Integrity monitoring of vertical and horizontal structures via visualization and statistical inspection of a mesh sensor network

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ID: 285503
2021
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Abstract
Studies have been conducted to understand how mild but frequent tremors affect the integrity of built structures such as of buildings and bridges. These have been done through the analysis of data gathered using tremor sensors that are either attached on or embedded within the structures. However, current tools used to analyze the sensor data usually require in-depth knowledge of seismic data and expertise in structural engineering to be able to interpret the generated seismic graphs. Not only are the data difficult to interpret, but systems being used to gather and process these data tend to be very expensive and rely only on prevailing seismic activities. This study presents a novel approach in monitoring and analyzing the structural integrity of buildings located in earthquake-prone areas through the use of a mesh of tremor sensors. Data collected from these numerous sensors are analyzed so that specific areas of built structures that may have some structural defect may be identified. These identified probable anomalies in the structure may then be the subject of a more thorough investigation. Anomaly detection in the structures is done through the use of unsupervised machine learning techniques that yield the expected movement readings of each area in the building, given the movement in the surrounding areas. Aside from making it easy to visually inspect the movement of the built structure, this study also proposes the use of statistical tests, specifically the chi-statistic that is fed to the Kruskal-Wallis test, in order to isolate the specific locations in a building where a structural anomaly might be found. Experiments were conducted using actual 2013 earthquake data from Bohol, Philippines, and applied on hypothetical healthy and damaged buildings that were constructed using the ETABS simulation software.
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Authors Lu, Ervin Lester
Journal Malay Journal
Year 2021
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdm_comsci/4
Keywords Keywords not found

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