This article presents the evolution of an algorithm that can be applied to a diagnostic systems for tunnels developed by the same authors. The aim of this work is the analysis of typical ground trust shape functions to be introduced in the library of a genetic algorithm in order to calculate the forces acting on tunnel lining starting only from the quantities measured by a set of clinometers and pressure sensors placed inside the lining itself, without any other knowledge of geotechnical or geological parameters. The knowledge of proper trust shapes, derived from geotechnical simulations, increases the performance of the algorithm in terms of convergence and correctness of the result. Some benchmarks of the genetic algorithm applied on geotechnical f.e.m. results is also given.

Calibration of Ground Pressure on Tunnel Lining in Genetic Algorithm Application for Structural Monitoring / Cortese, Giuseppe; Bertagnoli, Gabriele. - In: IOP CONFERENCE SERIES: MATERIALS SCIENCE AND ENGINEERING. - ISSN 1757-899X. - ELETTRONICO. - 960:(2020), p. 022096. ((Intervento presentato al convegno 5th World Multidisciplinary Civil Engineering-Architecture-Urban Planning Symposium – WMCAUS 15-19 June 2020, Prague, Czech Republic tenutosi a Prague nel 15-19 June 2020 [10.1088/1757-899X/960/2/022096].

Calibration of Ground Pressure on Tunnel Lining in Genetic Algorithm Application for Structural Monitoring

Gabriele Bertagnoli
2020

Abstract

This article presents the evolution of an algorithm that can be applied to a diagnostic systems for tunnels developed by the same authors. The aim of this work is the analysis of typical ground trust shape functions to be introduced in the library of a genetic algorithm in order to calculate the forces acting on tunnel lining starting only from the quantities measured by a set of clinometers and pressure sensors placed inside the lining itself, without any other knowledge of geotechnical or geological parameters. The knowledge of proper trust shapes, derived from geotechnical simulations, increases the performance of the algorithm in terms of convergence and correctness of the result. Some benchmarks of the genetic algorithm applied on geotechnical f.e.m. results is also given.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2857078