Permeability Analysis based on information granulation theory

TL;DR

This paper presents a novel approach combining information granulation, SOM, NFIS, and RST for permeability analysis of rock masses, validated on real-world dam data, enhancing accuracy and stability.

Contribution

It introduces an integrated method using SOM, NFIS, and RST for permeability analysis, with stability ensured by adaptive criteria, and validated on large in-situ data.

Findings

Effective permeability analysis using the proposed method.

Validation on large dam data set confirms accuracy.

Stable algorithm with adaptive error thresholds.

Abstract

This paper describes application of information granulation theory, on the analysis of "lugeon data". In this manner, using a combining of Self Organizing Map (SOM) and Neuro-Fuzzy Inference System (NFIS), crisp and fuzzy granules are obtained. Balancing of crisp granules and sub- fuzzy granules, within non fuzzy information (initial granulation), is rendered in open-close iteration. Using two criteria, "simplicity of rules "and "suitable adaptive threshold error level", stability of algorithm is guaranteed. In other part of paper, rough set theory (RST), to approximate analysis, has been employed >.Validation of the proposed methods, on the large data set of in-situ permeability in rock masses, in the Shivashan dam, Iran, has been highlighted. By the implementation of the proposed algorithm on the lugeon data set, was proved the suggested method, relating the approximate analysis on the permeability, could be applied.