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

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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.

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Funding

The authors gratefully acknowledge the support from the Konoike Construction Japan in field data collection and data analysis.

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Authors and Affiliations

  1. Graduate School of Engineering, Nagasaki University, 1-14 Bunkyo-machi, Nagasaki, 852-8521, Japan

    Jiankang Liu, Yujing Jiang, Wei Han & Osamu Sakaguchi

  2. Department of Civil Engineering, Konoike Construction Co., Ltd., 3-6-1, Kitakyuhoji-machi, Chuo-ku, Osaka, 541-0057, Japan

    Osamu Sakaguchi

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  1. Jiankang Liu
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Correspondence to Yujing Jiang.

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Liu, J., Jiang, Y., Han, W. et al. Optimized ANN model for predicting rock mass quality ahead of tunnel face using measure-while-drilling data. Bull Eng Geol Environ 80, 2283–2305 (2021). https://doi.org/10.1007/s10064-020-02057-6

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