Automated detection of construction work at heights and deployment of safety hooks using IMU with a barometer

Choo, Hunsang; Lee, Bogyeong; Kim, Hyunsoo; Choi, Byungjoo

doi:10.1016/j.autcon.2022.104714

Cited by 22 publications

(2 citation statements)

References 90 publications

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“…In construction process field, 20 out of 61 observations were made regarding the construction of buildings, dams, roads, and tunnels. Within this field, the 60 out of 202 observations covered topics such as construction delays [22], crane, drilling and excavation tasks [14,18,24,39,48,70,74]; geological conditions [54], scaffolding collapse [68]; transport delays [31]; tunnelling [28,36,37,41,43,55,67]; workers and machinery location [34,40]. According to Erzaij et al [22], project suspensions are among the most persistent tasks facing the construction sector, due to the difficulty of the industry and the essential interdependence between the bases of delay risk.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

Pireddu,

Bedini,

Lombardi

et al. 2024

Preprint

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Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programmes of the National Institute for Occupational Accident Insurance (Inail). Objectives: The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate to be applied to certain investigation objectives, types, and sources of data, as defined by the authors. Methods: Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies, were based on the Preferred Reporting Items for Systematic Review and meta-analysis (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we use principal component analysis (PCA) and meta-analysis. Results: The search strategy based on the PRISMA eligibility criteria, provided us with 61 out of 2,234 potential articles, 202 observation, 91 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource type. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: "supervised methods, institutional dataset, and predictive and classificatory purposes" (correlation 0.97÷8.18E-01; p-value 7.67E-55÷1.28E-22) and the second, Dim2 "not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment" (corr. 0.84÷0.47; p-value 5.79E-25÷3.59E-06). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry. Conclusions: The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53÷0.96) compared to not-supervised methods.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

Pireddu,

Bedini,

Lombardi

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Other studies insisted on the implementation of on-site precautionary measures such as reducing unguarded openings and the use of personal fall arrest systems (PFAS) [22,23]. Additionally, modern technologies, including sensors and alarm technologies, have been integrated into wearable devices to enhance the effectiveness of traditional equipment [24]. However, despite the known risk of severe injuries, many workers still do not use personal arrest systems [25].…”

Section: Introductionmentioning

confidence: 99%

Virtual Assessment of a Representative Torso Airbag under the Fall from Height Impact Conditions

Aranda-Marco,

Peldschus

2023

Safety

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A fall from height is the main cause of serious injuries and fatalities in occupational and work-related accidents, especially construction. Falls from scaffolds, ladders, or roofs are very frequent accident scenarios. Especially for those falls from a height of 1 m to 6 m, the use of wearable smart airbags has been proposed to mitigate possible torso injuries. In this study, a virtual assessment of such an inflatable protector was conducted using numerical simulations and finite element human body models in order to determine its impact-protection performance under realistic impact conditions and identify its possible limitations. The findings obtained from the simulation study showed a significant protective effect provided by the airbag, mitigating a multiple rib fracture scenario and reducing the risk of internal organ injuries for those falling from four meters of height or less. The use case analyzed in this research demonstrates the suitability of using a virtual environment not only to evaluate current protectors but also to develop new protector devices, which could improve occupational safety.

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Artificial intelligence in infrastructure construction: A critical review

Chen,

Zhou,

Bao

et al. 2024

Front. Eng. Manag.

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Artificial intelligence (AI) has emerged as a promising technological solution for addressing critical infrastructure construction challenges, such as elevated accident rates, suboptimal productivity, and persistent labor shortages. This review aims to thoroughly analyze the contemporary landscape of AI applications in the infrastructure construction sector. We conducted both quantitative and qualitative analyses based on 594 and 91 selected papers, respectively. The results reveal that the primary focus of current AI research in this field centers on safety monitoring and control, as well as process management. Key technologies such as machine learning, computer vision, and natural language processing are prominent, with significant attention given to the development of smart construction sites. Our review also highlights several areas for future research, including broadening the scope of AI applications, exploring the potential of diverse AI technologies, and improving AI applications through standardized data sets and generative AI models. These directions are promising for further advancements in infrastructure construction, offering potential solutions to its significant challenges.

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Automated detection of construction work at heights and deployment of safety hooks using IMU with a barometer

Cited by 22 publications

References 90 publications

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

Virtual Assessment of a Representative Torso Airbag under the Fall from Height Impact Conditions

Artificial intelligence in infrastructure construction: A critical review

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