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Article

Self-Healing Performance Assessment of Bacterial-Based Concrete Using Machine Learning Approaches

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Laboratory for Track Engineering and Operations for Future Uncertainties (TOFU Lab), School of Engineering, University of Birmingham, Birmingham B152TT, UK
2
Department of Civil Engineering, School of Engineering, University of Birmingham, Birmingham B152TT, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Lenka Scheinherrová
Received: 24 February 2022 / Revised: 1 June 2022 / Accepted: 21 June 2022 / Published: 23 June 2022
Bacterial-based self-healing concrete (BSHC) is a well-known healing technology which has been investigated for a few decades for its excellent crack healing capacity. Nevertheless, considered as costly and time-consuming, the healing performance (HP) of concrete with various types of bacteria can be designed and evaluated only in laboratory environments. Employing machine learning (ML) models for predicting the HP of BSHC is inspired by practical applications using concrete mechanical properties. The HP of BSHC can be predicted to save the time and cost of laboratory tests, bacteria selection and healing mechanisms adoption. In this paper, three types of BSHC, including ureolytic bacterial healing concrete (UBHC), aerobic bacterial healing concrete (ABHC) and nitrifying bacterial healing concrete (NBHC), and ML models with five kinds of algorithms consisting of the support vector regression (SVR), decision tree regression (DTR), deep neural network (DNN), gradient boosting regression (GBR) and random forest (RF) are established. Most importantly, 22 influencing factors are first employed as variables in the ML models to predict the HP of BSHC. A total of 797 sets of BSHC tests available in the open literature between 2000 and 2021 are collected to verify the ML models. The grid search algorithm (GSA) is also utilised for tuning parameters of the algorithms. Moreover, the coefficient of determination (R2) and root mean square error (RMSE) are applied to evaluate the prediction ability, including the prediction performance and accuracy of the ML models. The results exhibit that the GBR model has better prediction ability (R2GBR = 0.956, RMSEGBR = 6.756%) than other ML models. Finally, the influence of the variables on the HP is investigated by employing the sensitivity analysis in the GBR model. View Full-Text
Keywords: machine learning-aided prediction; self-healing concrete; bacterial-based self-healing concrete; K-fold cross validation; autonomous healing concrete machine learning-aided prediction; self-healing concrete; bacterial-based self-healing concrete; K-fold cross validation; autonomous healing concrete
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MDPI and ACS Style

Huang, X.; Sresakoolchai, J.; Qin, X.; Ho, Y.F.; Kaewunruen, S. Self-Healing Performance Assessment of Bacterial-Based Concrete Using Machine Learning Approaches. Materials 2022, 15, 4436. https://0-doi-org.brum.beds.ac.uk/10.3390/ma15134436

AMA Style

Huang X, Sresakoolchai J, Qin X, Ho YF, Kaewunruen S. Self-Healing Performance Assessment of Bacterial-Based Concrete Using Machine Learning Approaches. Materials. 2022; 15(13):4436. https://0-doi-org.brum.beds.ac.uk/10.3390/ma15134436

Chicago/Turabian Style

Huang, Xu, Jessada Sresakoolchai, Xia Qin, Yiu F. Ho, and Sakdirat Kaewunruen. 2022. "Self-Healing Performance Assessment of Bacterial-Based Concrete Using Machine Learning Approaches" Materials 15, no. 13: 4436. https://0-doi-org.brum.beds.ac.uk/10.3390/ma15134436

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