用温湿度数据预测古建钢构腐蚀，低成本实现智能保护
Corrosion Risk Estimation for Heritage Preservation: An Internet of Things and Machine Learning Approach Using Temperature and Humidity
- 仅用温湿度数据构建机器学习模型预测腐蚀速率
- 三年实测数据验证，预测准确率高且可实时更新
- 适合资源有限的文物遗址，部署简单易推广
对菲律宾圣塞巴斯蒂安大教堂等具有文化意义的遗产建筑中的钢构件进行主动保护，需要精确的腐蚀预测。本研究开发了一套基于物联网的硬件系统，通过LoRa无线通信监测具有钢结构的遗产建筑。利用该系统三年生成的数据集，构建了一个仅使用温度和相对湿度数据的机器学习框架，用于预测大气腐蚀速率。该框架通过Streamlit仪表盘部署，并结合ngrok隧道实现公众访问，提供实时腐蚀监测与可操作的保护建议。这种低数据依赖的方法在监测资源有限的遗产地具备可扩展性和成本效益，表明仅凭基础气象数据即可实现高精度腐蚀预测，从而在全球范围内推动重要文化遗产的主动保护，无需复杂传感器网络。
原文摘要 · Abstract (English)
Proactive preservation of steel structures at culturally significant heritage sites like the San Sebastian Basilica in the Philippines requires accurate corrosion forecasting. This study developed an Internet of Things hardware system connected with LoRa wireless communications to monitor heritage buildings with steel structures. From a three year dataset generated by the IoT system, we built a machine learning framework for predicting atmospheric corrosion rates using only temperature and relative humidity data. Deployed via a Streamlit dashboard with ngrok tunneling for public access, the framework provides real-time corrosion monitoring and actionable preservation recommendations. This minimal-data approach is scalable and cost effective for heritage sites with limited monitoring resources, showing that advanced regression can extract accurate corrosion predictions from basic meteorological data enabling proactive preservation of culturally significant structures worldwide without requiring extensive sensor networks
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