Fall Prevention from Scaffolding Using Computer Vision and IoT-Based Monitoring

Khan, Muhammad Basit; Khalid, Rabia; Anjum, Sharjeel; Tran, Si Van-Tien; Park, Chan-Sik

doi:10.1061/(asce)co.1943-7862.0002278

Cited by 36 publications

(13 citation statements)

References 50 publications

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“…e work phase is "Work on the system scaffolds," which is entered by dividing all materials, workers, and equipment brought to the site. e system scaffolding [61] consists of steel stair, guardrail, vertical diagonal brace, vertical, 2) Check whether the walk platform is installed.…”

Section: Worker With Safety Harness and Hook Figure 4(a)mentioning

confidence: 99%

“…As illustrated in Figure 4(a), the location of each object must be verified using shop drawings and site photographs [14]. By measuring the position of each member of the system scaffolding including the guardrail, all materials were analyzed and it was determined that there was no hazard factor [61]. Because the width of the walking platform exceeds 40 cm and the gap between the walk platform materials is 3 cm or less, it was determined that there is no hazard factor for the walking platform.…”

Section: Worker Climbing the System Scaffoldingmentioning

confidence: 99%

“…e work phase "System Scaffolding Installation Work" is entered by dividing all materials, workers, and equipment brought to the site [61]. In Figure 5(a), the input system scaffolding is shown and classified as forms for column and beam as material, five workers as workers, and a crane as equipment.…”

Section: Input Of Work Phase and Field Carry-on Itemsmentioning

confidence: 99%

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Computer Vision Process Development regarding Worker’s Safety Harness and Hook to Prevent Fall Accidents: Focused on System Scaffolds in South Korea

Lim

Jung

Park

et al. 2022

Advances in Civil Engineering

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In South Korea, industrial accidents continue to increase in frequency, with construction accidents accounting for more than a third of all industrial accidents. Specifically, by preventing fall accidents, the death rate from accidents can be reduced by 50%. Fall protection is required to prevent fall accidents, and investigating the reinforcement of the worker’s safety harness and hook fastening becomes imperative. This requires automation of computer vision confirmation of the safety harness and hook fastening. As the accident risk can be reduced by an effective safety culture in the system, it is necessary to monitor safety on site through a construction safety automation system. Therefore, the objective of this study is to develop a computer vision process for safety harness and hook for preventing fall accidents in South Korea’s construction industry. This study focuses on system scaffolds that are widely used at construction sites. The application of this methodology to sample sites and field cases established its applicability. The proposed computer vision application methodology will serve as the foundation for visualization research in the construction industry, and image recognition will help reduce the safety accident rate. Accidents caused by failure to use a safety harness and hook can be reduced in South Korea as well as globally. Additionally, this methodology is applicable to roof construction, tower crane installation and dismantling work, as well as steel tower installation and dismantling.

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Section: Worker With Safety Harness and Hook Figure 4(a)mentioning

confidence: 99%

Section: Worker Climbing the System Scaffoldingmentioning

confidence: 99%

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Computer Vision Process Development regarding Worker’s Safety Harness and Hook to Prevent Fall Accidents: Focused on System Scaffolds in South Korea

Lim

Jung

Park

et al. 2022

Advances in Civil Engineering

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“…In regard to scaffolding tasks, some authors proposed the prevention of FFH using computer vision and IoT-based monitoring [76]. Their experimental results showed high accuracy in the tested scenarios.…”

Section: Case 3: Scaffolding Tasksmentioning

confidence: 99%

Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML

2022

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The main cause of fatal accidents in the construction sector are falls from height (FFH) and the inappropriate use of a harness is commonly associated with these fatalities. Traditional methods, such as onsite inspections, safety communication, or safety training, are not enough to mitigate accidents caused by FFH associated with a poor management in the use of a harness. Although some technological solutions for the automated monitoring of workers could improve safety conditions, their use is not frequent due to the particularities of construction sites: complexity, dynamic environments, outdoor workplaces, etc. Then, the integration of expert knowledge with technology is a key issue. Fuzzy logic systems (FLS) and Internet of Things (IoT) present many potential benefits, such as real-time decisions being made based on FLS and data from sensors. In the current research, the development and test of an IoT system integrated with the Java Fuzzy Markup Language Library for FLS, to support experts’ decision making in FFH, is proposed. The proposal was checked in four construction scenarios based on working conditions with different levels of risk of FFH and obtained promising results.

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“…In recent years, AI has played a positive role in complex pattern recognition and solving real-world problems. , After the emergence of machine learning (ML) and deep learning (DL), researchers have employed these technologies in various fields such as healthcare, , construction, − bioinformatics, finance, , and agriculture . In the field of nanomaterials science, researchers have started to employ AI-based DL and ML technology for RHEED interpretation, such as employing statistically available methods with ML for RHEED interpretation. ,− For instance, authors used principal component analysis (PCA) and k -means clustering to analyze different types of perovskite films to understand their stoichiometry.…”

Section: Introductionmentioning

confidence: 99%

Rotation Error Detection of Gallium Nitride (GaN) Substrate in MBE Utilizing Ensemble Learning

Anjum¹,

Lee²,

Noh³

2023

Crystal Growth & Design

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A rotating substrate is essential for achieving highquality and uniform growth of gallium nitride (GaN) thin films in molecular beam epitaxy (MBE). By rotating the substrate during growth, the deposited material can be distributed more uniformly across the surface, resulting in a more uniform and defect-free film. However, the substrate may stop rotating during fabrication, and it is a challenge for engineers to detect the rotation of the substrate using information from reflection high-energy electron diffraction (RHEED). The current method for analyzing rotation using RHEED data is manual, time-consuming, and error-prone. Therefore, this research proposes an automated machine learning-based rotation error detection (RED) model to determine wafer rotation and then alert the engineers in case of an anomaly. The proposed approach consists of (1) ensemble learning models such as random forest classifier and extreme gradient boosting (XGBoost) for rotation error detection, (2) prepared novel intensity data from RHEED videos, (3) comparison to select the best ensemble learning model at a small scale, ( 4) training XGBoost at a large scale, (5) deployment in production using MLflow, (6) development of a REST application programming interface (API) for inference, and (7) integrated KakaoTalk API to generate and send a detailed alert message to engineers so that they can take appropriate actions in time. The experimental results achieved F1 scores for normal and rotational errors of 98.9 and 83.1%, respectively. The proposed method can make a valuable contribution to epitaxial wafer growth by enhancing production efficiency, improving product quality, and reducing costs.

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Fall Prevention from Scaffolding Using Computer Vision and IoT-Based Monitoring

Cited by 36 publications

References 50 publications

Computer Vision Process Development regarding Worker’s Safety Harness and Hook to Prevent Fall Accidents: Focused on System Scaffolds in South Korea

Computer Vision Process Development regarding Worker’s Safety Harness and Hook to Prevent Fall Accidents: Focused on System Scaffolds in South Korea

Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML

Rotation Error Detection of Gallium Nitride (GaN) Substrate in MBE Utilizing Ensemble Learning

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