Data-Driven Structural Health Monitoring Using Feature Fusion and Hybrid Deep Learning

“…They use clustering to determine clusters of abnormal behavior in accelerometer measurements from a bridge. In [196][197][198], accelerometer signals have been used where as [199] have used piezo electric transducers for performing structural health monitoring of bridges formulated as a classification problem between different damaged states of a bridge. The prime sensing modality for monitoring has been measuring vibration using accelerometers, however, other sensors such as fiber optic gratings can also be used to measure stress.…”

Section: Smart Industrymentioning

confidence: 99%

“…Table 11 summarizes the use of IoT based AI in Smart Infrastructure. [199] Classification-Different damage states Homogeneous (Piezo electric sensors) DNN [196] Homogeneous (Accelerometer) CNN + RNN (LSTM) [198] SVM [197] Energy and Environment management SVM [200] Cloud Regression-Forecasting electrical power usage…”

Section: Smart Industrymentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

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“…The constant growth of large-scale civil infrastructures built worldwide in recent years [1], [2] is sustained by increasing investments of the top economies in the world for renewing urban areas and roads. Furthermore, civil infrastructures are Fig.…”

Section: Introductionmentioning

confidence: 99%

Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge Health Monitoring

Moallemi

Burrello

Brunelli

et al. 2022

IEEE Internet Things J.

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Modern real-time Structural Health Monitoring systems can generate a considerable amount of information that must be processed and evaluated for detecting early anomalies and generating prompt warnings and alarms about the civil infrastructure conditions. The current cloud-based solutions cannot scale if the raw data has to be collected from thousands of buildings. This paper presents a full-stack deployment of an efficient and scalable anomaly detection pipeline for SHM systems which does not require sending raw data to the cloud but relies on edge computation. First, we benchmark three algorithmic approaches of anomaly detection, i.e., Principal Component Analysis (PCA), Fully-Connected AutoEncoder (FC-AE), and Convolutional AutoEncoder (C-AE). Then, we deploy them on an edge-sensor, the STM32L4, with limited computing capabilities. Our approach decreases network traffic by ≈ 8•10 5 × , from 780KB/hour to less than 10 Bytes/hour for a single installation and minimize network and cloud resource utilization, enabling the scaling of the monitoring infrastructure. A reallife case study, a highway bridge in Italy, demonstrates that combining near-sensor computation of anomaly detection algorithms, smart pre-processing, and low-power wide-area network protocols (LPWAN) we can greatly reduce data communication and cloud computing costs, while anomaly detection accuracy is not adversely affected.

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Data-Driven Structural Health Monitoring Using Feature Fusion and Hybrid Deep Learning

Abstract: Data-driven structural health monitoring using feature fusion and hybrid deep learning. IEEE Transactions on Automation Science and Engineering .

Cited by 83 publications

References 31 publications

Cloud-Based Digital Twinning for Structural Health Monitoring Using Deep Learning

Cloud-Based Digital Twinning for Structural Health Monitoring Using Deep Learning

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge Health Monitoring

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