Development of a k-Nearest Neighbor (kNN) Machine Learning Model to Estimate the SPT N-Values of Valenzuela City, Philippines

Abstract

Due to the intricate geological formation of geomaterials, they often exhibit a range of attributes at different sites on a given site. This has posed a problem for geotechnical engineers, as they require correct soil and rock information to plan and design geotechnical construction projects. Numerous efforts have been made at the local level to bridge this disparity by standardizing the quantification of soil parameters; nevertheless, these studies have limitations, and there are still areas with ambiguous or incorrect information. To help close this gap, this article will demonstrate an innovative technique for predicting representative Soil Penetration Test (SPT) N-values for each Valenzuela City Barangay/Zone using a k-Nearest Neighbor (k-NN) Machine Learning Model. Borehole data from the city of Valenzuela were collected and analyzed for this study. The k-NN Model’s input parameters are the borehole’s latitude, longitude, and depth, while the response parameters are the SPT N-Values. The k-NN technique was used to train the input data, identify patterns, and make classification decisions for the SPT N-Values per depth per location in Valenzuela City. The centroids of each Barangay/Zone in Valenzuela City were also extracted in Latitude and Longitude format and utilized to deploy the k-NN Model. To illustrate the findings, we presented subsurface information from Valenzuela City with SPT N-Values. For validation purposes, the accuracy rate of the machine learning model was obtained. The model’s hyperparameter in terms of k was tuned to determine if the accuracy rate could be improved.

Relationship of Resistivity Value with the Ground Material Information Obtained from Borehole Data: Case study

“…8 (b) indicates a point of SPT-N of 6 and 500 Ωm as unreliable data at the depth of 3.0-3.5 meters of penetration could be due to the presence of loose, silty sand, which may have affected the accuracy of the SPT-N value. Loose, silty sand typically has a lower resistance to penetration, which could result in inconsistent SPT-N values (Galupino & Dungca, 2022;Ahmed et al, 2021). The water table ending at depth of 0.52 meters may not directly explain the unreliable data at the deeper layer, as the water table level does not necessarily correlate with the soil's mechanical properties (Owusu-Nimo & Boadu, 2020).…”