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The Surface Subsidence prediction of Shield Construction Based on the Fuzzy Neural Network

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There are many factors that influence the surface subsidence caused by shield construction, which make it difficult to predict the subsidence using the mechanical models. The fuzzy neural network proves to be good at dealing with nonlinear and fuzzy problems and can well establish the nonlinear relationship between influence factors and surface subsidence. In order to improve predicting the tunneling-induced surface settlement, the fuzzy neural network is adopted. The model gives a consideration to the tunneling boring machine (TBM) geometric size, stratum mechanical properties and construction parameters. Compared with the predicted results of BP neural network in Nanchang subway, Adaptive Network-based Fuzzy Inference System (ANFIS) has a higher accuracy in the prediction. The model of ANFIS can be applied to the similar projects to guide the construction of tunnel and guarantee the safety of surface constructions.

LOKASI: Ruang Koleksi Umum
Rak: 2

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Ketersediaan

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Informasi Detail

Judul Seri

Proceedings of GeoShanghai 2018 International Conference: Tunnelling and Underground Construction

No. Panggil

624.19 (063) HAN s

Penerbit

Shanghai : Springer., 2018

Deskripsi Fisik

190-197p.: Illus.; 18 x 24 cm

Bahasa

English

ISBN/ISSN

978-981-13-0016-5

Klasifikasi

624.19 (063)

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

TUNNELLING

Info Detail Spesifik

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Pernyataan Tanggungjawab

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Versi lain/terkait

Lampiran Berkas

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