Self-healing performance assessment of bacterial-based concrete using machine learning approaches

Xu Huang, Jessada Sresakoolchai, Xia Qin, Yiu Fan Ho, Sakdirat Kaewunruen

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Abstract

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.
Original languageEnglish
Article number4436
Number of pages16
JournalMaterials
Volume15
Issue number13
DOIs
Publication statusPublished - 23 Jun 2022

Bibliographical note

Funding Information:
Acknowledgments: The authors are sincerely grateful to the European Commission for the financial sponsorship of the H2020-RISE Project No. 691135 “RISEN: Rail Infrastructure Systems Engineering Network,” which enables a global research network that tackles the grand challenge in railway infrastructure resilience and advanced sensing in extreme environments (www.risen2rail.eu (accessed on 9 September 2021)) [60]. In addition, this project is partially supported by the European Commission’s Shift2Rail, H2020-S2R Project No. 730849 “S-Code: Switch and Crossing Optimal Design and Evaluation”. The APC has been sponsored by the University of Birmingham Library’s Open Access Fund.

Funding Information:
Funding: This research was funded by the European Commission, grant number: H2020-MSCA-RISE No. 691135 and Shift2Rail H2020-S2R Project No. 730849. The APC was funded by the University of Birmingham Library’s Open Access Fund.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • machine learning-aided prediction
  • self-healing concrete
  • bacterial-based self-healing concrete
  • K-fold cross validation
  • autonomous healing concrete

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