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2 Polish Journal of Environmental Studies

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Multi-index classification model for loess deposits based on rough set and BP neural network

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Zhang X.-L. , Xue Y.-G. , Qiu D.-H. , Yang W.-M. , Su M.-X. , Li Z.-Q. , Zhou B.-H.

Polish Journal of Environmental Studies

2019

tom 28

nr 2

Classifying loess deposits is an important process for selecting support form and construction methods for tunnels. An accurate evaluation of loess deposits is a necessary prerequisite to control deformation, save cost, and improve construction efficiency. In this paper, a neural network model with an evaluation system consisting of physical and mechanical indices of loess is proposed to realize intelligent classification of loess deposits for tunneling. The influence of water content, natural density, cohesion, internal friction angle, elastic modulus, and Poisson ratio on stability level of loess is analyzed by rough set theory based on statistical data of borehole samples. Results show that the affect of natural density is negligible. Then other indicators such as input nodes and the BP neural network model are formed after learning statistical samples and being applied to the project for testing. Finally, the output of the model is consistent with the actual. This study provides a multi-index model for evaluating loess deposits surrounding tunnels and provides a reference for future research.

Risk assessment of rockfall hazards in a tunnel portal section based on normal cloud model

100%

Wang X.-T. , Li S.-C. , Ma X.-Y. , Xue Y.-G. , Hu J. , Li Z.-Q.

Polish Journal of Environmental Studies

2017

tom 26

nr 5

In mountainous regions, rockfall is a typical geological disaster which might bring immense casualties and economic losses, but also endanger the safety of civil engineering construction. Many tunnels are being built in the southwest of China, thus a comprehensive assessment for rockfall risk is needed. For this purpose, in this paper, based on normal cloud model theory, we created a multi-index evaluation model for the rockfall risk assessment. Then, according to previous research and specific geological conditions, potential tunnel dangers are classified into four ranks, and some geological factors are considered as the principal factors. In order to fully express the opinions of experts, the qualitative indices were quantified by continuous value scale. Moreover, the value of each index is determined by expert scoring. In view of different evaluation units, we used the normalization method to make geological indices dimensionless. And three numerical characteristics (Ex, En, and He) were calculated by the cloud generator algorithm with MATLAB. In this study, we assigned the weight of indices by simple dependent function to avoid the influence of subjective. Finally, by means of a normal cloud generator, we determined the integrated certainty grades. To ensure the accuracy of the normal cloud model method, it was tested in rockfall cases in Jiefangcun tunnel. And the results obtained by the cloud model method are in good agreement with the practical situation. Moreover, the results are better than those of the AHP-FUZZY and artificial neural networks methods after comparison. The cloud model-based method realizes a multi-criteria assessment of the rockfall risk in tunnel portal section and provides a practical guide on safe tunnel construction for similar projects.