An edge-enabled lightweight intelligent framework for structural health monitoring with strain-temperature sensing using wavelet scattering and 1D convolutional autoencoders
Aug 2026· Journal of Civil Structural Health Monitoring· Vol 16· 0 citations· 65 references
TL;DR
This study presents a generalizable, deep learning–based intelligent signal processing framework for the monitoring of civil infrastructure using strain–temperature sensing that exemplifies how intelligent signal processing pipelines can enable real-time monitoring and support timely maintenance decisions across infrastructure systems.
An intelligent SHM framework that integrates one-dimensional Convolutional Neural Networks (1D-CNN) with Long Short-Term Memory (LSTM) networks for automated damage detection from vibration sensor data acquired through Internet of Things (IoT) sensor networks is proposed.
Yijin Zhang· International Conference on...· 0 citations
A hybrid AI framework integrating CNN– Transformer architectures to address challenges of textile-based sensors through adaptive wavelet denoising, GAN-based data augmentation, and attention-guided multimodal feature fusion for heterogeneous sensing signals is proposed.
As the number of wearable sensing and IoT-enabled healthcare systems continues to surge, remote patient monitoring is gaining attention for the early detection of abnormal physiological conditions. However, current methods are restricted to single-signal analysis, rely on intensive deep learning models, offer poor inte...
Mohammed Liaqat Ali Khan, Priya Vij, Abdul Bari· Discover Computing· 0 citations
This paper proposes an anomaly detection framework that combines the sequential retraining of CAE networks with an exclusion logic strategy, and demonstrates good generalizability, seamless adaptability to varying monitoring scenarios, and robustness across both training and validation datasets.
Matheus Dalcin, Marcos Spínola, R. Finotti et al.· The Arabian journal for scie...· 0 citations
This systematic review synthesizes recent advances in AI applications for SHM across civil infrastructure including bridges, buildings, tunnels, and dams and identifies interdisciplinary opportunities including federated learning for decentralized monitoring, explainable AI for stakeholder trust, and autonomous inspect...
M. Khan, M. Ashraf, Muhammad Jahanzeb et al.· International journal of com...· 0 citations
Abstract. High-performance mechanical component structural health monitoring (SHM) is a vital issue in contemporary engineering, especially in the aerospace, automotive, and industrial turbomachinery sectors where component failure may be disastrous. This article introduces a new AI-aided SHM framework with multimodal...
Jasjeet Singh· Materials Research Proceedin...· 0 citations
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