Differential Settlement Monitoring System Using Inverse Perspective Mapping

Lim, King Hann; Lim, Chin Hong; Lim, Wen Loong; Yap, Huei Ee

doi:10.1109/jsen.2018.2791201

Cited by 7 publications

(1 citation statement)

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“…With the explosive development of China's real estate industry, the living conditions of the vast population have greatly improved, and high-rise and super-high-rise buildings are emerging continuously. However, over time, buildings inevitably undergo structural changes, such as deformation and settlement, which may lead to significant safety issues [1][2][3]. Therefore, to effectively prevent the above safety hazards, accurate monitoring of various parameters of the building structure is necessary to reasonably assess its health status.…”

Section: Introductionmentioning

confidence: 99%

Improved detection of building settlement debris based on deep learning

Wang,

Han,

Dou

2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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In response to the issues of low reliability and poor prediction accuracy in traditional building structure settlement monitoring, a method for building settlement monitoring based on multiple sensors and Radial Basis Function (RBF) neural network is proposed. Building settlement information is collected and wirelessly transmitted using various sensors and hardware devices, including GPRS communication modules. The monitoring data collected by sensors are compared and analyzed to determine the settlement status of the building. An RBF neural network prediction model is constructed for potential settlement points. Additionally, the structural parameters of the RBF neural network are optimized using the leapfrog algorithm. Experimental results demonstrate that this method can accurately assess potential building structure settlements in real-world environments with small prediction errors. The maximum relative error is 4.83%, indicating good predictive capabilities.

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Section: Introductionmentioning

confidence: 99%