Dual-Objective XGBoost Prediction Model for the Cementation Performance of MICP-Treated Sandy Soil in Small-Sample Scenarios
Pingan Tang1, Guang Zhu1, Junjun Xu1, Chaojian Yue1, Wei He1, Jun Sun2, Chen Zeng3,*
1 China State Construction International Engineering Limited, Wuhan, China
2 School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China
3 School of Civil Engineering and Architecture, Wuhan Polytechnic University, Wuhan, China
* Corresponding Author: Chen Zeng. Email: 
(This article belongs to the Special Issue: Durability Assessment of Engineering Structures and Advanced Construction Technologies)
Structural Durability & Health Monitoring https://doi.org/10.32604/sdhm.2026.084032
Received 15 April 2026; Accepted 25 June 2026; Published online 29 July 2026
Abstract
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.
Keywords
Microbially induced carbonate precipitation; small-sample prediction; XGBoost; feature engineering; unconfined compressive strength; calcium carbonate content
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