The state of the art of artificial intelligence approaches and new technologies in structural health monitoring of bridges

R Zinno, SS Haghshenas, G Guido, K Rashvand… - Applied Sciences, 2022 - mdpi.com
The challenges of urban administration are growing, as the population, automobiles, and
cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A …

From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods

SHM Rizvi, M Abbas - Engineering Research Express, 2023 - iopscience.iop.org
Owing to recent advancements in sensor technology, data mining, Machine Learning (ML)
and cloud computation, Structural Health Monitoring (SHM) based on a data-driven …

Acoustic emission and artificial intelligence procedure for crack source localization

J Melchiorre, A Manuello Bertetto, MM Rosso… - Sensors, 2023 - mdpi.com
The acoustic emission (AE) technique is one of the most widely used in the field of structural
monitoring. Its popularity mainly stems from the fact that it belongs to the category of non …

Reconstruction of structural acceleration response based on CNN-BiGRU with squeeze-and-excitation under environmental temperature effects

M Huang, N Wan, H Zhu - Journal of Civil Structural Health Monitoring, 2024 - Springer
Structural health monitoring (SHM) system is used to evaluate the service performance of
structures. Accurate monitoring data is crucial to obtain the real health status of a structure …

A novel multistage damage detection method for trusses using time-history data based on model order reduction and deep neural network

QX Lieu - Mechanical Systems and Signal Processing, 2023 - Elsevier
This article first proposes a multistage damage identification approach for trusses using time-
series data relied upon model order reduction (MOR) and deep neural network (DNN). In the …

Bayesian-based hyperparameter optimization of 1D-CNN for structural anomaly detection

X Li, H Guo, L Xu, Z Xing - Sensors, 2023 - mdpi.com
With the rapid development of sensor technology, structural health monitoring data have
tended to become more massive. Deep learning has advantages when handling big data …

Multi-zone parametric inverse analysis of super high arch dams using deep learning networks based on measured displacements

X Liu, F Kang, MP Limongelli - Advanced Engineering Informatics, 2023 - Elsevier
Parametric inverse analysis/identification provides significant information for structural
damage detection and construction in dam engineering. The main challenge in inverse …

Deep learning-based fatigue damage segmentation of wind turbine blades under complex dynamic thermal backgrounds

S Sheiati, X Chen - Structural Health Monitoring, 2024 - journals.sagepub.com
Passive thermography is an efficient method to inspect fatigue damage of large-scale
structures such as wind turbine blades under cyclic loads. Quantitative damage evaluation …

Towards vibration-based damage detection of civil engineering structures: overview, challenges, and future prospects

A Zar, Z Hussain, M Akbar, T Rabczuk, Z Lin… - International Journal of …, 2024 - Springer
In this paper, we delve into the evolving landscape of vibration-based structural damage
detection (SDD) methodologies, emphasizing the pivotal role civil structures play in society's …

Unveiling out-of-distribution data for reliable structural damage assessment in earthquake emergency situations

B Ahmed, S Mangalathu, JS Jeon - Automation in Construction, 2023 - Elsevier
To ensure accurate and effective emergency responses following an earthquake, one must
promptly and accurately assess damage to structures with minimal manual effort. An …