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Bulletin of Engineering Geology and the Environment

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07.01.2021 | Original Paper

Optimized ANN model for predicting rock mass quality ahead of tunnel face using measure-while-drilling data

verfasst von: Jiankang Liu, Yujing Jiang, Wei Han, Osamu Sakaguchi

Erschienen in: Bulletin of Engineering Geology and the Environment | Ausgabe 3/2021

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Abstract

Rock mass quality assessment has a vital influence on the excavation of tunnels and caverns in rock mass. For this purpose, extensive field studies, including records of measure-while-drilling data and rock mass quality scores (RQS) from the observation reports of tunnel faces, have been conducted. In order to predict RQS, three optimized artificial neural network (ANN) models based on genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competition algorithm (ICA) were developed. Six parameters of measure-while-drilling (MWD) data and their corresponding RQS constituted 1270 datasets, which were set as input and output of ANN, respectively. The traditional multiple linear regression (MLR), multiple nonlinear regression (MNR) statistical model, and ANN model were developed as comparative models. Comparison results reveal that PSO-ANN and ICA-ANN models are capable of predicting RQS with higher reliability than the MLR, MNR, ANN, and GA-ANN models. Results indicate that PSO-ANN and ICA-ANN models can be used to predict RQS; however, the PSO-ANN model has better performance.

Vorheriger Artikel Characterization of discontinuity surface morphology based on 3D fractal dimension by integrating laser scanning with ArcGIS

Nächster Artikel Experimental study on mechanical properties and failure behaviour of the pre-cracked coal-rock combination

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Titel: Optimized ANN model for predicting rock mass quality ahead of tunnel face using measure-while-drilling data
verfasst von: Jiankang Liu
Yujing Jiang
Wei Han
Osamu Sakaguchi
Publikationsdatum: 07.01.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Bulletin of Engineering Geology and the Environment / Ausgabe 3/2021
Print ISSN: 1435-9529
Elektronische ISSN: 1435-9537
DOI: https://doi.org/10.1007/s10064-020-02057-6

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