Abstract
Self-healing concrete has been studied as an alternative material to overcome problems such as cracking and low durability of conventional concrete. However, laboratory experiments can be costly and time-consuming. Hence, machine learning algorithms can assist in the development of better formulations for self-healing concrete. In this work, machine learning (ML) models were developed using multiple linear regression (MLR), support vector machine (SVM) and random forest (RF) regression to predict and analyze the repair rate of the cracked area of self-healing concretes containing bacteria and fibers in their formulations. The results show that the radial-basis (RBF) SVM (R2 = 0.927, MAE = 0.053 and RMSE = 0.004) and RFG (R2 = 0.984, MAE = 0.019, RMSE = 0.000) algorithms performed better in predictions and delivered better-fitted models. Therefore, RF regressor and RBF SVM models can be applied to develop and validate high-performance self-healing concrete formulations based on polymeric fibers and bacteria.
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Data availability
Data and Jupyter Notebook are available at https://github.com/rstefaniufmt/cementML/. Supplementary Data are available on request.
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All authors contributed to the study conception and design. Data collection and analysis were performed by CEP, VHPS and RS. The first draft of the manuscript was written by CEP, and all authors commented on previous versions of the manuscript. RS was the supervisor of the work. All authors read and approved the final manuscript.
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Pessoa, C.L.E., Peres Silva, V.H. & Stefani, R. Prediction of the self-healing properties of concrete modified with bacteria and fibers using machine learning. Asian J Civ Eng 25, 1801–1810 (2024). https://doi.org/10.1007/s42107-023-00878-w
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DOI: https://doi.org/10.1007/s42107-023-00878-w