Development of a Cloud-Based Real-Time Building Health Monitoring and Prediction System Using AI and IoT Sensors

Autor:	Chang-Wan Ha, Byungtae Ahn, Young-Sik Shin, Jinseong Park, Jai-Kyung Lee, Jungjip Kim
Rok vydání:	2021
Zdroj:	Journal of the Korean Society of Hazard Mitigation. 21:31-39
ISSN:	2287-6723 1738-2424
Popis:	In this study, a cloud-based real-time building health monitoring and prediction system using AI and IoT sensors was developed. To predict the building condition, which constitutes time-series data, statistical-based ARIMA and AI-based LSTM prediction models were designed, and the effectiveness of the proposed prediction models was experimentally verified using a 1/8-scaled miniaturized structure. The prediction accuracy in terms of MAPE (less than 1%) was experimentally confirmed to be satisfactory. Moreover, a method for analyzing dimensional structure deformation was developed by combining multiple sensor measurements, and its effectiveness was verified through the case study of a real earthquake-damaged building.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b3fab03d8b7a2720aaf0f43806ffbb27 https://doi.org/10.9798/kosham.2021.21.6.31 Zobrazit plný text záznamu