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Artificial Neural Network-Based Automated Finite Element Model Updating with an Integrated Graphical User Interface for Operational Modal Analysis of Structures

Year: 2024

Type: article

Abstract: This paper presents an artificial neural network-based graphical user interface, designed to automate finite element model updating using data from operational modal analysis. The approach aims to reduce the uncertainties inherent in both the experimental data and the computational model. A key feature of this method is the application of a discrete wavelet transform-based approach for denoising OMA data. The graphical interface streamlines the FEMU process by employing neural networks to automatically optimize FEM inputs, allowing for real-time adjustments and continuous structural health monitoring under varying environmental and operational conditions. This approach was validated with OMA results, demonstrating its effectiveness in enhancing model accuracy and reliability. Additionally, the adaptability of this method makes it suitable for a wide range of structural types, and its potential integration with emerging technologies such as the Internet of Things further amplifies its relevance.

Source: Buildings

DOI: 10.3390/buildings14103093

Authors: Hamed Hasani, Francesco Freddi

Institution: University of Parma

Language: English

Topic: Structural Health Monitoring Techniques

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,955