Incident Analysis and Prediction of Safety Performance on Construction Sites

Awolusi, Ibukun; Marks, Eric; Hainen, Alexander M.; Alzarrad, Ammar

doi:10.3390/civileng3030039

Cited by 7 publications

(4 citation statements)

References 35 publications

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“…This widespread belief underscores the importance of reporting near-misses for enhancing safety. According to the WHO, near-misses are defined as a severe mistake with the potential to inflict harm but not due to luck or interception [31]. The OSHA defines a near-miss as an incident which could have led to severe injury or sickness but did not [32].…”

Section: Near-miss Definition: Worldwide Perspectivementioning

confidence: 99%

Near-Miss Detection Metrics: An Approach to Enable Sensing Technologies for Proactive Construction Safety Management

Hashmi,

Hassan,

Zubair

et al. 2024

Buildings

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One in every five occupational deaths occurs in the construction sector. A proactive approach for improving on-site safety is identifying and analyzing accident precursors, such as near-misses, that provide early warnings of accidents. Despite the importance of near-misses, they are frequently left unreported and unrecorded in the construction sector. The adoption of modern technologies can prevent accidents by automated data collection and analysis. This study aims to develop near-miss detection metrics to facilitate the automated detection of near-misses through sensors. The study adopted a mixed method approach including both qualitative and quantitative approaches. First, a quantifiable definition of near-misses was developed from the literature. Hazards, accidents, and the causes of accidents were identified. Through empirical and statistical analyses of accidents from the OSHA repository, combinations of unsafe acts and conditions responsible for a near-miss were identified. The identified factors were analyzed using a frequency analysis, correlation, and a lambda analysis. The results revealed twelve significant near-misses, such as A1—approach to restricted areas and C2—unguarded floor/roof openings, A5—equipment and tool inspection was incomplete and C8—unsafely positioned ladders and scaffolds, A2—no or improper use of PPE and C2—unguarded floor or roof openings, etc. Lastly, measurable data required by sensors for autonomous detection of near-misses were determined. The developed metric set the basis for automating near-miss reporting and documentation using modern sensing technology to improve construction safety. This study contributes to improving construction safety by addressing the underreporting of near-miss events. Overall, the developed metrics lay the groundwork for enhancing construction safety through automated near-miss reporting and documentation. Furthermore, it helps for the establishment of safety management schemes in the construction industry, specifically in identifying near-misses. This research offers valuable insight into developing guidelines for safety managers to improve near-miss reporting and detection on construction sites. In sum, the findings can be valuable for other industries also looking to establish or assess their own safety management systems.

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Section: Near-miss Definition: Worldwide Perspectivementioning

confidence: 99%

Near-Miss Detection Metrics: An Approach to Enable Sensing Technologies for Proactive Construction Safety Management

Hashmi,

Hassan,

Zubair

et al. 2024

Buildings

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“…On the contrary, leading indicators are measurements related to accident prevention [54]. One way to record leading indicators is by counting the frequency of several safety management efforts in preventing an incident [55].…”

Section: Safety Performancementioning

confidence: 99%

Influence of Safety Climate on Safety Performance in Gas Stations in Indonesia

Lestari

Modjo

Wibowo

et al. 2023

Safety

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Accidents in gas stations may cause injury or even death to people. Moreover, an accident in a gas station might halt its operation for some time. When an accident takes place in a region with scant amount of gas stations, it may lead to fuel scarcity in the area, affecting the economy and sparking other issues. Therefore, safety climate and safety performance in gas stations need to be evaluated. Safety climate has been used as a tool to assess the safety performance of an organization at a given time. The aim of this study was to understand the influence of safety climate on safety performance in gas stations in Indonesia. A total of 129 gas stations were selected. To assess safety climate, a safety climate questionnaire was used, while the safety performance was captured by conducting on site structured observations. The assessment shows that gas stations have a good level of safety climate, especially when it is compared with other industrial sectors in Indonesia, such as construction. The study findings also show that safety climate has a significant positive impact on safety performance. Three safety climate dimensions with significant positive influence on safety performance are management commitment, communication, and personal accountability. Therefore, improvement efforts should focus more on these dimensions to boost safety performance in gas stations.

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“…Unsafe behavior has been defined as unacceptable practices with the potential to contribute to future accidents and injuries [ 25 ]. Examples of unsafe behaviors or acts that are commonly observed on construction sites include non-compliance with PPE use, improper lifting, and working or standing too close to heavy construction equipment [ 26 ]. Monitoring and changing the behaviors of workers engaging in unsafe acts can help to solve safety problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Unmanned Aerial Systems and Deep Learning for Safety and Health Activity Monitoring on Construction Sites

Akinsemoyin

Awolusi

Chakraborty

et al. 2023

Sensors

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Construction is a highly hazardous industry typified by several complex features in dynamic work environments that have the possibility of causing harm or ill health to construction workers. The constant monitoring of workers’ unsafe behaviors and work conditions is considered not only a proactive but also an active method of removing safety and health hazards and preventing potential accidents on construction sites. The integration of sensor technologies and artificial intelligence for computer vision can be used to create a robust management strategy and enhance the analysis of safety and health data needed to generate insights and take action to protect workers on construction sites. This study presents the development and validation of a framework that implements the use of unmanned aerial systems (UASs) and deep learning (DL) for the collection and analysis of safety activity metrics for improving construction safety performance. The developed framework was validated using a pilot case study. Digital images of construction safety activities were collected on active construction sites using a UAS, and the performance of two different object detection deep-learning algorithms/models (Faster R-CNN and YOLOv3) for safety hardhat detection were compared. The dataset included 7041 preprocessed and augmented images with a 75/25 training and testing split. From the case study results, Faster R-CNN showed a higher precision of 93.1% than YOLOv3 (89.8%). The findings of this study show the impact and potential benefits of using UASs and DL in computer vision applications for managing safety and health on construction sites.

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Incident Analysis and Prediction of Safety Performance on Construction Sites

Cited by 7 publications

References 35 publications

Near-Miss Detection Metrics: An Approach to Enable Sensing Technologies for Proactive Construction Safety Management

Near-Miss Detection Metrics: An Approach to Enable Sensing Technologies for Proactive Construction Safety Management

Influence of Safety Climate on Safety Performance in Gas Stations in Indonesia

Unmanned Aerial Systems and Deep Learning for Safety and Health Activity Monitoring on Construction Sites

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