Prediction of Tunnelling-Induced Surface Settlement with Artificial Neural Networks, Case Study: Mashhad Subway Tunnel

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

MEHRNAHAD H.; Kholgh Zekrabad M.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF ENGINEERING GEOLOGY Year:2018 | Volume:12 | Issue:5 (supp.) Page(s): 135-158

Download Full-Text

Persian Verion

View:

593

Download:

188

Cites:

Information Journal Paper

Title

Prediction of Tunnelling-Induced Surface Settlement with Artificial Neural Networks, Case Study: Mashhad Subway Tunnel

Author(s)

MEHRNAHAD H. | Kholgh Zekrabad M. | Issue Writer Certificate

Keywords

Surface SettlementQ1

Artificial Neural NetworkQ1

Mashhad Subway TunnelQ1

Prediction of SettlementQ2

Abstract

In urban areas, it is essential to protect the existing adjacent structures and underground facilities from the damage due to tunneling. In order to minimize the risk, a tunnel engineer needs to be able to make reliable prediction of ground deformations induced by tunneling. Numerous investigations have been conducted in recent years to predict the settlement associated with tunneling; the selection of appropriate method depends on the complexity of the problems. This research intends to develop a method based on Artificial Neural Network (ANN) for the prediction of tunnelling-induced Surface Settlement. Surface Settlements above a tunnel due to tunnel construction are predicted with the help of input variables that have direct physical significance. The data used in running the network models have been obtained from line 2 of Mashhad Subway Tunnel project. In order to predict the tunnelling-induced Surface Settlement, a Multi-Layer Perceptron (MLP) analysis is used. A three-layer, feed-forward, backpropagation neural network, with a topology of 7-24-1 was found to be optimum. For optimum ANN architecture, the correlation factor and the minimum of Mean Squared Error are 0. 963 and 2. 41E-04, respectively. The results showed that an appropriately trained neural network could reliably predict tunnelling-induced Surface Settlement.

Cites

No record.

References

No record.

Cite

APA: Copy

MEHRNAHAD, H., & Kholgh Zekrabad, M.. (2018). Prediction of Tunnelling-Induced Surface Settlement with Artificial Neural Networks, Case Study: Mashhad Subway Tunnel. JOURNAL OF ENGINEERING GEOLOGY, 12(5 (supp.) ), 135-158. SID. https://sid.ir/paper/186328/en

Vancouver: Copy

MEHRNAHAD H., Kholgh Zekrabad M.. Prediction of Tunnelling-Induced Surface Settlement with Artificial Neural Networks, Case Study: Mashhad Subway Tunnel. JOURNAL OF ENGINEERING GEOLOGY[Internet]. 2018;12(5 (supp.) ):135-158. Available from: https://sid.ir/paper/186328/en

IEEE: Copy

H. MEHRNAHAD, and M. Kholgh Zekrabad, “Prediction of Tunnelling-Induced Surface Settlement with Artificial Neural Networks, Case Study: Mashhad Subway Tunnel,” JOURNAL OF ENGINEERING GEOLOGY, vol. 12, no. 5 (supp.) , pp. 135–158, 2018, [Online]. Available: https://sid.ir/paper/186328/en

Related Journal Papers