Document Type

Article

Publication Date

11-11-2025

Publication Title

BUILDSYS '25: Proceedings of the 12th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

First Page

456

Last Page

460

DOI

https://doi.org/10.1145/3736425.377235

Abstract

The deployment of deep learning (DL) models on edge devices offers significant opportunities for structural health monitoring (SHM), particularly by enabling localized, low-latency inference in built environments. However, practical deployment on resource-constrained platforms faces barriers in scalability, reliability, and security. In response, we introduce Zeki, a unified, security-aware, containerized pipeline for deploying DL models for edge-based SHM. Zeki unifies model optimization (via LiteRT and quantization), container-level hardening, and benchmarking-driven model-device co-selection into a reproducible workflow. We evaluate Zeki by deploying convolutional neural networks (CNNs) for crack detection on Raspberry Pi 4 and BeagleBone AI-64 along with a server. Results show significant improvements in inference latency and memory efficiency compared to unoptimized baselines. Beyond performance, Zeki establishes a systematic methodology for safe, resilient, and evidence-based edge deployment of DL in safety-critical SHM settings, enabling long-term infrastructure monitoring.

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This article was published open access under the University of Nebraska at Omaha and ACM open access publishing agreement.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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Funded by the University of Nebraska at Omaha Open Access Fund