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Multi-index fusion database and intelligent evaluation modelling for geostress classification.

Authors :
Duan, Shuqian
Zhao, Gengchen
Jiang, Quan
Xiong, Jiecheng
Sun, Yuanda
Kou, Yongyuan
Qiu, Shili
Source :
Tunneling & Underground Space Technology. Jul2024, Vol. 149, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

• A database has been established by collecting 204 sets of engineering cases. • A multi-factor-coupling geostress model was established using AutoGluon. • The model exhibits higher accuracy compared to traditional criteria. Accurate evaluation of geostress levels is crucial for the investigation, design, and construction of underground engineering. However, existing methods for geostress classification are limited by their reliance on singular indexes, leading to discrepancies between the evaluation results and the actual observations in engineering. The discrepancies have resulted in casualties and economic losses. To address the above issues, 204 sets of geostress levels data were collected from numerous documents in the study. Six indexes related to geostress levels have been identified by analyzing the collected data and traditional criteria. Additionally, an orthogonal experimental design was carried out on the range-based indexes to obtain a multi-index fusion database. Furthermore, an intelligent evaluation model for the geostress levels was established in combination with the AutoGluon automatic machine learning framework. Subsequently, software has been developed from this model for ease of use in practical engineering. After on-site data verification, the proposed model demonstrated an accuracy of 95%, outperforming five traditional criteria, with the highest accuracy among them being 70%. The research could provide an effective basis for geostress classification, thereby enhancing the safety of on-site construction personnel. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08867798
Volume :
149
Database :
Academic Search Index
Journal :
Tunneling & Underground Space Technology
Publication Type :
Academic Journal
Accession number :
177455414
Full Text :
https://doi.org/10.1016/j.tust.2024.105802