From Instrumented Towers to Vulnerable Blocks: A Physics-Informed Federated Edge Digital-Twin Framework for Translational Seismic Damage Assessment in Andean Emerging Economies (NSET-Andes)
Abstract
Abstract Structural Health Monitoring (SHM) in seismic megacities faces a persistent trade-off between the fidelity of centralized cloud-based Digital Twins (DT) and the latency, bandwidth, and single-point-of-failure risks these architectures incur during network-congested disaster scenarios. This paper proposes NSET-Andes, a physics-informed Federated Learning (FL) and edge-computing framework for real-time seismic damage estimation, and — as its central contribution — a structured translational methodology for adapting instrumentation practices developed for Tokyo's high-rise steel moment-resisting frames to Peru's markedly different, largely undocumented building stock. The framework embeds verified structural-dynamics formulations — the multi-degree-of-freedom (MDOF) equation of motion integrated via the Newmark-β method, the Bouc-Wen hysteretic model, the Park-Ang damage index (DPA), and Gazetas' frequency-dependent soil-structure interaction (SSI) impedance functions — as physics residual penalties inside a federated training loop (FedAvg), so that gradient-only communication replaces raw-data transmission. Performance figures reported here (inference latency, bandwidth reduction, drift-prediction accuracy) are derived from a synthetic Nankai Trough-calibrated benchmark scenario and are presented as preliminary, simulation-based indicators — not as field-validated measurements — pending instrumented pilot testing. The principal gap this work addresses is the absence,…
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