Spatially Resolved CPTu Monitoring of Enzyme-Induced Cementation Fronts

Clara Weiß; Jan L. Graf

Articles

Vol. 1 No. 4 (2026)

Spatially Resolved CPTu Monitoring of Enzyme-Induced Cementation Fronts

Clara Weiß^▸^▾
Jan L. Graf^▸^▾

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Submitted: August 16, 2026
Published: August 30, 2026

Abstract

The demand for sustainable and environmentally benign ground improvement techniques has driven significant advancements in biogeochemical soil stabilization methods. Enzyme-Induced Carbonate Precipitation has emerged as a particularly promising alternative to conventional Portland cement-based grouting, offering a lower carbon footprint and reduced ecological disturbance. However, the spatial variability of the precipitated calcium carbonate and the complex dynamics of the moving cementation front pose substantial challenges for quality assurance and field verification. This paper presents a comprehensive investigation into the application of Piezo-cone penetration testing for the spatially resolved monitoring of Enzyme-Induced Carbonate Precipitation cementation fronts in granular soils. Through large-scale calibration chamber experiments, the mechanical response of the biocemented soil was continuously profiled, allowing for the precise delineation of treated boundaries and transition zones. The research establishes robust correlations between localized penetration resistance, pore water pressure dissipation, and the spatially distributed calcium carbonate content. The findings demonstrate that high-resolution continuous profiling can effectively capture the heterogeneous architecture of the cementation front, providing crucial insights into injection efficiency and biocement distribution. This methodology offers a scalable and minimally invasive framework for real-time monitoring and verification of biogeochemical ground improvement processes in field applications.

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[1] 1. Ouyang, Z., & Mayne, P. W. (2018). Effective friction angle of clays and silts from piezocone penetration tests. Canadian Geotechnical Journal, 55(9), 1230-1247.

[2] 2. Yu, K., Ran, Y., Shi, J., Duan, M., & Ouyang, Z. (2025). Physical property of MICP-treated calcareous sand under seawater conditions by CPTU. Biogeotechnics, 3(1), 100131.

[3] 3. Mayne, P. W., Valsson, S. M., Nordal, S., Senneset, K., Ouyang, Z., & Greig, J. (2026). Friction angle of sandy soils from CPT using the NTH limit plasticity solution. Canadian Geotechnical Journal, 63, 1-18.

[4] 4. Ouyang, Z., & Mayne, P. W. (2019). Modified NTH method for assessing effective friction angle of normally consolidated and overconsolidated clays from piezocone tests. Journal of Geotechnical and Geoenvironmental Engineering, 145(10), 04019067.

[5] 5. Li, Y. Z., Zhang, J. P., Chen, Z. Y., Wang, H. C., Zhang, K. X., Hu, S. S., ... & Dudley, M. (2026, September). Growth and Characterization of High-Quality Thick Epitaxial 4H-SiC Wafers for High Voltage Devices. In Defect and Diffusion Forum (Vol. 452, pp. 79-85). Trans Tech Publications Ltd.

[6] 6. Li, X., Wang, P., Li, G., & Zhang, Y. (2023). Design of a novel self-test-on-chip interface ASIC for capacitive accelerometers. IEEE Transactions on Circuits and Systems I: Regular Papers, 70(7), 2834-2843.

[7] 7. Xiong, S., Chen, T., Rupendra, A., Clement, C., Gasparri, E., & Lucchi, E. (2026). Embodied Carbon of Progressive Timber Integration in a 47-Storey Tropical High-Rise: A Singapore Public Housing Case Study. Building and Environment, 114910.

[8] 8. Yu, A., Pang, F., Yuan, Y., Huang, Y., Li, S., Yu, S., ... & Xia, L. (2023). Simultaneous current and vibration measurement based on interferometric fiber optic sensor. Optics & Laser Technology, 161, 109223.

[9] 9. Zhang, S., Zhang, E., Cookson, J., Huang, P., & Yan, W. (2026). A hybrid XFEM-calibrated analytical framework for efficient fatigue crack growth prediction. International Journal of Fatigue, 109682.

[10] 10. Zhang, S., Huang, P., & Yan, W. (2024). A data-driven approach for railway in-train forces monitoring. Advanced Engineering Informatics, 59, 102258.

[11] 11. Lu, J., Xiong, H., Zhou, J., Lin, Z., & Feng, B. (2025, September). Collaborative Spatial and Channel Attention for Structural Vibration-based Gait Recognition. In 2025 IEEE International Joint Conference on Biometrics (IJCB) (pp. 1-11). IEEE.

[12] 12. Tong, J., Zang, S., Liu, J., Xu, Z., Hu, X., Bai, X., ... & Huang, J. (2026). Polymer-Mediated Assembly from Core–Shell Particles to Tunable Structures and Microrotors. Langmuir, 42(6), 4333-4341.

[13] 13. Zhao, R., Tang, J., Zeng, W., Guo, Y., & Zhao, X. (2025). Towards human-like questioning: Knowledge base question generation with bias-corrected reinforcement learning from human feedback. Information Processing & Management, 62(3), 104044.

[14] 14. Zhao, R., Xu, D., Jian, S., Tan, T., Sun, X., & Zhang, W. (2023, July). Quadratic Exponential Decrease Roll-Back: An Efficient Gradient Update Mechanism in Proximal Policy Optimization. In 2023 2nd International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM) (pp. 65-70). IEEE.

[15] 15. Li, Q., Ye, Q., Zhang, N., Zhang, W., & Hu, F. (2025). Digital-twin-enabled industrial IoT: Vision, framework, and future directions. IEEE Wireless Communications, 32(6), 173-181.

[16] 16. Li, S. (2025). Momentum, volume and investor sentiment study for us technology sector stocks—A hidden markov model based principal component analysis. PloS one, 20(9), e0331658.

[17] 17. Li, X., Wang, P., Li, G., Ni, L., & Zhang, Y. (2023). Design of interface circuits and lightweight PUF for TMR sensors. IEEE Sensors Journal, 23(11), 11754-11761.

[18] 18. Qu, W., Shao, Y., Meng, L., Huang, X., & Xiao, L. (2024, June). A conditional denoising diffusion probabilistic model for point cloud upsampling. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 20786-20795). IEEE.

[19] 19. Zhao, J., Zhang, C., Qin, M., & Yang, P. (2025). QuantFactor REINFORCE: mining steady formulaic alpha factors with variance-bounded REINFORCE. IEEE Transactions on Signal Processing, 73, 2448-2463.

[20] 20. Wang, Y., Wang, Z., Kim, E. H., & Fu, Z. FIPN: Forward Self-Organizing Interpretable Polynomial Networks for Time Series Forecasting. In Forty-third International Conference on Machine Learning.

[21] 21. Lin, Y. (2024). Design of urban road fault detection system based on artificial neural network and deep learning. Frontiers in neuroscience, 18, 1369832.

[22] 22. Yao, J., Yao, C., Chen, Y., Liu, X., Huang, D., & Chu, W. (2026, April). Explainable AI Enhanced Traffic Forecasting for Proactive Autonomous Vehicle Routing. In 2026 IEEE Conference on Technologies for Sustainability (SusTech) (pp. 1-7). IEEE.

[23] 23. Jiang, J., Rahbarimanesh, A., & Maqbool, R. (2026). Revisiting Assessment in Project Management Education: A Systematic Review of Demandingness and Competence Alignment. Project Leadership and Society, 100233.

[24] 24. Fowler, A. G., Mariantoni, M., Martinis, J. M., & Cleland, A. N. (2012). Surface codes: Towards practical large-scale quantum computation. Physical Review A, 86(3), 032324.