Corrosion Risk Estimation for Heritage Preservation: An Internet of Things and Machine Learning Approach Using Temperature and Humidity

2025-10-03

AI Summary

Predicting corrosion risk for steel structures at cultural heritage sites faces challenges including scarcity of temperature-humidity data, high monitoring costs, and complex system deployment. Method: This study proposes a lightweight machine learning framework that relies solely on temperature and humidity sensor data. A low-power LoRa-based wireless monitoring network is constructed, and a high-accuracy atmospheric corrosion rate regression model is trained using three years of in-situ measurements. A remotely accessible, interactive decision-support dashboard is developed using Streamlit and ngrok to enable real-time corrosion risk visualization and early warning. Contribution/Results: The system has been successfully deployed and operated stably over the long term at the Basilica of Saint Sebastian. It significantly reduces hardware investment and operational maintenance costs while demonstrating strong robustness and global scalability. This work establishes a cost-effective, easily deployable, and high-precision technical paradigm for proactive conservation of cultural heritage assets.

Technology Category

• Machine Learning: Hardware-aware ML
• Data Mining & Knowledge Management: Web
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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

Problem

• Predicting atmospheric corrosion rates using temperature and humidity data
• Developing IoT monitoring system for heritage steel structure preservation
• Creating cost-effective corrosion forecasting for resource-limited heritage sites

Innovation

• IoT system monitors heritage buildings via LoRa
• Machine learning predicts corrosion from temperature humidity
• Streamlit dashboard enables real-time corrosion monitoring