Struck-by Accidents Research Articles

Effectiveness of alarm sounds in preventing operator habituation to auditory warnings in construction equipment

A proximity auditory warning (AW) system has been developed to mitigate accidents involving construction equipment blind spots. However, frequent AW sounds can lead to habituation among construction equipment operators, potentially reducing the system's effectiveness. This paper assessed the effectiveness of AW sounds in mitigating habituation. Twenty-one participants underwent a driving simulation, with reaction time and skin conductance level used as indicators of habituation. Participants' responses to the conventional probe-tone sound were compared with three proposed AW sounds: an auditory icon resembling a threat or dangerous situation, a self-owned name (SON) addressing the participant's name, and a combination of both. SON is recommended as a viable alternative for effective alarm sound. By investigating alarm sounds that enhance operators' responsiveness to obstacles in blind spots, particularly in environments where frequent alarms are unavoidable, this paper contributes to reducing struck-by accidents involving construction equipment.

Effect of additively manufactured polymeric inserts on impact response of construction safety helmets

Purpose Struck-by accidents (i.e. being hit by a falling object) are a leading cause of traumatic brain injuries in the construction industry. Despite the critical role of hard hats in minimizing such injuries, their overall design has not appreciably changed in decades. Therefore, this study aims to explore the potential benefits of modifying commercially available hard hat designs by incorporating a compliant cantilever and a sacrificial, energy-absorbing structure to enhance their protective capabilities against impacts. Design/methodology/approach This study involved conducting experimental impact tests to obtain the head acceleration attenuation using hard hats with a variety of compliant cantilever lattice insert designs. These lattice inserts were additively manufactured using three polymeric materials, including polylactide (PLA), acrylonitrile butadiene styrene, high-impact polystyrene and three porosity levels. A Hybrid III head/neck assembly was fitted with each hard hat design, and experimental drop tests were conducted using a 1.8-kg steel impactor dropped from 1.83 m. The maximum acceleration and head injury criterion (HIC) values were obtained for each test. Findings Analysis of variance revealed that HIC was significantly reduced for all lattices with 56% porosity (p < 0.023) compared to the control (unmodified) hard hat. The most effective insert was found to be a PLA insert with 56% porosity, which reduced the HIC value by 38% compared to the control (unmodified) hard hat, with a statistically significant p-value of 0.018. Originality/value The data present in this study reveals that simple and inexpensive modifications can be made to existing hard hat designs to reduce injury risk from overhead impacts.

Exploring construction workers' attention and awareness in diverse virtual hazard scenarios to prevent struck-by accidents

Repetitive tasks in construction reduce workers' attentiveness of hazards on sites. This decline in attentiveness can be influenced by their situation awareness level. However, research on the relationship between attentiveness and situation awareness is scarce in the construction safety domain. This study employs eye-tracking techniques to investigate how visual attention changes due to variations in workers’ situation awareness levels. A virtual reality-based experiment was conducted to evaluate the situation awareness level of individual workers and examine its relationship with attentiveness towards different hazards using the situation awareness global assessment technique and linear regression analysis. The experimental findings reveal that the overall trend of allocating attention toward hazards declined over time. Furthermore, the attentiveness of workers varied depending on their situation awareness levels and the type of hazardous condition. Throughout the experiment, the group with a low situation awareness failed to sustain their vigilance toward hazards as effectively as the group with a high situation awareness. The outcomes of this study will help construction safety trainers understand variations in workers' vigilance behavior over time and, thus mitigate the risk of accidents owing to inattentiveness at job sites.

RETRACTED: Vision-based tracking method of nighttime construction workers by integrating YOLOv5 and Deepsort

Due to poor visibility and fatigue factors, although nighttime construction has been widely used, its safety problems like struck-by accidents have also become increasingly prominent. Most of the current tracking methods of workers are not suitable for direct application in nighttime construction scenarios, so this research proposes a vision-based method, which integrates low-light image enhancement technology, YOLOv5 and Deepsort to track nighttime workers. The proposed method is mainly composed of four modules, including illumination enhancement module, detection module, the Kalman filter and matching module. In the experiment based on nine test videos, the method achieved the average multiple-object tracking accuracy (MOTA) of 89.93% and multiple-object tracking precision (MOTP) of 97.07%. At the same time, the experimental results also show that the method is robust to the common tracking challenges of occlusions, scale variations and posture variations. The proposed method has practical application potential in the monitoring task in nighttime construction, which makes the nighttime construction activities safer and more efficient.

RETRACTED: Vision-based tracking method of nighttime construction workers by integrating YOLOv5 and Deepsort (REDIRECT)

Due to poor visibility and fatigue factors, although nighttime construction has been widely used, its safety problems like struck-by accidents have also become increasingly prominent. Most of the current tracking methods of workers are not suitable for direct application in nighttime construction scenarios, so this research proposes a vision-based method, which integrates low-light image enhancement technology, YOLOv5 and Deepsort to track nighttime workers. The proposed method is mainly composed of four modules, including illumination enhancement module, detection module, the Kalman filter and matching module. In the experiment based on nine test videos, the method achieved the average multiple-object tracking accuracy (MOTA) of 89.93% and multiple-object tracking precision (MOTP) of 97.07%. At the same time, the experimental results also show that the method is robust to the common tracking challenges of occlusions, scale variations and posture variations. The proposed method has practical application potential in the monitoring task in nighttime construction, which makes the nighttime construction activities safer and more efficient.

Perspectives of Contributing Factors and Prevention Measures of Struck-by Accidents in from Experienced Safety Personnel

Malaysia’s construction industry has been long characterized as risky, as evidencedby its poor health and safety performance. The statistics on fatal accidents recorded by the Department of Safety and Health Malaysia (DOSH) present that struck by accident in the Malaysian construction industry have the highest number of deaths and were one of the critical contributor. Therefore, this study was done to find the main contributing factor to the struck by accidents and its preventive measures. The questionnaire was distributed to the respondents, who were experienced safety personnel in construction industry with at least 10 years’ experience in handling safety and health at the workplace. From the questionnaire survey, the most significant contributing factor to struck-by accidents is carelessbehaviour, and the main perceived prevention measure for struck-by accident is job hazard analysis or job safety analysis briefing to workers before starting job. The outcome of the study could provide awareness on struck-by accidents and reduce the risk of struck-by accident by objects in the construction industry in Malaysia.

Vibrotactile Alerting to Prevent Accidents in Highway Construction Work Zones: An Exploratory Study.

Struck-by accidents are the leading cause of injuries in highway construction work zones. Despite numerous safety interventions, injury rates remain high. As workers' exposure to traffic is sometimes unavoidable, providing warnings can be an effective way to prevent imminent threats. Such warnings should consider work zone conditions that can hinder the timely perception of alerts, e.g., poor visibility and high noise level. This study proposes a vibrotactile system integrated into workers' conventional personal protective equipment (PPE), i.e., safety vests. Three experiments were conducted to assess the feasibility of using vibrotactile signals to warn workers in highway environments, the perception and performance of vibrotactile signals at different body locations, and the usability of various warning strategies. The results revealed vibrotactile signals had a 43.6% faster reaction time than audio signals, and the perceived intensity and urgency levels on the sternum, shoulders, and upper back were significantly higher than the waist. Among different notification strategies used, providing a moving direction imposed significantly lower mental workloads and higher usability scores than providing a hazard direction. Further research should be conducted to reveal factors that affect alerting strategy preference towards a customizable system to elicit higher usability among users.

Signal Processing and Alert Logic Evaluation for IoT–Based Work Zone Proximity Safety System

Construction projects are dynamic by nature because of continuously moving resources such as heavy equipment and workers. This nature necessarily results in proximity hazards, especially on a large-scale construction site. Especially, struck-by accidents still account for about 38% of the total injuries in the US construction industry. Although there have been several efforts to mitigate the hazards, the statistics show that the hazards still persist. To provide a practical solution to this problem, this study proposes an Internet of Things (IoT)-based proximity warning system that provides an alert to workers whenever they are close to heavy equipment. The system includes equipment protection units (EPUs), personal protection units (PPUs), and Bluetooth low energy (BLE) beacons. A framework of signal processing and alert logic was developed to estimate the distance between PPUs and EPUs and to activate alerting modules timely. By calculating the distance based on the signal strength using a particle filtering method, EPUs and PPUs provide auditory and vibration alerts to equipment operators and workers when they are in an alert range. Also, practical alert logic was developed based on the site worker’s feedback. This study validates the system performance with different signal processing methods and alert logic through five real-world field tests. The system achieved a precision, recall, and F1-score of 89.21%, 97.45%, and 0.931 from the field tests, respectively. Also, positive feedback was obtained from the participating workers. The proposed IoT-based proximity warning system has a high potential for a practical solution to proximity hazards in actual construction sites.

Excavator 3D pose estimation using deep learning and hybrid datasets

Earthwork operations are crucial parts of most construction projects. Heavy construction equipment and workers are often required to work in limited workspaces simultaneously. Struck-by accidents resulting from poor worker and equipment interactions account for a large proportion of accidents and fatalities on construction sites. The emerging technologies based on computer vision and artificial intelligence offer an opportunity to enhance construction safety through advanced monitoring utilizing site cameras. A crucial pre-requisite to the development of safety monitoring applications is the ability to identify accurately and localize the position of the equipment and its critical components in 3D space. This study proposes a workflow for excavator 3D pose estimation based on deep learning using RGB images. In the proposed workflow, an articulated 3D digital twin of an excavator is used to generate the necessary data for training a 3D pose estimation model. In addition, a method for generating hybrid datasets (simulation and laboratory) for adapting the 3D pose estimation model for various scenarios with different camera parameters is proposed. Evaluations prove the capability of the workflow in estimating the 3D pose of excavators. The study concludes by discussing the limitations and future research opportunities.

Mobile Device-Based Struck-By Hazard Recognition in Construction Using a High-Frequency Sound.

The construction industry experiences the highest rate of casualties from safety-related accidents at construction sites despite continuous social interest in safety management. Accordingly, various studies have been conducted on safety management, wherein recent studies have focused on its integration with Machine Learning (ML). In this study, we proposed a technology for recognizing struck-by hazards between construction equipment and workers, where a Convolutional Neural Network (CNN) and sound recognition were combined to analyze the changes in the Doppler effect caused by the movements of a subject. An experiment was conducted to evaluate the recognition performance in indoor and outdoor environments with respect to movement state, direction, speed, and near-miss situations. The proposed technology was able to classify the movement direction and speed with 84.4–97.4% accuracy and near-misses with 78.9% accuracy. This technology can be implemented using data obtained through the microphone of a smartphone, thus it is highly applicable and is also effective at ensuring that a worker becomes aware of a struck-by hazard near construction equipment. The findings of this study are expected to be applicable for the prevention of struck-by accidents occurring in various forms at construction sites in the vicinity of construction equipment.

Application of Ultrasound Sensor in Construction Safety: Study of Struck-By Hazard

Objectives: To accesses the impact of the sensors in preventing from struck-by hazards in the construction sites; To compare the suitability of available sensor technologies in prevention system of struck-by hazard. Method: The current demand of construction industry is to complete project in the minimum time, to achieve the goal of safety as construction sites are getting complex in terms of management challenge. Available proximity sensors were examined to choose an appropriate sensor for using in early warning system to prevent from struck-by hazards. An early warning system was proposed using ultrasound sensor, that will warn the vehicle operator by visual and acoustical methods to stop the vehicle before collision between vehicle and personal / equipment / material and will prevent from struck-by hazards. Findings: It has been proven that blind spots (visibility problem) are the main reason for struckby accidents. Ultrasound sensors uses sound technology, so that, they are suitable to detect objects in non-visible conditions (Rain, Fog, Night etc.). Ultrasound technology is independent and does not necessitate external hardware installation or processing facilities once it is integrated on the machinery. Proposed early warning system will help construction managers to manage sites more effectively. Novelty: Many researchers have done various studies on struck-by hazard prevention in construction. However, there is no enough focus on, particularly using ultrasound sensors to prevent from struckby hazards. Hence to assess the capability and use of ultrasound sensors in this area of construction is been done Keywords: Construction Safety; Safety Management; Ultrasound Sensor; Struckby Hazard; Prevention System; Early Warning System

Providing proximity alerts to workers on construction sites using Bluetooth Low Energy RTLS

Construction is one of the most dangerous industrial sectors. Struck-by object or equipment is one of the main causes of fatal accidents on construction sites. Although many regulations have been designed for preventing struck-by accidents, they still happen and lead to many injuries and fatalities. The application of real-time location systems (RTLS) on construction sites provides new possibilities in construction safety management. Previous researchers have proposed using RTLS to track the locations of workers and equipment on construction sites to improve construction safety. However, they have the limitation of cabling problems. Also, providing effective safety alerts to workers within dangerous proximity to equipment has not been well addressed. This paper aims to develop a method for detecting the proximities as well as providing proximity safety alerts to workers on construction sites using Bluetooth Low Energy (BLE) RTLS. Here, vibro-tactile alerts representing different cases of proximities were defined. Unnecessary alerts were filtered out to avoid desensitizing workers. A number of tests were conducted on a construction site to test the prototype system developed based on the proposed method. The results indicate that the system is capable of detecting proximities and generating timely vibro-tactile alerts, which can be easily perceived by the workers.

ISafeUAS: An unmanned aerial system for construction safety inspection

Unmanned Aerial Systems (UASs) have recently been utilized as an efficient safety inspection and monitoring tool on construction jobsites. However, their deployment could potentially result in hazardous situations such as worker distraction or struck-by accidents. This paper introduces iSafeUAS as an initial effort to develop and assess a platform designed for safety inspection purposes with customized features such as a super optical zoom RGB Camera and a Parachute Recovery System to ensure safe yet accurate visual data acquisition without the need to fly over or near populated areas. After conducting multiple flight tests to evaluate its technical performance, the iSafeUAS was deployed on a construction jobsite to validate its safety inspection capabilities. The study results showed that the Parachute Recovery System reduced the fatality probability by more than 40%, and the super optical zoom capabilities helped safety managers better identify and inspect potential safety hazards.

Most common accidents and best safety strategies to reduce accidents in Northern Iraq construction projects

Worldwide, the construction sector is one of the riskiest sectors. Although reducing accidents is not easy, but it is possible at the same time. To design solutions to prevent accidents, it is essential to know the most common accidents and also to get the opinion of people with experience. Knowing these things will allow effective strategies to be designed based on the causes and the common accidents compared to the current safety system will result in an effective safety method to be undertaken. This paper identifies the most common accidents in Northern Iraq construction projects and identifies and examines practical ways to reduce accidents that ensure good safety performance. The study has been conducted by applying a questionnaire to collect data from stockholders about how effective ways have shown promising results in reducing accidents. On this basis, it suggests the best strategies to be undertaken. The results of the current study illustrated that the most common accidents in construction projects are falling from heights, struck-by accidents in highway construction and also found effective ways to reduce accidents in construction projects in general and highway projects as well.

Firefighter daytime visibility: trim properties and conspicuity

Conspicuity enhancing trims on turnout gear promote firefighters’ abilities to (1) reduce the possibility of struck-by accidents, (2) be located and recognized by others, and (3) coordinate their work with colleagues. Firefighting personal protective equipment (PPE) ensemble standards specify technical requirements for new visibility materials. A better understanding of the relation between laboratory measures of material properties and behavioral measures of conspicuity can be used to evaluate existing and newly developed turnout gear trim designs. The current paper examined how photometric luminance measures (%Y) of fluorescent trims on firefighter turnout gear relate to daytime conspicuity in two naturalistic field experiments (distance detection and identification, peripheral detection). The test samples consisted of lime yellow and orange-red fluorescent trims, including traditional solid trims and more recently developed segmented trims. We found overall strong relations between measured luminance and (1) observers’ detection of a firefighter from afar, and (2) observers’ identification of the color of a firefighter’s clothing in their peripheral view. A relation did not exist between %Y and peripheral detection. Additionally, the experiments demonstrated that traditional solid and newer segmented trim designs showed little conspicuity performance difference, with the only observed difference being in peripheral color identification. This suggests that the impact of luminance on conspicuity may be overestimated when evaluating a trim’s daytime performance. Our results further support that luminance measures alone are not sufficient to evaluate the conspicuity of turnout gear in naturalistic settings.

Estimating Worker-Centric 3D Spatial Crowdedness for Construction Safety Management Using a Single 2D Camera

AbstractAs a major risk factor that leads to struck-by accidents, crowdedness indicates the number of workers within the range of a targeted worker (this range varies according to the construction ...

Remote proximity monitoring between mobile construction resources using camera-mounted UAVs

Abstract Struck-by accidents have resulted in a significant number of fatal and nonfatal injuries in the construction industry. As a proactive safety measure against struck-by hazards, the authors present an Unmanned Aerial Vehicle (UAV)-assisted visual monitoring method that can automatically measure proximities among construction entities. To attain this end, this research conducts two research thrusts: (i) object localization using a deep neural network, YOLO-V3; and (ii) development of an image rectification method that allows for the measurement of actual distance from a 2D image collected from a UAV. Tests on real-site aerial videos show the promising accuracy of the proposed method; the mean absolute distance errors for estimated proximity were less than 0.9 m and the mean absolute percentage errors were around 4%. The proposed method enables the advanced detection of struck-by hazards around workers, which in turn can make timely intervention possible. This proactive intervention can ultimately promote a safer working environment for construction workers.

Worker safety and injury severity analysis of earthmoving equipment accidents

Introduction: Research on construction worker safety associated with construction equipment has mostly focused on accident type rather than injury severity and the embedded factor relationships. Significant variables and their effects on the degree of injury are examined for earthmoving equipment using data from OSHA. Four types of equipment, backhoe, bulldozer, excavator, and scraper are included in the study. Accidents involving on-foot workers and equipment operators are investigated collectively, as well as separately. Methods: Cross tabulation analysis was conducted to establish the associations between selected categorical variables, using degree of injury as a dichotomous dependent variable (fatal vs. nonfatal) and a number of independent variables having different values. Odds ratios were calculated to determine how much a certain variable/factor increases the odds of fatality in an accident, and the odds ratios were ranked to determine the relative impact of a given factor. Results: It was found that twelve variables were significantly associated with injury severity. Rankings based on odds ratios showed that inadequate safety training (2.54), missing equipment protective system (2.38), being a non-union worker (2.26), being an equipment operator (1.93), and being on or around inadequately maintained equipment (1.58) produced higher odds for fatality. Conclusion: A majority of the earthmoving equipment accidents resulted in fatality. Backhoes were the most common equipment involved in accidents and fatalities. Struck-by accidents were the most prevalent and most fatal. Non-OSHA compliant safety training, missing seatbelt, operator not using seatbelt, malfunctioning back-up alarms, and poorly maintained equipment were factors contributing to accidents and fatalities. On-foot workers experienced a higher number of accidents than operators, while fatality odds were higher for the operators. Practical applications: Safety professionals should benefit from our findings in planning and delivering training and providing oversight to workers in earthmoving equipment operations.

Vision-Based Object-Centric Safety Assessment Using Fuzzy Inference: Monitoring Struck-By Accidents with Moving Objects

AbstractDue to the dynamic environment of construction sites, workers are continuously confronted with the potential for safety accidents. Although various safety guidelines have been developed, workers cannot always be aware of everything that occurs around them when they focus on their work on noisy and congested job sites. Therefore, it is difficult for workers to conform to guidelines to protect themselves when confronting dangerous situations. To address this safety issue, this paper presents an on-site safety-assessment system for monitoring struck-by accidents with moving entities based on computer vision and fuzzy inference. Computer vision is used to monitor a construction site and extract spatial information for each entity (workers and equipment). Next, fuzzy inference is used to assess the proper safety levels of each entity using spatial information. A safety level represents the potential hazard or the integrating danger that a person encounters at a particular moment. Struck-by accidents ar...

Innovative Safety Interventions

NIOSH researchers are studying methods to reduce struckby and caught-in accidents, involving machinery at mining operations. This is a challenging problem-in many circumstances, workers must be near moving machinery to perform their jobs. Innovative safety interventions are needed to further decrease accidents without adversely affecting productivity. Monitoring hazardous areas near machines by using the IVSs may be one possible solution, but limitations must be understood and anticipated during implementation. Although more tests are needed, some general conclusions can be drawn about the possible applications of intelligent video for stationary machine safety.