Metaheuristic Optimization Model Selection for Forecasting Surface Settling Caused by Tunneling

DOI

10.2991/978-94-6463-258-3_48How to use a DOI?

Keywords

Surface settlement prediction; metaheuristic algorithms; optimization; benchmark functions

Abstract

One of the riskiest aspects of excavating tunnels for infrastructure projects like subways and such is the possibility for surface set tlement, particularly in metropolitan areas. Therefore, it is crucial to predict maximum surface settlement (MSS) accurately to reduce the likeli hood of damage. Many researchers proposed new algorithms to solve this problem. This paper compares six existing metaheuristic nature-inspired algorithms i.e., Grey wolf, Ant lion, Dragonfly, Whale, Moth flame, Sine cosine optimizer concerning the given parameters i.e., hori zontal to vertical stress ratio, cohesion, and Young’s modulus. As a con sequence of this research, the researcher will be able to choose the most matched algorithm to handle this problem because each region has various variants in the parameters and each algorithm behaves differently with these factors. Through simulations and numerical values, the findings are validated on many benchmark functions.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Asad Askari
AU  - Hasan Askari
PY  - 2023
DA  - 2023/11/08
TI  - Metaheuristic Optimization Model Selection for Forecasting Surface Settling Caused by Tunneling
BT  - Proceedings of the Rocscience International Conference  (RIC 2023)
PB  - Atlantis Press
SP  - 491
EP  - 501
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-258-3_48
DO  - 10.2991/978-94-6463-258-3_48
ID  - Askari2023
ER  -