Corrosion Risk Estimation for Heritage Preservation: An Internet of Things and Machine Learning Approach Using Temperature and Humidity
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908574554259456 |
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| author | Mercado, Reginald Juan M. Kabeer, Muhammad Al-Obaidy, Haider Nordin, Rosdiadee |
| author_facet | Mercado, Reginald Juan M. Kabeer, Muhammad Al-Obaidy, Haider Nordin, Rosdiadee |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02973 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Corrosion Risk Estimation for Heritage Preservation: An Internet of Things and Machine Learning Approach Using Temperature and Humidity Mercado, Reginald Juan M. Kabeer, Muhammad Al-Obaidy, Haider Nordin, Rosdiadee Computers and Society Artificial Intelligence Networking and Internet Architecture Systems and Control 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 |
| title | Corrosion Risk Estimation for Heritage Preservation: An Internet of Things and Machine Learning Approach Using Temperature and Humidity |
| topic | Computers and Society Artificial Intelligence Networking and Internet Architecture Systems and Control |
| url | https://arxiv.org/abs/2510.02973 |