What question did this study set out to answer?

The main aim is to establish an effective method for predicting soil properties using simple machine learning algorithms.

February 21, 2026Open Access

Solving BM/AirportSoilProperties/2/2025 using simple machine learning algorithms

Puntos clave

The main aim is to establish an effective method for predicting soil properties using simple machine learning algorithms.
Employed l1 mixed filtering and Bayesian Linear Regression for predictions
Focused on predicting unknown undrained shear strength in a specific dataset
Introduced a revised root-mean-square error for performance evaluation
The method's RMSE values were higher than previous benchmarks but still captured true soil properties effectively
Predicted probability distributions for undrained shear strength closely matched actual values
The study emphasizes the importance of adaptively choosing suitable algorithms for data

Resumen

This paper presents solutions for GEOAI benchmark problems BM/AirportSoilProperties/2/2025. Two simple machine learning (ML) methods, l 1 mixed filtering and Bayesian Linear Regression (BLR), were used to develop a new site characterization method. To demonstrate its effectiveness, the unknown undrained shear strength s u in the cluster-BID/4 was predicted. The revised root-mean-square error (RMSE) was also proposed to more reasonably evaluate the performance of ML predictions. Although the RMSE values obtained using the proposed method are larger than those reported in the sample solutions by Otake et al. (2025), the predictive probability distributions of undrained shear strength s u still reasonably capture the true s u values. This demonstrates that even simple ML methods can perform well in challenging benchmark problems for site characterization. The results also highlight the importance of adaptively selecting a suitable BID for the target dataset.

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