Data Fusion for Structural Health Monitoring

Dear Colleagues,

Structural Health Monitoring (SHM) is a critical discipline dedicated to evaluating the integrity, operational safety, and service performance of civil, mechanical, and aerospace structures throughout their entire life cycles. With the rapid advancement of sensing technologies, fiber optic sensing, optical inspection, guided wave testing, the Internet of Things (IoT), artificial intelligence (AI), and digital twin (DT) technologies, modern SHM systems can collect massive volumes of multi-source heterogeneous data, including vibration, strain, displacement, temperature, guided wave signals, fiber optic monitoring data, optical imaging, and visual imagery. However, single-modal monitoring data generally suffer from limitations such as insufficient reliability, limited information dimensions, and susceptibility to environmental interference, making it difficult to achieve accurate identification, localization, performance evolution prediction of structural damage, and refined identification of structural damage.

As an important interdisciplinary technology, data fusion can integrate multi-source, multi-scale, multi-metric, and multi-temporal monitoring data from guided wave propagation, fiber optic sensing, and optical inspection, making up for the shortcomings of a single monitoring method. Furthermore, it improves the stability, detection accuracy, and information completeness of SHM systems, providing strong technical support for the refined identification of structural damage. This technology can realize the synergistic integration of complementary information from different sensors and monitoring methods, breaking the "data island" phenomenon in traditional SHM and providing support for the accurate assessment of structural conditions. Algorithms based on rules, statistics, feature-level consensus, and machine learning (including deep learning) are the mainstream research directions for data fusion in SHM at this stage, effectively promoting the development of real-time structural monitoring, damage identification, microstructure measurement, refined identification of structural damage, residual life prediction, and intelligent operation and maintenance decision-making technologies.

Data fusion has wide applications in the field of SHM, covering various types of engineering objects such as bridges, buildings, dams, aircraft, ships, and composite structures. Its core application scenarios include refined identification of structural damage, guided wave damage assessment, online fiber optic monitoring, optical non-destructive testing, digital twin construction, intelligent operation and maintenance, and long-term management of infrastructure. It plays a key role in promoting the transformation of structural operation and maintenance from passive maintenance to active and predictive maintenance modes. In recent years, the growing demand for high-precision and intelligent SHM systems in the engineering field has greatly promoted the rapid development of data fusion algorithms, multi-sensor integration, real-time data processing frameworks, and multi-method fusion monitoring technologies combining guided waves, fiber optics, and optics. In particular, technological advancements in localized high-resolution inspections are closing the gap between macro-scale monitoring and micro-defect characterization.

This Special Issue aims to collect cutting-edge research findings, innovative theoretical methods, and engineering application cases related to data fusion for structural health monitoring. We warmly welcome researchers to submit manuscripts, focusing on the theoretical challenges, technological breakthroughs, and engineering implementation of data fusion in SHM. In particular, we encourage innovative research combining guided wave testing, fiber optic sensing, and optical measurement technologies in the refined identification of structural damage, to promote the in-depth integration of multi-physics and multi-metric data fusion technologies with SHM engineering practice, and advance the development of intelligent, highly reliable, and efficient structural monitoring technologies as well as technologies for the refined identification of structural damage.

Dr. Dongyue Gao
Dr. Chenglong Guan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• data fusion
• structural health monitoring (SHM)
• multi-source sensing
• guided wave
• fiber optic sensing
• optical inspection
• refined identification of structural damage
• digital twin
• multi-source sensing
• damage detection
• micro-structural damage