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基于轴对齐边界框方法的安全警告算法,以防止移动施工机械现场事故。

A Safety Warning Algorithm Based on Axis Aligned Bounding Box Method to Prevent Onsite Accidents of Mobile Construction Machineries.

机构信息

School of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia.

School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.

出版信息

Sensors (Basel). 2021 Oct 25;21(21):7075. doi: 10.3390/s21217075.

DOI:10.3390/s21217075
PMID:34770381
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8587039/
Abstract

Mobile construction machineries are accident-prone on a dynamic construction site, as the site environment is constantly changing and continuous safety monitoring by human beings is impossible. These accidents usually happen in the form of machinery overturning or collapsing into risk areas, including the foundation pit, slopes, or soft soil area. Therefore, preventing mobile construction machineries from entering risk areas is the key. However, currently, there is a lack of practical safety management techniques to achieve this. Utilizing a wireless sensor device to collect the location information of mobile construction machineries, this research develops a safety warning algorithm to prevent the machineries moving into risk area and reduces onsite overturning or collapsing accidents. A modified axis aligned bounding box method is proposed according to the movement patterns of mobile construction machineries, and the warning algorithm is developed based on the onsite safety management regulations. The algorithm is validated in a real case simulation when machinery enters the warning zone. The simulation results showed that the overall algorithm combining the location sensing technology and the modified bounding box method could detect risk and give warnings in a timely manner. This algorithm can be implemented for the safety monitoring of mobile construction machineries in daily onsite management.

摘要

移动施工机械在动态施工现场容易发生事故,因为现场环境不断变化,人类不可能进行持续的安全监控。这些事故通常以机械翻倒或陷入危险区域(包括基坑、边坡或软土区域)的形式发生。因此,防止移动施工机械进入危险区域是关键。然而,目前缺乏实用的安全管理技术来实现这一目标。本研究利用无线传感器设备收集移动施工机械的位置信息,开发了一种安全预警算法,以防止机械进入危险区域,并减少现场翻倒或坍塌事故。根据移动施工机械的运动模式,提出了一种改进的轴对齐边界框方法,并根据现场安全管理规定开发了预警算法。当机械进入预警区域时,在真实案例模拟中对算法进行了验证。模拟结果表明,结合位置感测技术和改进的边界框方法的整体算法可以及时检测到风险并发出警报。该算法可用于日常现场管理中移动施工机械的安全监控。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/84aa6698b7de/sensors-21-07075-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/d738c1fe5239/sensors-21-07075-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/997b3007f3b0/sensors-21-07075-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/45b2a5de23dc/sensors-21-07075-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/6d80a6019c2d/sensors-21-07075-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/f2c5c5d4f195/sensors-21-07075-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/d2d107e25d04/sensors-21-07075-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/175f780bd09e/sensors-21-07075-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/d8b4deec1c87/sensors-21-07075-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/cab18b883026/sensors-21-07075-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/06959a592fd8/sensors-21-07075-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/d0cdab6e6fa0/sensors-21-07075-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/653afa5d6147/sensors-21-07075-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/84aa6698b7de/sensors-21-07075-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/d738c1fe5239/sensors-21-07075-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/997b3007f3b0/sensors-21-07075-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/45b2a5de23dc/sensors-21-07075-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/6d80a6019c2d/sensors-21-07075-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/f2c5c5d4f195/sensors-21-07075-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/d2d107e25d04/sensors-21-07075-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/175f780bd09e/sensors-21-07075-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/d8b4deec1c87/sensors-21-07075-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/cab18b883026/sensors-21-07075-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/06959a592fd8/sensors-21-07075-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/d0cdab6e6fa0/sensors-21-07075-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/653afa5d6147/sensors-21-07075-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d230/8587039/84aa6698b7de/sensors-21-07075-g013.jpg

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