Bayesian Fusion of CPTu Dissipation and DMT Stress Indices for Clay History Classification
DOI:
https://doi.org/10.71465/jgs69Keywords:
Bayesian Inference, Piezocone Penetration Test, Flat Dilatometer Test, Clay Stress History, Geotechnical FusionAbstract
The accurate classification of clay stress history is a foundational requirement in geotechnical engineering, directly influencing the design, safety, and economic viability of infrastructure projects. Traditional deterministic methods often rely on isolated in-situ tests, such as the Piezocone Penetration Test with pore pressure measurement or the Flat Dilatometer Test, which can yield conflicting interpretations due to inherent soil variability and measurement uncertainties. This paper presents a comprehensive framework for the Bayesian fusion of dissipation metrics derived from the Piezocone Penetration Test and horizontal stress indices obtained from the Flat Dilatometer Test to classify clay history. By leveraging Bayesian inference, the proposed model systematically integrates the complementary strengths of both testing modalities while rigorously quantifying the epistemic and aleatory uncertainties associated with site characterization. Extensive field data from multiple well-documented clay deposits are utilized to calibrate the probabilistic model. The fusion approach demonstrates a significant improvement in classification accuracy for normally consolidated, lightly overconsolidated, and heavily overconsolidated clay profiles compared to single-sensor interpretations. The integration of dissipation decay characteristics with horizontal stress parameters provides a multidimensional decision boundary that mitigates false positive classifications in transitional soil layers. This research offers a robust, probabilistically sound methodology that enhances the reliability of geotechnical site investigations and provides engineers with a quantifiable measure of confidence in soil behavior predictions.
References
1. Malik, B. A., Jie, S., Zeyu, L., Ouyang, Z., & Wang, D. (2025). Experimental investigation of suction installation behavior of caissons in loose saturated sands. Canadian Geotechnical Journal, 62, 1-21.
2. Malik, B. A., Mandhaniya, P., Soni, A. K., & Ouyang, Z. (2024). Numerical modeling and failure analysis of caisson foundations subjected to the monotonic lateral loads. Ocean Engineering, 309, 118491.
3. 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.
4. Ouyang, Z., Feng, H., Yang, J., Gu, X., Shi, J., & Li, H. (2026). Interpretation of soil deformation characteristics based on in-situ test: A comprehensive review. Ocean Engineering, 352, 124508.
5. Malik, B. A., Soni, A. K., Mandhnaiya, P., & Ouyang, Z. (2024, December). Development of an ABAQUS-Python simulation model of offshore caisson. In Vietnam Symposium on Advances in Offshore Engineering (pp. 123-133). Singapore: Springer Nature Singapore.
6. 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.
7. 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.
8. 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.
9. Yu, A., Huang, Y., Li, S., Wang, Z., & Xia, L. (2023). All fiber optic current sensor based on phase-shift fiber loop ringdown structure. Optics Letters, 48(11), 2925-2928.
10. Zhang, S., & Yan, W. (2025). In-trainNet: A Two-Step Data-Driven Framework for Enhancing Railway In-Train Forces Monitoring. Advanced Engineering Informatics, 65, 103352.
11. Luan, S., Wang, P., Zhang, L., He, Y., Huang, X., Jian, G.,... & Fu, Z. (2023). Atmospherically hydrothermal assisted solid-state reaction synthesis of ultrafine BaTiO3 powder with high tetragonality. Journal of Electroceramics, 50 (4), 97-111.
12. Wang, Y. Y., Lou, M., Wang, Y., Wu, W. G., & Yang, F. (2022). Stochastic failure analysis of reinforced thermoplastic pipes under axial loading and internal pressure. China Ocean Engineering, 36(4), 614-628.
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. Zhao, R., Tang, J., Zeng, W., Chen, Z., & Zhao, X. (2024, October). Zero-shot knowledge graph question generation via multi-agent llms and small models synthesis. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 3341-3351).
15. Dai, Y., Ji, Y., Zhang, X., Wang, Y., Chu, X., & Lu, Z. (2026). Harder is better: Boosting mathematical reasoning via difficulty-aware grpo and multi-aspect question reformulation. arXiv preprint arXiv:2601.20614.
16. 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.
17. Wang, S., Zhang, X., Wang, P., & Li, X. (2026). Design of Magnetic Sensor Array-Based PUFs for IoT Security. IEEE Transactions on Instrumentation and Measurement, 75, 1-9.
18. Liang, R., Xu, S., Xie, C., Chen, J., Ren, F., Yang, S., & Yabe, T. (2026). Abstain Mask Retain Core: Time Series Prediction by Adaptive Masking Loss with Representation Consistency. Advances in Neural Information Processing Systems, 38, 99445-99471.
19. Hu, H., Yang, Y., Zhao, Q., & Zhang, C. (2022, June). On the role of discount factor in offline reinforcement learning. In International conference on machine learning (pp. 9072-9098). PMLR.
20. Pei, Z., Huang, Q., & Wang, S. (2026). When LLMs develop languages: Symbolic communication for efficient multi-agent reasoning. arXiv preprint arXiv:2606.29354.
21. Zhang, T., Xu, J., Chen, J., Li, J., Xiao, Y., & Wang, Q. (2026). NodeJEPA: Structure-Conditioned Latent Prediction for Node-Level Graph Self-Supervised Learning. arXiv preprint arXiv:2608.04381.
22. Liao, Q., Chen, Y., He, S., Wang, R., Xu, W., & Chu, W. (2025). Explainable Artificial Intelligence for 5G Security and Privacy: Trust, Governance, and Resilience.
23. Wang, Y., Lou, M., Luo, Y., Wan, Z., Wang, L., & Tang, Q. (2026). Contact dynamics characteristics of a deepwater drilling riser-drillstring coupled system. Ocean Engineering, 366, 127458.
24. Acquah, C., Hoehn, S., Krul, S., Jockusch, S., Yang, S., Seth, S. K., ... & Crespo-Hernández, C. E. (2024). Electronic relaxation pathways in thio-acridone and thio-coumarin: two heavy-atom-free photosensitizers absorbing visible light. Physical Chemistry Chemical Physics, 26(46), 28980-28991.
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Copyright (c) 2026 Ye-Jin Moon, Ha-Neul Jeon (Author)
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