Property Estimation in Geotechnical Databases Using Labeled Random Finite Sets

TL;DR

This paper presents a novel approach for estimating geotechnical properties from noisy, incomplete data using the labeled random finite set framework and the GLMB filter, addressing data uncertainty issues in geotechnics.

Contribution

It introduces the application of the GLMB filter within the LRFS framework to improve property estimation in uncertain geotechnical datasets, a novel integration in this field.

Findings

Effective estimation of geotechnical properties from noisy data

Demonstrated robustness of the method with a public clay database

Potential for improved decision-making in geotechnical engineering

Abstract

The sufficiency of accurate data is a core element in data-centric geotechnics. However, geotechnical datasets are essentially uncertain, whereupon engineers have difficulty with obtaining precise information for making decisions. This challenge is more apparent when the performance of data-driven technologies solely relies on imperfect databases or even when it is sometimes difficult to investigate sites physically. This paper introduces geotechnical property estimation from noisy and incomplete data within the labeled random finite set (LRFS) framework. We leverage the ability of the generalized labeled multi-Bernoulli (GLMB) filter, a fundamental solution for multi-object estimation, to deal with measurement uncertainties from a Bayesian perspective. In particular, this work focuses on the similarity between LRFSs and big indirect data (BID) in geotechnics as those characteristics resemble each other, which enables GLMB filtering to be exploited potentially for data-centric geotechnical engineering. Experiments for numerical study are conducted to evaluate the proposed method using a public clay database.