Data Division for Developing Neural Networks Applied to Geotechnical Engineering

TLDR

Artificial neural networks have been applied to geotechnical engineering problems, but data division is often arbitrary and can significantly affect model performance. This study investigates how different data division strategies influence ANN performance in predicting settlement of shallow foundations on granular soils. Four division methods—random, statistically consistent, self‑organizing maps, and fuzzy clustering—were compared to assess their impact on model training, testing, and validation. Results show that accounting for statistical properties of data subsets yields optimal performance, and that SOM and fuzzy clustering methods are effective for data division.

Abstract

In recent years, artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. In the majority of these applications, data division is carried out on an arbitrary basis. However, the way the data are divided can have a significant effect on model performance. In this paper, the issue of data division and its impact on ANN model performance is investigated for a case study of predicting the settlement of shallow foundations on granular soils. Four data division methods are investigated: (1) random data division; (2) data division to ensure statistical consistency of the subsets needed for ANN model development; (3) data division using self-organizing maps (SOMs); and (4) a new data division method using fuzzy clustering. The results indicate that the statistical properties of the data in the training, testing, and validation sets need to be taken into account to ensure that optimal model performance is achieved. It is also apparent from the results that the SOM and fuzzy clustering methods are suitable approaches for data division.