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Review Jul 2026

A Critical Review of Engineering Properties of Soil Treated with Microbially Induced Calcium Carbonate Precipitation (MICP)

Microbially induced calcium carbonate precipitation (MICP) is a process that leverages microbial metabolic activity to facilitate biochemical interactions with surrounding chemical compounds, resulting in the precipitation of calcium carbonate. This mineralization process enhances soil properties by filling pores, binding soil particles, and reducing permeability, resulting in a significant increase in soil strength and stability. MICP holds great potential for various geotechnical and environmental engineering applications, including mitigating soil liquefaction, stabilizing erosion-prone slopes, remediating contaminated soils, and controlling seepage in infrastructure such as dams and tunnels. This review paper provides a concise overview of the cementation mechanism involved in MICP and the urea hydrolysis process, based on recent research findings. Key factors in evaluating the engineering properties of MICP-treated soils include compressive strength, permeability, stiffness, durability, volumetric behavior, shear wave velocity, and microstructural characteristics. Future research directions are discussed to guide the further development of MICP technology, focusing on the cost-effective bacterial strains, improved treatment uniformity, and the durability of MICP-treated soils under combined erosion and leachate conditions.

Neeraj Kumar, Arvind Kumar · 0 citations
Open access Aug 2026

Predictive modeling and design optimization of concrete properties reinforced with basalt fiber under harsh environment

Basalt fiber reinforced concrete (BFRC) has recently attracted increased attention for improving durability, mechanical strength, and chemical resistance concerning harsh environmental conditions. The most noticeable gap is left in being able to predict long-term performance accurately and optimize that performance because of the complexities that arise from the multiscale interactions between fibers, matrix, and environmental stressors. This study, therefore, offers a highly unified and multiscale machine learning framework by pulling together five disaggregated analytical models into a single predictive-optimization pipeline prearranged for basalt fiber reinforced concrete. The physics-augmented graph attention transformer network (P-GATNet) is expected to embed interfacial physics within graph-based message passing to capture load-driven mechanical responses, resulting in highly accurate strength and fracture evolution predictions (e.g., flexural R² ≈ 0.97). The spectral decomposition assisted degradation model uses Hilbert-Huang-based spectral analysis, which then decouples degradation mechanisms for accurately forecasting alkali resistance and damage kinetics with an error of less than 4.5%. The multi-agent physics reinforcement optimizer (MAPRO) jointly optimizes the strength and chemical performance by modeling competing failure mechanisms via cooperative agents. For improved representation of features, the deep morphological encoder with multi-modal fusion (DME-MMF) marries image-derived morphological embeddings with experimental tabular data, thus enhancing the interpretability and accuracy of predictions. Lastly, the transformer-based inverse composite generator enables reverse material design by producing feasible basalt fiber reinforced concrete formulations that satisfy predetermined strength and durability targets at an approximate success rate of 93%. This approach improves predictive fidelity, interpretability, and design in basalt fiber reinforced concrete.

V. Vairagade · 0 citations
Open access 2026

Dual-Objective XGBoost Prediction Model for the Cementation Performance of MICP-Treated Sandy Soil in Small-Sample Scenarios

: Microbially Induced Carbonate Precipitation (MICP) is an environmentally friendly technique for sandy soil stabilization. However, the cementation performance is governed by multiple coupled factors and complex experimental procedures, making accurate prediction challenging. In this study, a dual-objective XGBoost prediction model suitable for small-sample scenarios is developed from 77 sets of laboratory data to rapidly estimate unconfined compressive strength (UCS) and calcium carbonate content (CCC) separately. A mechanism-guided feature engineering strategy is adopted to construct three cross features, including urease activity coupled with curing time, calcium carbonate content combined with curing time, and urea-calcium concentration, together with five key influencing parameters. Five-fold cross-validation is used to ensure model stability. In the UCS model, soil particle size fraction (29.94%) and urease activity (20.85%) dominate, while in the CCC model, soil particle size fraction (22.47%) and urease activity (17.63%) prevail, both align well with fundamental MICP mechanisms. The CCC model achieves a coefficient of determination (R 2 ) of 0.6342 and a mean absolute error (MAE) of 2.92%, showing reliable predictive ability. The UCS model achieved an R 2 of 0.7991 and a MAE of 844.83 kPa. However, due to the mathematical amplification of relative error, a small portion of low-strength specimens produced abnormally high MAPE (116.60%), limiting the formal engineering design of the UCS model. SHAP (SHapley Additive exPlanations) analysis is further employed to enhance model interpretability and to quantitatively clarify the marginal contributions and interaction effects of the input features. The proposed framework offers a valuable reference for parameter analysis and mechanistic interpretation of MICP-treated soils. At the same time, the larger prediction deviation for UCS highlights the intrinsic uncertainty of strength evolution in such complex multi-factor systems.

Pingan Tang, Guang Zhu, Junjun Xu et al. · 0 citations
Oct 2026

Tensile Strength Enhancement of Soils Using Fiber and Biocementation: Effects of Treatment Method and Biotreated Fibers

The tensile behavior of soil is crucial for geotechnical design because tensile cracking can initiate soil erosion, slope failures, and structural failure, including embankments, dams, and retaining walls. These failures affect structural integrity and lead to environmental deterioration by enhancing soil permeability and promoting the movement of sediments and pollutants. Consequently, there is an increasing demand for sustainable techniques to improve soil tensile strength and provide long-term stability and sustainability. Based on this, in the current study, direct tensile tests were performed on a series of biocemented soil, fiber-reinforced soil, biocemented fiber–reinforced soil, and fiber-reinforced biocemented soil. The study was conducted using two different soils, fibers (jute and polypropylene), and treatment processes [microbial-induced calcite precipitation (MICP) and enzyme-induced calcite precipitation (EICP)]. Three different methods were adopted for biotreatment, namely, the direct mixing method (DMM), biotreated fiber mixing method (BFMM), and mixing-percolation method (MPM). The novel method of biotreatment of fibers was explored in this study. The tensile test results showed that the combined use of biocementation and fiber reinforcement significantly increased the peak tensile strength and ductility of soil. The increase in tensile strength was higher for specimens subjected to the MICP process than for those treated with the EICP process. Further, biotreatment of fiber offers a more practical and consistent approach for soil stabilization by improving the tensile strength of the soil.

C. Chauhan, D. Suresh, K. V. Uday · 0 citations
Conference Open access 2026

Bio-chemo-hydro-mechanical finite element modeling of microbially induced calcite precipitation for optimal treatment design

To overcome the limitations associated with conventional cement-based soil stabilization methods, Microbially Induced Calcite Precipitation (MICP) has emerged as a sustainable alternative for improving soil strength through bio-cementation. Although substantial research has been devoted to understanding, controlling, optimizing, and designing the MICP process, there is still a need for mechanistic yet practical tools that can support the accurate design of treatment strategies and reliably predict treatment performance. This study presents a novel theoretical framework based on a finite element code that couples biological, chemical, hydraulic, and mechanical processes to predict calcium carbonate precipitation and the corresponding improvements observed in both laboratory- and field-scale applications. This study presents a detailed parametric investigation using a fully coupled BCHM finite element model to assess the influence of cyclic treatment on the overall precipitation of calcium carbonate, which is one of the main objectives in sand stabilization. Different cementation solution injection cycles were examined to evaluate how increasing treatment intensity affects calcium carbonate precipitation and its spatial distribution within the column. The results provide valuable information for optimum design of MICP injection strategies.

Alireza Azizi, K. Atefi-Monfared · 0 citations
Review Open access Aug 2026

Machine learning based prediction and analysis of bond strength between steel reinforcement and geopolymer concrete under cyclic loading for seismic applications: a review

The bond behavior between steel reinforcement and geopolymer concrete under cyclic loading is a critical factor for the performance of reinforced concrete structures in seismic regions. Traditional empirical models often fail to capture the complex, nonlinear interactions at the steel concrete interface, particularly under repeated loading conditions. This systematic literature review aims to synthesize and critically analyze existing research on machine learning-driven approaches for predicting and interpreting bond strength in this context. We systematically identified and evaluated studies that develop, validate, or apply machine learning algorithms including artificial neural networks, support vector machines, and ensemble methods to model bond-slip relationships, failure modes, and degradation mechanisms. The review methodology involved a structured search and thematic analysis of peer-reviewed articles, focusing on how these models incorporate key variables such as concrete compressive strength, fiber reinforcement type, confinement conditions, and loading history. Our analysis reveals that Several reviewed studies reported improved predictive performance of machine learning models compared with selected empirical equations. However, differences in datasets, validation strategies, and performance metrics limit direct comparison among studies and prevent definitive conclusions regarding consistent superiority and generalizability., achieving higher accuracy in predicting bond strength under both monotonic and cyclic regimes. Furthermore, we found that feature importance analyses from these models provide new insights into the relative influence of material properties for instance, the critical role of fiber volumetric ratio and lateral confinement in mitigating bond degradation under reversed cyclic loads. The review also identifies significant gaps, including the scarcity of experimental datasets for high-magnitude seismic loading and the limited generalizability of models across different geopolymer mix designs. We conclude that machine learning offers a powerful framework for advancing bond strength prediction in geopolymer concrete systems, but future work must prioritize the development of robust, transferable models trained on more diverse, large-scale cyclic test data. These findings provide a foundation for more reliable seismic design guidelines and inform the selection of machine learning strategies for structural performance assessment.

Qaim Shah, Waheed Ali Khoso, Mussa Umali · 0 citations