Corrosion Risk Estimation for Heritage Preservation: An Internet of Things and Machine Learning Approach Using Temperature and Humidity
This addresses corrosion risk estimation for heritage preservation, particularly at sites with limited monitoring resources, though it is incremental as it applies existing methods to a new domain.
The study tackled the problem of forecasting atmospheric corrosion for steel structures at heritage sites by developing an IoT and machine learning framework using only temperature and humidity data, resulting in accurate predictions that enable proactive preservation with a scalable, cost-effective approach.
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