Bridge damage identification under varying environmental and operational conditions combining Deep Learning and numerical simulations

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Fernandez Navamuel, A (Author) Other; Pardo, D (Author) Other; Magalhães, F. (Author) FEUP; Zamora Sanchez, D (Author) Other; Omella, AJ (Author) Other; Garcia Sanchez, D (Author) Other

Journal

Title: Mechanical Systems and Signal Processing

Vol. 200

ISSN: 0888-3270

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00Y-MV0

DOI: 10.1016/j.ymssp.2023.110471

Abstract (EN): This work proposes a novel supervised learning approach to identify damage in operating bridge structures. We propose a method to introduce the effect of environmental and operational conditions into the synthetic damage scenarios employed for training a Deep Neural Network, which is applicable to large-scale complex structures. We apply a clustering technique based on Gaussian Mixtures to effectively select Q representative measurements from a long-term monitoring dataset. We employ these measurements as the target response to solve various Finite Element Model Updating problems before generating different damage scenarios. The synthetic and experimental measurements feed two Deep Neural Networks that assess the structural health condition in terms of damage severity and location. We demonstrate the applicability of the proposed method with a real full-scale case study: the Infante Dom Henrique bridge in Porto. A comparative study reveals that neglecting different environmental and operational conditions during training detracts the damage identification task. By contrast, our method provides successful results during a synthetic validation.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17