Work

Data-driven Crack Detection in the Realm of Structural Health Monitoring: An Overview*

Year: 2025

Type: conference-paper

Abstract: The advancements of sensor technologies, improvements in computational power, and massive amounts of data from the increasing number of sensors deployed on structures have augmented the need for data-driven techniques in structural health monitoring (SHM), particularly for fatigue crack detection. Data-driven methods and tools are increasingly used in analyzing structures to ensure the absence of any fatigue crack in various engineering fields including civil, mechanical, aerospace, and maritime engineering. This overview aims to present the most recent research that utilizes data-driven methods for fatigue crack detection in engineering structures that fall within the realms of signal processing (SP) and machine learning (ML). It focuses on studies utilizing Deep Learning (DL) for crack detection, highlighting the importance of Transfer Learning (TL).

Source: International Conference on Control, Decision and Information Technologies (CoDIT)

DOI: 10.1109/codit66093.2025.11321723

Authors: Hassan Dabaja, Hassan Noura, Mustapha Ouladsine

Institutions: Centre National de la Recherche Scientifique, Aix-Marseille Université, Laboratoire d’Informatique et Systèmes

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: green

Funder: BP