Title: Research on estimation of permeability coefficients in microbial geotechnical soils based on data-driven models

Authors: Mayao Cheng; Linsheng Chen

Addresses: College of Transportation and Civil Engineering and Architecture, Foshan University, Foshan, 528225, China ' College of Transportation and Civil Engineering and Architecture, Foshan University, Foshan, 528225, China

Abstract: Microbial geotechnical soil permeability coefficient estimation prediction is extremely valuable for the development of soil engineering. The study proposes an integrated data-driven model combining three base learners, RVM, ANFIS and iTLBO-ELM, assigning corresponding weights to each base learner through the PLS integrated model combination model, and applying the model to the prediction of microbial geotechnical soil permeability coefficient estimation. The MAE of the proposed integrated model has lower values compared to the single model, but the two metrics MAPE and RMSE are not the lowest; however, the integrated model outperforms both iTLBO-ELM and RVM in terms of MAPE, and the estimated predictions of permeability coefficients for November data are better than those for May. For iTLBO-ELM and RVM, the MAPE of PLS decreased by 5.51% and 1.56% respectively in May and 3.46% and 1.24% respectively in October. The integrated data-driven model proposed in the study can effectively achieve the estimated prediction of microbial geotechnical soil permeability coefficients and facilitate the intelligent acquisition of engineering permeability coefficients.

Keywords: data-driven models; microorganisms; geotechnical land; permeability coefficients.

DOI: 10.1504/IJBIC.2025.143651

International Journal of Bio-Inspired Computation, 2025 Vol.25 No.1, pp.22 - 31

Received: 11 Aug 2022
Accepted: 18 Jul 2023
Published online: 03 Jan 2025 *

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