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Feasibility of imperialist competitive algorithm to predict the surface settlement induced by tunneling

Tashayo, B ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1007/s00366-018-0641-3
  3. Publisher: Springer London , 2018
  4. Abstract:
  5. Surface settlement is considered as an adverse effect induced by tunneling in the civil projects. This paper proposes the use of the imperialist competitive algorithm (ICA) for predicting the maximum surface settlement (MMS) resulting from the tunneling. For this work, three forms of equations, i.e., linear, quadratic and power are developed and their weights are then optimized/updated with the ICA. The requirement datasets were collected from the line No. 2 of Karaj urban railway, in Iran. In the ICA models, vertical to horizontal stress ratio, cohesion and Young’s modulus, as the effective parameters on the MSS, are adopted as the inputs. The statistical performance parameters such as root mean square error (RMSE), mean bias error (MBE), and square correlation coefficient (R2) are presented and compared to validate the performance. The findings indicate that the developed ICA-based models with the R2 of 0.979, 0.948 and 0.941, obtained from ICA power, ICA quadratic and ICA linear models, respectively, are the acceptable and accurate tools to estimate MSS, and furthermore prove their prediction capability for future research works in this field. © 2018, Springer-Verlag London Ltd., part of Springer Nature
  6. Keywords:
  7. Imperialist competitive algorithm ; Prediction ; Surface settlement ; Tunneling ; Electron tunneling ; Forecasting ; Mean square error ; Optimization ; Railroad transportation ; Satellites ; Settlement of structures ; Correlation coefficient ; Horizontal stress ratio ; Imperialist competitive algorithm (ICA) ; Imperialist competitive algorithms ; Maximum surface settlements ; Root mean square errors ; Statistical performance ; Surface settlements ; Railroad tunnels
  8. Source: Engineering with Computers ; 2018 ; 01770667 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s00366-018-0641-3