桥梁伸缩缝的完整性和可靠性是保障行车安全、延长基础设施使用寿命的关键。然而，传统检测方法往往存在重复性高、耗时长以及对交通运行干扰较大等问题。为克服这些局限性，本文提出了一种用于长期监测桥梁伸缩缝健康状态的端云融合声学监测系统。在多座桥梁、为期18个月的监测期间，共采集了约21,000条桥梁伸缩缝声纹数据且由经验丰富的公路工程师标注，制作成可靠的声纹识别数据集。基于该数据集，本文构建了一种自适应端云融合检测算法框架。边缘设备采用基于支持向量机的级联分类模型，实现轻量化、实时推理；云端则利用基于门控循环单元的深度学习模型进行更精确的分析。其中，部署于边缘设备的级联分类模型分类准确率为96.7%，其95%置信区间为(0.9632, 0.9668)；云端的基于门控循环单元的模型准确率为98.2%，其95%置信区间为(0.9783, 0.9823)。此外，本文所提出的自适应两级分类策略将数据传输量降低至总数据量的5%以下。实验结果表明，该系统能够为桥梁伸缩缝健康监测提供一种可靠、高效且准确的解决方案。

Ensuring the structural integrity of bridge expansion joints is critical for maintaining traffic safety and prolonging infrastructure lifespan. However, conventional inspection methods are often labor-intensive, time-consuming, and disruptive to traffic flow. To overcome these limitations, this study proposes an edge–cloud collaborative acoustic monitoring system for the long-term health assessment of bridge expansion joints. Over an 18-month monitoring period spanning multiple bridges, approximately 21,000 acoustic signature samples of bridge expansion joints were collected. To ensure accuracy and reliability, all samples were meticulously annotated by experienced highway engineers. Based on this dataset, an adaptive edge–cloud collaborative classification framework was developed. Specifically, the edge device employs a cascade classification model based on Support Vector Machines (SVM) for real-time, lightweight inference, while the cloud leverages a deep learning model based on Gated Recurrent Units (GRU) to perform more complex analysis. The cascaded classification model deployed on the edge device achieved an accuracy of 96.7%, with a 95% confidence interval (CI) of (0.9632, 0.9668). In comparison, the GRU-based classifier running on the cloud attained a higher accuracy of 98.2%, with a 95% CI of (0.9783, 0.9823). Furthermore, the proposed adaptive two-stage classification strategy reduced data transmission to less than 5% of the total collected data. These experimental results demonstrate that the proposed system offers a reliable, efficient, and accurate solution for the acoustic health monitoring of bridge expansion joints.