High-Performance Computation for Real-time Structural Health Monitoring

Structural Health Monitoring (SHM) systems are critical for ensuring the safety and reliability of infrastructure. However, the complexity and scale of modern SHM systems demand high-performance computation across various aspects. Here, I outline six key areas where computational resources can significantly enhance SHM capabilities.

Real-Time Data Acquisition and Processing is the foundation of SHM, involving the collection of large volumes of data from dense low-cost sensor networks deployed across structures (Linderman et al., 2011; Rainieri et al., 2011). Sensors such as accelerometers (and other types of sensors) generate high-frequency data streams, often reaching 1000 Hz. High-performance computing (HPC) systems are required to ingest, preprocess, and analyze this data in real time, ensuring the timely detection of anomalies and providing actionable insights during critical events such as high-intensity earthquake events.

Advanced Signal Processing is essential for extracting meaningful structural features such as natural frequencies, damping ratios, interstory-drifts and mode shapes (Yan et al., 2017). Techniques like Fourier Transform, Wavelet Transform, and cross-correlation demand significant computational resources, especially when applied to large-scale datasets or transient events. HPC accelerates these analyses, enabling real-time assessments of structural behavior and enhancing the accuracy of damage detection.

Damage Detection and Localization is one of the most computationally intensive aspects of SHM. Identifying and pinpointing structural damage requires solving inverse problems and applying sophisticated algorithms like mode shape curvature analysis or strain energy methods. Real-time HPC capabilities are critical during extreme events, enabling early detection and localization of damage, which can significantly mitigate risks and inform emergency response.

Finite Element (FE) Model Updating bridges the gap between real-world structural behavior and numerical models, enabling SHM systems to identify subtle changes in structural integrity (Mordini et al., 2007; Fusco et al., 2024). This process involves iteratively updating finite element models to reflect observed data, which requires solving large-scale numerical simulations and optimization problems. For example, the OpenSees (Open System for Earthquake Engineering Simulation) framework (Brandenberg, 2004) is widely used for simulating the dynamic behavior of complex structures under seismic loading. When integrated into SHM workflows, OpenSees can run real-time simulations to compare measured responses with model predictions, identify discrepancies, and refine model parameters accordingly. However, such iterative simulations are computationally intensive, particularly for large structures or high-fidelity models. High-performance computation accelerates these updates, ensuring timely convergence and improving the accuracy of structural assessments, which is critical for real-time damage detection and risk evaluation.

Machine Learning and Data Analytics play a transformative role in SHM by automating anomaly detection, classification, and prediction tasks. Training deep learning models on high-dimensional data and performing real-time inference for damage assessment require substantial computational power (Sun et al., 2021; Malekloo et al., 2022; Boccagna et al., 2023). HPC systems and GPU acceleration are indispensable for deploying robust, scalable machine learning pipelines that can handle the complexities of SHM data (Entezami et al., 2020).

Visualization and Decision Support enhance the interpretability of SHM results through high-resolution 3D visualizations and real-time dashboards. Rendering complex structural models and integrating live data streams require substantial computational resources. 

Incorporating high-performance computation across these areas will significantly advance SHM systems, enabling real-time monitoring, accurate diagnostics, and efficient decision-making. These capabilities will not only improve the safety and reliability of infrastructure but also optimize maintenance strategies and reduce long-term costs.

Figure: Finite‐element model of the 52‐story building showing the modal shapes (Source: Kohler et al., 2016)

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