Evidence map›Paper›PMID 34770381›Full record

ArticleSensors (Basel, Switzerland)2021

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

Cynthia Changxin Wang, Mudan Wang, Jun Sun, Mohammad Mojtahedi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Cynthia Changxin WangSchool of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia.ORCID 0000-0001-6414-3228
Mudan WangSchool of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia.ORCID 0000-0001-7862-1286
Jun SunSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Mohammad MojtahediSchool of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia.ORCID 0000-0001-6942-0650

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AlgorithmsSafety ManagementComputer SimulationHumansaccident preventionaxis aligned bounding box methodconstruction machinerylocation sensing technologysafety warning algorithm

Identifiers

PMID34770381
PMCPMC8587039

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.