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Analysis of high-rise building safety detection methods based on big data and artificial intelligence

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  • Jiaojiao Xu
  • Chuanjie Yan
  • Yangyang Su
  • Yong Liu

Abstract

With rapid industrialization, the construction of high-rise buildings is a good and effective solution to the rational and effective use of land resources and alleviation of existing land resource tensions. Especially in the construction process, if there is a problem with the pile foundation, the building will inevitably be tilted, which will directly affect the personal safety of the construction workers and resident users. The experiments in this article use the concept of big data to divide the system into modules such as data collection, data preprocessing, feature extraction, prediction model building, and model application in order to provide massive data storage and parallel computing services to form a security test system. The experimental data show that wireless sensor technology is applied to the inclination monitoring of buildings, and a monitoring system based on wireless inclination sensors is designed to enable real-time dynamic monitoring of buildings to ensure human safety. When the experimental model frame is stable under normal environmental conditions, a nonstationary vibration is artificially produced for a period of time from the outside world, which is about 60 s higher than the traditional method, and the efficiency is also increased by about 80%, a situation where a building has a reversible tilt change.

Suggested Citation

  • Jiaojiao Xu & Chuanjie Yan & Yangyang Su & Yong Liu, 2020. "Analysis of high-rise building safety detection methods based on big data and artificial intelligence," International Journal of Distributed Sensor Networks, , vol. 16(6), pages 15501477209, June.
    • Handle: RePEc:sae:intdis:v:16:y:2020:i:6:p:1550147720935307
    DOI: 10.1177/1550147720935307
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    References listed on IDEAS

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    Cited by:

    1. Mingzhi Song & Zheng Zhu & Peipei Wang & Kun Wang & Zhenqi Li & Cun Feng & Ming Shan, 2023. "An Alternative Rural Housing Management Tool Empowered by a Bayesian Neural Classifier," Sustainability, MDPI, vol. 15(3), pages 1-18, January.

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