Hazardous Proximity Zone Design for Heavy Construction Excavation Equipment

Xu, Shen; Marks, Eric; Pradhananga, Nipesh; Cheng, Tao

doi:10.1061/(asce)co.1943-7862.0001108

Cited by 23 publications

(14 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vahdatikhaki suggested a risk map generation considering the visibility and proximity-based hazards using pose information and Near-Real-Time Simulation [35]. Shen designed a hazard zone around construction equipment in which site personnel should not be during operations [36]. Gonon applied the potential field method in order to avoid collisions when controlling the excavator [37].…”

Section: Literature Reviewmentioning

confidence: 99%

Creation of One Excavator as an Obstacle in C-Space for Collision Avoidance during Remote Control of the Two Excavators Using Pose Sensors

et al. 2020

View full text Add to dashboard Cite

Many solutions about the teleoperation of unmanned robotic excavators have been studied continuously. However, if excavators that are remotely controlled were employed, the probability of an accident would be higher. For this reason, there have been many ways proposed to attempt to reduce accidents in these dangerous situations. In this paper, a novel methodology will be introduced with a focus on the multiple excavators themselves. The proposed method details how one excavator can be generated or expressed as an obstacle in the Configuration-Space (C-space) with respect to another excavator’s side for collision avoidance each other. This method is based on kinematics information which is measurable and given. Therefore, this method can be used or applied independently by using widely used pose sensors and wireless communication devices. The phase of mathematical derivation about obstacle sets is described in detail. The results are shown in accompanying figures and tables for demonstrating that the proposed method detected the proximity and found the collision-free zone accurately. The proposed system can be powerful when applied to a teleoperation system, where it would be particularly useful and helpful to the operator.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Creation of One Excavator as an Obstacle in C-Space for Collision Avoidance during Remote Control of the Two Excavators Using Pose Sensors

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Adequate safety knowledge is a prerequisite to making the 'right' safety decisions, judgements, performance, and behavior [3]. Knowledge is often shared via different platforms [4], the most important being Web 2.0, IoT, and mobile apps. Web 2.0 enables users to write and change web content, share knowledge and socialize with others online [5] via online social networks such as Facebook, LinkedIn, YouTube, wikis, fora and folksonomies [6].…”

Section: Introductionmentioning

confidence: 99%

Sustainable Construction Safety Knowledge Sharing: A Partial Least Square-Structural Equation Modeling and A Feedforward Neural Network Approach

Tang

Chau

2019

Sustainability

View full text Add to dashboard Cite

Most studies focused on the introduction of new technologies have not investigated the psychological factors affecting the willingness to use them or conducted empirical studies to explore whether willingness and actual construction safety knowledge-sharing behavior are associated with fewer construction incidents. We conducted face-to-face and LinkedIn open-ended interviews as well as a global survey to study the willingness and actual behavior to share construction knowledge via social software Web 2.0, Internet of Things (IoT) and mobile apps. Then, the Partial Least Square-Structural Equation Model (PLS-SEM) for willingness and actual knowledge-sharing behavior, as well as the Multilayer Perceptron (MLP) Neural Network were used to illustrate the effect of various factors on predicting the willingness to share knowledge via Web 2.0, mobile apps and IoT. Results of the interviews found that practitioners use IoT for knowledge sharing, mainly because they do not want to fall behind the curve. PLS-SEM and MLP revealed that practitioners share construction safety knowledge are not driven by safety-related reasons such as safety awareness enhancement but perceived organization support from their companies. Employees who agree that their organization cared about their employees’ well-being was the strongest predictor in influencing people’s decision to use tools for knowledge sharing. Moreover, many respondents claimed that factors such as monetary rewards have little impact on motivating people to use tools for knowledge sharing.

show abstract

“…A framework integrated with six major steps for creating the hazard zones around construction equipment was presented in the work [9]. A user interface was also developed to generate the hazard zone automatically for a specific piece of equipment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Moreover, generally the hazardous distance or zone (i.e., the threshold) around equipment is defined in 2D space [4,9]. Also in some studies, the thresholds were defined as constant values without updating over time [4].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Due to the multiple uncertainties and dynamics on jobsites, it is difficult to calculate the exact warning distance needed by the entities to avoid collisions when they come to a complete stop. The existing methods were endeavoring to quantify it as close to the needed distance as possible by taking more factors into account [9,14].…”

Section: Quantification Of Warning Distancementioning

confidence: 99%

See 1 more Smart Citation

An Integrated INS-GPS-Raspberry Pi System Using the Time-Sphere Model for Real-Time Identification of Struck-by-Equipment Hazard

Wang¹,

Du²,

Razavi³

2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

View full text Add to dashboard Cite

Struck-by-equipment hazard, i.e, contact collision between worker-on-foot and construction equipment or between equipment and equipment, is a major cause of injuries and fatalities in construction. A variety of smart technologies and systems have been developed and utilized to alleviate the risks of this type of hazard. However, most of the existing technologies and systems perform with a high rate of false alarms which have critically impeded their implementations on real construction sites. Therefore, a system performing more accurately and in real-time needs to be developed to enhance construction safety. In this context, a real-time system integrating with the 4D Time-Sphere model is developed to timely identify and alarm struck-byequipment hazards with reduced false alarms. This system is comprised of Global Positioning System (GPS), Inertial Navigation System (INS), Raspberry Pi micro-processor and the developed Time-Sphere model. Corresponding actuation will be triggered if an unsafe situation is identified, i.e., struck-byequipment hazard(s) are or to be presented. A controlled field experiment was conducted to evaluate the feasibility of the integrated system and the effectiveness of the Time-Sphere model in reducing false alarms. The obtained results show that average 53% of the alarms generated by the prevalent method can be reduced by the TimeSphere model. All of the reduced alarms are false positives. The average false positive rate of the prevalent method is 29%. Moreover, it is demonstrated that the developed system is feasible and effective in identifying struck-by-equipment hazards in real time and potentially enhancing construction safety.

show abstract

Hazardous Proximity Zone Design for Heavy Construction Excavation Equipment

Cited by 23 publications

References 13 publications

Creation of One Excavator as an Obstacle in C-Space for Collision Avoidance during Remote Control of the Two Excavators Using Pose Sensors

Creation of One Excavator as an Obstacle in C-Space for Collision Avoidance during Remote Control of the Two Excavators Using Pose Sensors

Sustainable Construction Safety Knowledge Sharing: A Partial Least Square-Structural Equation Modeling and A Feedforward Neural Network Approach

An Integrated INS-GPS-Raspberry Pi System Using the Time-Sphere Model for Real-Time Identification of Struck-by-Equipment Hazard

Contact Info

Product

Resources

About