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A proof of concept application of sensing technologies for managing proximity hazards on construction sites
... The results also indicated that certain researchers have endeavored to employ DT in predicting hazards. The achieved functions include collision detection (Eiffert et al., 2020), audio-based collision detection (Elelu et al., 2023), automated close call (Mastrolembo Ventura et al., 2021), intent prediction , task learning , estimation and visualization of tower crane hazard exposure (Hu et al., 2023), as well as estimation of the fall zone directly beneath the crane load (Chian et al., 2022). Hazard identification and anticipation accomplished at this level demonstrates the potential to enhance individuals' projection abilities. ...
The dynamics of construction processes pose significant challenges for workers to perceive, understand, and anticipate hazards on sites. Maintaining an adequate level of situation awareness (SA) is critical for safety. Recent years have seen increasing research interests in SA and digital technology (DT) in the construction safety field. However, there lacks a systematic review that elucidates the impacts of DT on workers' cognitive processes involved in hazard identification. To fill the research gap, the paper aims to conceptualize the role of SA and DT in construction safety management and provide a theoretical basis for the future development of technology-enabled situation-aware construction safety management systems. This study employed a five-step systematic review approach with a scientometric analysis to provide an unbiased and comprehensive overview of research concerning SA in construction safety. The results suggested that current research on the cognitive processes related to hazard identification has been fragmented and has caused theoretical confusion. There is a need to develop a cognitive framework for hazard identification. Four specific research directions were recommended: (1) develop and validate a real-time SA measurement, (2) identify SA information requirements, (3) develop a cognitive model of hazard identification, and (4) develop a technology-enhanced SA system. By synthesizing the current literature, this review paper identified and analyzed the theoretical roles of SA and DT in worker hazard identification, shedding light on the potential for developing a technology-enabled situation-aware construction safety management system. It also offered a valuable perspective to review and redesign safety management practices.
Accident prevention on the manufacturing shop floor is essential. This paper aims to develop a proximity warning system capable of reducing accidents on the manufacturing shop floor, by notifying employers of any possible danger within the workplace. The system consists of an ultrasonic sensor, Arduino microcontroller, alarm system and an easy to understand user interface that can detect and alert workers within the danger zone. The system was developed using the Unified Modelling Language, python programming language and the Arduino software. Preliminary test conducted on volunteer participants revealed that the system is easy to use and provides real-time easy-to-understand audio and visual feedback to workers. The system is useful for reducing accidents on shop floors.
In the Industry 4.0 the communication infrastructure is derived from the Internet of Things (IoT), and it is called Industrial IoT or IIoT. Smart objects deployed on the field collect a large amount of data which is stored and processed in the Cloud to create innovative services. However, differently from most of the consumer applications, the industrial scenario is generally constrained by time-related requirements and its needs for real-time behavior (i.e., bounded and possibly short delays). Unfortunately, timeliness is generally ignored by traditional service provider, and the Cloud is treated as a black box. For instance, Cloud databases (generally seen as "Database as a service"-DBaaS) have unknown or hard-to-compare impact on applications. The novelty of this work is to provide an experimental measurement methodology based on an abstract view of IIoT applications, in order to define some easy-to-evaluate metrics focused on DBaaS latency (no matter the actual implementation details are). In particular, the focus is on the impact of DBaaS on the overall communication delays in a typical IIoT scalable context (i.e., from the field to the Cloud and the way back). In order to show the effectiveness of the proposed approach, a real use case is discussed (it is a predictive maintenance application with a Siemens S7 industrial controller transmitting system health status information to a Cloudant DB inside the IBM Bluemix platform). Experiments carried on in this use case provide useful insights about the DBaaS performance: evaluation of delays, effects of involved number of devices (scalability and complexity), constraints of the architecture, and clear information for comparing with other implementations and for optimizing configuration. In other words, the proposed evaluation strategy helps in finding out the peculiarities of Cloud Database service implementations.
A smart glasses-based wearable personnel proximity warning system (PWS) was developed for pedestrian safety in construction and mining sites. The smart glasses receive signals transmitted by Bluetooth beacons attached to heavy equipment or vehicles, with the proximity determined by the signal strength. A visual alert is displayed to the wearer when in close proximity. The media access control address of the Bluetooth beacon provides information on the approaching equipment or vehicle, which is displayed to the wearer so that they can respond appropriately. There was a detection distance of at least 10 m regardless of the direction the pedestrian was looking and the alert was successful in all 40 trials at ≥10 meters. The subjective workload was evaluated using the NASA task load index on ten subjects, either without a personal PWS, with a smartphone-based PWS, or with the smart glasses-based PWS. The mental, temporal, and physical stresses were lowest when using the smart glasses-based PWS. Smart glasses-based PWSs can improve work efficiency by freeing both hands of the pedestrians, and various functions can be supported through application development. Therefore, they are particularly useful for pedestrian safety in construction and mining sites.
Proximity warning systems for construction sites do not consider whether workers are already aware of the hazard prior to issuing warnings. This can generate redundant and distracting alarms that interfere with worker ability to adopt timely and appropriate avoidance measures; and cause alarm fatigue, which instigates workers to habitually disable the system or ignore the alarms; thereby increasing the risk of injury. Thus, this paper integrates the field-of-view of workers as a proxy for hazard awareness to develop an improved hazard proximity warning system for construction sites. The research first developed a rule-based model for the warning generation, which was followed by a virtual experiment to evaluate the integration of worker field-of-view in alarm generation. Based on these findings, an improved hazard proximity warning system incorporating worker field-of-view was developed for field applications that utilizes wearable inertial measurement units and localization sensors. The system’s effectiveness is illustrated through several case studies. This research provides a fresh perspective to the growing adoption of wearable sensors by incorporating the awareness of workers into the generation of hazard alarms. The proposed system is anticipated to reduce unnecessary and distracting alarms which can potentially lead to superior safety performance in construction.
Time difference of arrival (TDoA) localization with ultra-wideband (UWB) radios is rapidly gaining interest. Nevertheless , TDoA requires tightly time-synchronized anchors, either via wireless synchronization in small-scale setups with all anchors in range or via expensive wired backbones, both ultimately hampering the use of TDoA in large-scale areas.
In this paper we present TALLA, a novel wireless-only TDoA approach able to scale over large operational areas without sacrificing positioning accuracy. TALLA relies on a TDMA schedule enabling continuous, multi-hop operation of the anchor infrastructure. We evaluate TALLA in our 12-node UWB testbed and in much larger (>100 anchors) simulated areas, empowered by a technique that generates synthetic timing information faithfully reproducing the trends of the real one. Our real and simulated results show that TALLA achieves decimeter-level accuracy while tracking a moving target across several hops.
The Internet of Things (IoT) paradigm contaminated the industrial world. Wireless communications seem to be particularly attracting, especially when complement indoor and outdoor Real Time Location Systems (RTLS) for geo-referencing smart objects (e.g. for asset tracking). In this paper, the LoRaWAN solution is considered for long range transmission of RTLS data (LoRaWAN is an example of Low Power Wide Area Network). Given that the RTLSs use time synchronization, this work proposes to opportunistically obtain LoRaWAN Class A node time synchronization using the RTLS ranging devices. Once a common sense of time is shared in the LoRaWAN network, more efficient scheduled medium access strategies can be implemented. The experimental testbed, based on commercially available solutions, demonstrates the affordability and feasibility of the proposed approach. When low-cost GPS (outdoor) and UWB (indoor) ranging devices are considered, synchronization error of few microseconds can be easily obtained. The experimental results show the that time reference pulses disciplined by GPS have a maximum jitter of 180 ns and a standard deviation of 40 ns whereas, if time reference pulses disciplined by UWB are considered, the maximum jitter is 3.3 μs and the standard deviation is 0.7 μs.
Location based services (LBS) have emerged as an active area of research aimed to filter and organize the information present on the internet. This work deals with LBS applied to industrial Internet of Things (IoT) applications , ranging from distributed measurement systems (DMS) to distributed human-machine interface (HMI). The goal is to investigate the available techniques to determine the location of assets (e.g., sensors and actuators) and then to characterize some application scenarios. In detail, the use cases have been implemented exploiting the eLUX Living Laboratory in Brescia, Italy, which resembles a smart grid, smart living and Industry 4.0 real scenario thanks to the numerous installed plants (including renewable sources, indoor sensory systems and electric vehicles). The ranging performance of beacon-based and ultrawide band-based localization principles have been experimentally assessed.
Poor hazard recognition is a widespread issue in the construction industry. When construction hazards remain unrecognized, workers are more likely to indulge in unsafe behavior, experience unanticipated hazard exposure, and suffer catastrophic injuries. To improve our understanding of why construction hazards remain unrecognized, the current study examined hazard recognition as an everyday visual search task – similar to an individual searching for a product in a supermarket, a radiologist examining a radiograph for tissue abnormalities, or a security personnel screening baggage at an airport terminal. More specifically, the research used eye-tracking technology to examine the relationship between visual search patterns adopted by workers while participating in a hazard recognition activity and the resulting performance levels (i.e., hazard recognition performance). The research also focused on testing the effects of introducing a recently developed personalized training intervention on visual search patterns adopted by workers – and the subsequent hazard recognition performance. Visual search patterns examined in the study included search duration, fixation count, fixation spatial density and others identified from past research. The results reveal that several quantifiable visual search patterns are predictive of superior hazard recognition performance. For example, workers who spent more time examining the workplace for safety hazards (i.e., search duration) recognized a larger proportion of hazards. Likewise, workers that devoted higher levels of attention through a larger number of fixations (i.e., fixation count) and longer fixation durations (i.e., fixation time), and those that distributed their visual attention more broadly across the work area (i.e., fixation spatial density) demonstrated superior performance. The findings also suggest that the adoption of the personalized intervention can lead to improvements in visual search patterns and hazard recognition performance among workers. The present research will be useful to diagnose and remedy search weaknesses demonstrated by workers (i.e., human factors) that are associated with poor hazard recognition levels.
Construction sites need to be monitored continuously to detect unsafe conditions and protect workers from potential injuries and fatal accidents. In current practices, construction safety monitoring relies heavily on manual observation, which is labor-intensive and error-prone. Due to the complex environment of construction sites, it is extremely challenging for safety inspectors to continuously monitor and manually identify all incidents that may expose workers to safety risks. There exist many research efforts applying sensing technologies to construction sites to reduce the manual efforts associated with construction safety monitoring. However, several bottlenecks are identified in applying these technologies to the onsite safety monitoring process, including (1) recognition and registration of potential hazards, (2) real-time detection of unsafe incidents, and (3) reporting and sharing of the detected incidents with relevant participants in a timely manner. The objective of this study was to create and evaluate a low-cost automated safety monitoring system to assist in the construction safety monitoring process. This paper presents a framework for this safety monitoring system as a cloud-based real time on-site application. The system integrates Bluetooth Low Energy (BLE)-based location detection technology, Building Information Model (BIM)-based hazard identification, and a cloud-based communication platform. Potential unsafe areas are defined automatically or manually in a BIM Model. Real-time worker locations are acquired to detect incidents where workers are exposed to predefined risks. Then, the safety monitoring results are instantly communicated over a cloud for effective safety management. Through a real-world construction site case study, the system successfully demonstrated the capability to detect unsafe conditions and collect and analyze the trajectories of workers with respect to potential safety hazards. The results indicate that the proposed approach can assist in the construction site monitoring process and potentially improve site safety.
Workspace interferences and collisions occurring on construction jobsites are products of unexpected spatiotemporal overlaps of resources. They affect the project performance and can create an unsafe environment. Current practices for workspace planning adopt a linear/mechanistic approach in modeling, which ignores uncertainties occurring during construction operations. Those methods do not account for the emergent behavior of crews, irreversibility of decisions made in the case of interferences, and bifurcation of those decisions over the project lifecycle. This paper presents the results of qualitative and quantitative analysis of the significance of such gaps. A software tool was developed to model workspaces probabilistically and detects interferences from 4D models. The impact of uncertainties on interference magnitudes was evaluated based on available models, and an industry survey was conducted accordingly. The results suggest that compounding effects of uncertainties may significantly affect the frequency and volume of overlapped workspaces in a project (volumes can increase for as much as 220%, for a 40% deviation in the inputs). Our findings also propose crew attributes for formulating a bottom-up approach, capable of capturing crews' interactions and predicting their decision when managing collisions.
The construction process is considered a very risky endeavor because of the high frequency of work-related
injuries and fatalities. The collection and analysis of safety data is an important element in measurement and
improvement strategy development. The adoption of wearable technology has the potential for a result-oriented data collection and analysis approach to providing real-time information to construction personnel. The objective of this paper is to provide a comprehensive review of the applications of wearable technology for personalized construction safety monitoring. The characteristics of wearable devices and safety metrics thought to
be capable of predicting safety performance and management practices are identified and analyzed. The review
indicates that the existing wearable technologies applied in other industrial sectors can be used to monitor and measure a wide variety of safety performance metrics within the construction industry. Benefits of individual wearable sensors or systems can be integrated based on their attributes for multi-parameter monitoring of safety performance.
Over six hundred construction worker deaths occurred in the United States during the inclusive years of 2004 to 2006 that were related to construction equipment and contact collisions. This paper presents findings about emerging radio frequency (RF) remote sensing and actuating technology that can improve construction safety by warning or alerting workers-on-foot and equipment operators in a pro-active real-time mode once equipment gets too close in proximity to unknown or other equipment. A review is provided on the background and importance of safety related to various pieces of construction equipment. Pro-active real-time proximity and alert technology for daily construction operations is introduced to solve this problem. Results of various field experiments that tested the proximity and alert technology are presented. A discussion follows on how such technology can improve objective construction site safety data collection and lead to more effective construction workforce safety training and education.
This paper uses the findings from two workshops conducted with 77 employees of an underground mining operation in Western Australia in April and May 2011. Risk management requires all managers and employees to identify hazards in their work environments. Managers assume that their employees have sufficient knowledge and skills to successfully identify not only obvious but also emerging hazards. For this study, two workshops were conducted using an action research methodology. In the first workshop, “Hazard Identification” it was found that the range of workplace hazards the staff could identify was extensive by some groups and very limited by others. For example length of experience underground did not predetermine an ability to identify hazards. Some of the longest serving and those in supervisory positions identified few hazards. Most teams identified 8-12 hazards under each of four categories within a typology: obvious, trivial, emerging and hidden hazards. However, the team with the least experience were unable to identify more than four obvious, two trivial, five emerging and three hidden hazards in their work areas. In workshop two, “Managing Workplace Hazards”, the teams showed a range of abilities to complete the task with one team (with an average 12 years experience underground) unable to identify any strategies to control the list of emerging hazards and one team of managers displaying limited skills. Given these results there is a need to provide further training for all managers and employees in hazard identification and management.
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