.com/2075-5309/13/9/2252
Subjects Current > FOR (2020) Classification > 4005 Civil engineering
Current > Division/Research > Institute for Sustainable Industries and Liveable Cities
Keywords machine learning, creep behavior, low carbon concrete, concrete, fly ash, slag, cement
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