Numerical benchmark for damage identification in Structural Health Monitoring

TL;DR

This paper introduces a comprehensive, open-source simulated dataset for Structural Health Monitoring, incorporating environmental variations, damage scenarios, noise, and faults to aid validation of data-driven SHM methods.

Contribution

It provides a detailed, realistic synthetic dataset and framework for generating SHM data, addressing data accessibility issues and supporting research validation.

Findings

Dataset includes dynamic and static measurements with environmental effects.

Simulated data accounts for damage, noise, and sensor faults.

Open-source code ensures reproducibility and broad accessibility.

Abstract

The availability of a dataset for validation and verification purposes of novel data-driven strategies and/or hybrid physics-data approaches is currently one of the most pressing challenges in the engineering field. Data ownership, security, access and metadata handiness are currently hindering advances across many fields, particularly in Structural Health Monitoring (SHM) applications. This paper presents a simulated SHM dataset, comprised of dynamic and static measurements (i.e., acceleration and displacement), and includes the conceptual framework designed to generate it. The simulated measurements were generated to incorporate the effects of Environmental and Operational Variations (EOVs), different types of damage, measurement noise and sensor faults and malfunctions, in order to account for scenarios that may occur during real acquisitions. A fixed-fixed steel beam structure was chosen as reference for the numerical benchmark. The simulated monitoring was operated under the assumptions of a Single Degree of Freedom (SDOF) for generating acceleration records and of the Euler-Bernoulli beam for the simulated displacement measurements. The generation process involved the use of parallel computation, which is detailed within the provided open-source code. The generated data is also available open-source, thus ensuring reproducibility, repeatability and accessibility for further research. The comprehensive description of data types, formats, and collection methodologies makes this dataset a valuable resource for researchers aiming to develop or refine SHM techniques, fostering advancements in the field through accessible, high-quality synthetic data.