Finite element simulation of bacterial self-healing in concrete using microstructural transport and precipitation modeling

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 6, 2025

Abstract Bacteria-based self-healing concrete has emerged as a promising solution for enhancing structural durability by autonomously repairing cracks. However, the underlying transport mechanisms of healing agents and efficiency mineral precipitation remain inadequately modelled. This study presents finite element modelling (FEM) approach to simulate diffusion reaction kinetics bacterial in microstructures. X-ray micro-computed tomography (Micro-CT) meshes were utilized accurately represent crack pore geometries, while diffusion-reaction equation governing calcium carbonate (CaCO 3 ) was numerically solved using FEniCS. Key input parameters, including coefficients, rates, efficiencies, extracted from literature ensure model validation. Simulations reveal that agent concentration follows nonlinear pattern, with influenced geometry metabolic activity. Heatmaps contour plots highlight dispersion, time-dependent analysis indicates 65.5% closure under optimal conditions. The proposed effectively replicates experimental trends, demonstrating its applicability predicting performance realistic provides computational framework can be extended optimize bacteria encapsulation strategies, kinetics, long-term assessments concrete.

Язык: Английский

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Revolutionizing concrete: Unveiling bio-concrete's advantages and challenges in self-healing through microbial-induced calcium carbonate precipitation

Sustainable materials and technologies, Год журнала: 2025, Номер unknown, С. e01465 - e01465

Опубликована: Май 1, 2025

Язык: Английский

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On the use of Synthetic Data for Machine Learning prediction of Self-Healing Capacity of Concrete

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июль 31, 2024

This work investigated the use of synthetic data to overcome limitations scarce experimental in predicting self-healing capacity bacteria-driven concrete. We generated a dataset based on real-world data, significantly expanding original and then trained compared machine learning models, including probabilistic ensemble methods, predict concrete capacity. The results demonstrate that particularly random forest (RF) method (accuracy = 0.863 F1-score 0.863), outperformed models achieved high accuracy were further applied real-word examples, showing accuracy. research validates utility modelling reliability civil engineering, areas with limited data. findings contribute growing ML AI transformative potential addressing challenges engineering.

Язык: Английский

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Bio-inspired synthesis of nanocrystalline calcite demonstrating significant improvement in mechanical properties of concrete: a construction-nanobiotechnology approach

Environmental Science and Pollution Research, Год журнала: 2024, Номер unknown

Опубликована: Сен. 17, 2024

Язык: Английский

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