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Volume 13 | Issue 5 | Year 2026 | Article Id. IJCE-V13I5P128 | DOI : https://doi.org/10.14445/23488352/IJCE-V13I5P128

A Comprehensive Review on Strength Prediction and Self-Healing Properties of Bacterial Concrete using Machine Learning and Optimization Algorithms

S. Packialakshmi, PrajeeshaM.P

Received Revised Accepted Published
19 Feb 2026 06 Apr 2026 14 Apr 2026 30 May 2026

Citation :

S. Packialakshmi, PrajeeshaM.P, "A Comprehensive Review on Strength Prediction and Self-Healing Properties of Bacterial Concrete using Machine Learning and Optimization Algorithms," International Journal of Civil Engineering, vol. 13, no. 5, pp. 397-408, 2026. Crossref, https://doi.org/10.14445/23488352/IJCE-V13I5P128

Abstract

Bacterial concrete is an innovative self-healing material that deals with durability challenges in traditional concrete using Microbial Induced Calcium Carbonate Precipitation (MICP). Recent advances in optimization and ML algorithms have enabled accurate prediction of healing and strength efficiency, decreasing reliance on costly trial-and-error experimentation. This review offers present research on predictive modelling approaches, including ensemble methods, hybrid ANN, regression models, and ensemble methods optimized with PSO. A comparative study has been performed and emphasized the better performance of ANN-PSO in identifying non-linearities and mix design parameters optimization. PSO-like optimization algorithms improve sustainability and predictive accuracy. The key challenges may include variability, limited interpretability, and data scarcity of black box models. Future directions highlight the integration of recent technologies. This review emphasized the transformative potential of ML-driven optimization in advancing bacterial concrete as a sustainable and durable construction material.

Keywords

Bacterial Concrete, Machine Learning, Optimization Algorithms, Self-Healing Properties, Crack Healing.

References

  1. Henk M. Jonkers et al., “Application of Bacteria as Self-healing Agent for the Development of Sustainable Concrete,” Ecological Engineering, vol. 36, no. 2, pp. 230-235, 2010.
    [    CrossRef] [Google Scholar] [Publisher Link]
  2. J.Y. Wang, N. De Belie, and W. Verstraete, “Diatomaceous Earth as a Protective Vehicle for Bacteria Applied for Self-healing Concrete,” Journal of Industrial Microbiology & Biotechnology, vol. 39, no. 4, pp. 567-577, 2012.
    [    CrossRef] [Google Scholar] [Publisher Link]
  3. Weina Meng and Kamal H. Khayat, “Effect of Graphite Nanoplatelets and Carbon Nanofibers on Rheology, Hydration, Shrinkage, Mechanical Properties, and Microstructure of UHPC,” Cement and Concrete Research, vol. 105, pp. 64-71, 2018.
    [    CrossRef] [Google Scholar] [Publisher Link]
  4. Shekhar Saxena, and Ajay R. Tembhurkar, “Microbiological Induced Calcium Carbonate Process to Enhance the Properties of Cement Mortar,” Materials Today: Proceedings, vol. 21, no. 2, pp. 1350-1354, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
  5. SalmabanuLuhar, Ismail Luhar, and Faiz Uddin Ahmed Shaikh, “A Review on the Performance Evaluation of Autonomous Self-Healing Bacterial Concrete: Mechanisms, Strength, Durability, and Microstructural Properties,” Journal of Composites Science, vol. 6, no. 1, pp. 1-35, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  6. El ˙zbietaStanaszek-Tomal, “Bacterial Concrete as a Sustainable Building Material?,” Sustainability, vol. 12, no. 2, pp. 1-13, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  7. C. Edvardsen, “Water Permeability and Autogenous Healing of Cracks in Concrete,” ACI Materials Journal, vol. 96, no. 4, pp. 448-454, 1999.
    [    CrossRef] [Google Scholar] [Publisher Link]
  8. Rafat Siddique and Navneet Kaur Chahal, “Effect of Ureolytic Bacteria on Concrete Properties,” Construction and Building Materials, vol. 25, no. 10, pp. 3791-3801, 2011.
    [    CrossRef] [Google Scholar] [Publisher Link]
  9. Ahmed Ramadan Suleiman and Moncef L. Nehdi, “Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network,” Materials, vol. 10, no. 2, pp. 1-15, 2017.
    [    CrossRef] [Google Scholar] [Publisher Link]
  10. Lorena Skevi et al., “Incorporation of Bacteria in Concrete: The Case against MICP as a Means for Strength Improvement,” Cement and Concrete Composites, vol. 120, pp. 1-28, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  11. Krishnapriya, S., Venkatesh Babu, D.L., and Prince Arulraj, G. “Isolation and Identification of Bacteria to Improve the Strength of Concrete.” Microbiological Research, vol. 174, pp. 48-55, 2015.
    [    CrossRef] [Google Scholar] [Publisher Link]
  12. V. Nagarajan et al., “A Study on the Strength of the Bacterial Concrete Embedded with Bacillus Megaterium,” International Research Journal of Engineering and Technology, vol. 4, no. 12, pp. 1784-1788, 2017.
    [    Google Scholar] [Publisher Link]
  13. Ahmad Alyaseenet al., “Application of Soft Computing Techniques for the Prediction of Splitting Tensile Strength in Bacterial Concrete,” Journal of Structural Integrity and Maintenance, vol. 8, no. 1, pp. 26-35, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  14. Parviz Soroushian, and Mohamed Elzafraney, “Damage Effects on Concrete Performance and Microstructure,” Cement and Concrete Composites, vol. 26, no. 7, pp. 853-859, 2004.
    [CrossRef] [Google Scholar] [Publisher Link]
  15. Md. Mahfuzul Islam et al., “An Experimental Study on the Strength and Crack Healing Performance of E. Coli Bacteria-Induced Microbial Concrete,” Advances in Civil Engineering, vol. 2022, no. 1, 1-13, 2022. 
    [CrossRef] [Google Scholar] [Publisher Link]
  16. Kennedy C. Onyelowe et al., “Modeling the Influence of Bacteria Concentration on the Mechanical Properties of Self-Healing Concrete (SHC) for Sustainable Bio-Concrete Structures,” Scientific Reports, vol. 14, no. 1, pp. 1-40, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  17. Fadi Almohammed et al., “Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete,” Materials, vol. 15, no. 2, pp. 1-17, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  18. Alexey N. Beskopylny et al., “Concrete Strength Prediction using Machine Learning Methods: CatBoost, K-Nearest Neighbors, Support Vector Regression,” Applied Sciences, vol. 12, no. 21, pp. 1-19, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  19. Salwa R. Al-Taai et al., “XGBoost Prediction Model Optimized with Bayesian for the Compressive Strength of Eco-Friendly Concrete Containing Ground Granulated Blast Furnace Slag and Recycled Coarse Aggregate,” Applied Sciences, vol. 13, no. 15, pp. 1-23, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  20. Yajian Wang et al., “Predicting the Microbiologically Induced Concrete Corrosion in Sewer based on XGBoost Algorithm,” Case Studies in Construction Materials, vol. 17, pp. 1-17, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  21. Carolina Luiza Emerenciana Pessoa, Victor Hugo Peres Silva, and Ricardo Stefani, “Prediction of the Self-Healing Properties of Concrete Modified with Bacteria and Fibers using Machine Learning,” Asian Journal of Civil Engineering, vol. 25, no. 2, pp.1801-1810, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  22. M. Meghashree et al., “Advancing Sustainable Concrete with Bacterial Self-Healing Technology and Kuhn-Tucker Condition,” Scientific Reports, vol. 15, pp. 1-19, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  23. Anumol Sukumaran et al., “Assessment of Strength and Self-Healing Properties of Bacterial Concrete using Machine Learning Techniques and Microstructural Characterization,” Journal of Building Pathology and Rehabilitation, vol. 11, no. 1, 2026.
    [    CrossRef] [Google Scholar] [Publisher Link]       
  24. Yuanfeng Lou et al., “Predicting the Crack Repair Rate of Self-healing Concrete using Soft-Computing Tools,” Materialstoday Communications, vol. 38, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  25. Jing Luo et al., “Interactions of Fungi with Concrete: Significant Importance for Bio-Based Self-Healing Concrete,” Construction and Building Materials, vol. 164, pp. 275-285, 2018.
    [    CrossRef] [Google Scholar] [Publisher Link]
  26. Nageswari Nagarajan, Divahar Ravi, and Preethi Elangovan, “AI-Driven Prediction Framework for Nano–Bio Self-Healing Concrete: Integrating Nano Biomass Silica and Bacterial Admixture for Sustainable Structural Materials,” Construction and Building Materials, vol. 505, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  27. Bin Xi et al., “Predicting Ultra High-Performance Concrete Self-Healing Performance using Hybrid Models based on Metaheuristic Optimization Techniques,” Construction and Building Materials, vol. 381, pp. 1-16, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  28. Sameh Fuqaha, “Artificial Intelligence for Sustainable Self-Healing Concrete Design Through Evolutionary Algorithms,” An-Najah University Journal for Research - A (Natural Sciences), vol. 40, no. 4, pp. 1-14, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  29. Narayana Harish et al., “Particle Swarm Optimization based Support Vector Machine for Damage Level Prediction of Non-Reshaped Berm Breakwater,” Applied Soft Computing, vol. 27, pp. 313-321, 2015.
    [    CrossRef] [Google Scholar] [Publisher Link]
  30. Xinghua Fan, Shasha Li, and Lixin Tian,” Chaotic Characteristic Identification for Carbon Price and A Multi-Layer Perceptron Network Prediction Model”. Expert Systems with Applications, 42(8), pp.3945–3952. 2015.
    [    CrossRef] [Google Scholar] [Publisher Link]
  31. Yanqi Wu, and Yisong Zhou, “Prediction and Feature Analysis of Punching Shear Strength of Two-Way Reinforced Concrete Slabs using Optimized Machine Learning Algorithm and Shapley Additive Explanations,” Mechanics of Advanced Materials and Structures, vol. 30, no. 15, pp. 3086-3096, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  32. Xu Huang et al., “Self-Healing Performance Assessment of Bacterial-Based Concrete using Machine Learning Approaches,” Materials, vol. 15, no. 13, pp. 1-16, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  33. Tiana Milović et al., “Enhancing Compressive Strength of Cement by Indigenous Individual and Co-Culture Bacillus Bacteria,” Materials, vol. 17, no. 20, pp. 1-20, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  34. Hassan Amer Algaifi et al., “Machine Learning and RSM Models for Prediction of Compressive Strength of Smart Bio-Concrete,” Smart Structures and Systems, vol. 28, no. 4, pp. 535-551, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
  35. Kunamineni Vijay, and Meena Murmu, “Application of Artificial Neural Networks for Prediction of Microbial Concrete Compressive Strength,” Journal of Building Pathology and Rehabilitation, vol. 7, no. 1, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  36. Yuanfeng Lou et al., “Predicting the Crack Repair Rate of Self-healing Concrete using Soft-computing Tools,” Materialstoday Communications, vol. 38, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  37. K. Shanthi Sri, R. Ramesh Nayaka, and M.V.N. Siva Kumar “Mechanical Properties of Sustainable Self-Healing Concrete and its Performance Evaluation using ANN and ANFIS Models,” Journal of Building Pathology and Rehabilitation, vol. 8, no. 2, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  38. Wanmao Zhang et al., “An Intelligent Approach for Predicting the Strength of Roadbed Foam Lightweight Concrete based on an Optimized XGBoost Model,” Case Studies in Construction Materials, vol. 22, pp. 1-19, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  39. Md. Habibur Rahman Sobuz et al., “Machine Learning-Based Modeling to Predict and Parametrically Optimize the Compressive Strength of Nanomaterial Concrete Composites Exposed to Elevated Temperatures,” Case Studies in Construction Materials, vol. 23, pp. 1-25, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  40. Mallikarjuna Reddy V et al., “ML Prediction and ANN-PSO based Optimization for Compressive Strength of Blended Concrete,” Cogent Engineering, vol. 11, no. 1, pp. 1-13, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  41. Youcef Chakali  et al., “A PSO-ANN Intelligent Hybrid Model to Predict the Compressive Strength of Limestone Fillers Roller Compacted Concrete (RCC) to Build Dams,” KSCE Journal of Civil Engineering, vol. 25, no. 8, pp. 3008-3018, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]