Topics in construction safety and health : struck-by and caught-in hazards : an interdisciplinary annotated bibliography

"These referenced articles provide literature on the dangers to construction workers from job hazards in their occupations including the equipment they use and the type of work environment they are working in" - NIOSHTIC-2NIOSHTIC no. 20068258Production of this document was supported by cooperative agreement OH 009762 from the National Institute for Occupational Safety and Health (NIOSH). The contents are solely the responsibility of the authors and do not necessarily represent the official views of NIOSH.Struck-by-and-Caught-in-Hazards-annotated-bibliography.pdfcooperative agreement OH 009762 from the National Institute for Occupational Safety and Healt

Digital technologies for enhancing crane safety in construction: a combined quantitative and qualitative analysis

A digital-enabled safety management approach is increasingly crucial for crane operations, which are common yet highly hazardous activities sensitive to environmental dynamics on construction sites. However, there exists a knowledge gap regarding the current status and developmental trajectory of this approach. Therefore, this paper aims to provide a comprehensive overview of digital technologies for enhancing crane safety, drawing insights from articles published between 2008 and 2021. Special emphasis is placed on the sensing devices currently in use for gathering “man-machine-environment” data, as well as the communication networks, data processing algorithms, and intuitive visualization platforms employed. Through qualitative and quantitative analysis of the literature, it is evident that while notable advancements have been made in digital-enabled crane safety management, these achievements remain largely confined to the experimentation stage. Consequently, a framework is proposed in this study to facilitate the practical implementation of digital-enabled crane safety management. Furthermore, recommendations for future research directions are presented. This comprehensive review offers valuable guidance for ensuring safe crane operations in the construction industry

Support vector regression for anomaly detection from measurement histories

Copyright © 2013 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Advanced Engineering Informatics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Advanced Engineering Informatics Vol. 27 (2013), DOI: 10.1016/j.aei.2013.03.002This research focuses on the analysis of measurements from distributed sensing of structures. The premise is that ambient temperature variations, and hence the temperature distribution across the structure, have a strong correlation with structural response and that this relationship could be exploited for anomaly detection. Specifically, this research first investigates whether support vector regression (SVR) models could be trained to capture the relationship between distributed temperature and response measurements and subsequently, if these models could be employed in an approach for anomaly detection. The study develops a methodology to generate SVR models that predict the thermal response of bridges from distributed temperature measurements, and evaluates its performance on measurement histories simulated using numerical models of a bridge girder. The potential use of these SVR models for damage detection is then studied by comparing their strain predictions with measurements collected from simulations of the bridge girder in damaged condition. Results show that SVR models that predict structural response from distributed temperature measurements could form the basis for a reliable anomaly detection methodology

Semantic and spatio-temporal understanding for computer vision driven worker safety inspection and risk analysis

Despite decades of efforts, we are still far from eliminating construction safety risks. Recently, computer vision techniques have been applied for construction safety management on real-world residential and commercial projects; they have shown the potential to fundamentally change safety management practices and safety performance measurement. The most significant breakthroughs of this field have been achieved in the areas of safety practice observations, incident and safety performance forecasting, and vision-based construction risk assessment. However, fundamental theoretical and technical challenges have yet to be addressed in order to achieve the full potential of construction site images and videos for construction safety. This dissertation explores methods for automated semantic and spatio-temporal visual understanding of workers and equipment and how to use them to improve automatic safety inspections and risk analysis: (1) a new method is developed to improve the breadth and depth of vision-based safety compliance checking by explicitly classifying worker-tool interactions. A detection model is trained on a newly constructed image dataset for construction sites, achieving 52.9% mean average precision for 10 object categories and 89.4% average precision for detecting workers. Using this detector and new dataset, the proposed human-object interaction recognition model achieved 79.78% precision and 77.64% recall for hard hat checking; 79.11% precision and 75.29% recall for safety vest checking. The new model also verifies hand protection for workers when tools are being used with 66.2% precision and 64.86% recall. The proposed model is superior to methods relying on hand-made rules to recognize interactions or that reason directly on the outputs of object detectors. (2) to support systems that proactively prevent these accidents, this thesis presents a path prediction model for workers and equipment. The model leverages the extracted video frames to predict upcoming worker and equipment motion trajectories on construction sites. Specifically, the model takes 2D tracks of workers and equipment from visual data -based on computer vision methods for detection and tracking- and uses a Long Short-Term Memory (LSTM) encoder-decoder followed by a Mixture Density Network (MDN) to predict their locations. A multi-head prediction module is introduced to predict locations at different future times. The method is validated on an existing dataset TrajNet and a new dataset of 105 high-definition videos recorded over 30 days from a real-world construction site. On the TrajNet dataset, the proposed model significantly outperforms Social LSTM. On the new dataset, the presented model outperforms conventional time-series models and achieves average localization errors of 7.30, 12.71, and 24.22 pixels for 10, 20, and 40 future steps, respectively. (3) A new construction worker safety analysis method is introduced that evaluates worker-level risk from site photos and videos. This method evaluates worker state, which is based on workers' body pose, their protective equipment use, their interactions with tools and materials, the construction activity being performed, and hazards in the workplace. To estimate worker state, a visual-based Object-Activity-Keypoint (OAK) recognition model is proposed that takes 36.6% less time and 40.1% less memory while keeping comparably performances compared to a system running individual models for each sub-task. Worker activity recognition is further improved with a spatio-temporal graph model using recognized per-frame worker activity, detected bounding boxes of tools and materials, and estimated worker poses. Finally, severity levels are predicted by a trained classifier on a dataset of images of construction workers accompanied with ground truth severity level annotations. In the test dataset, the severity level prediction model achieves 85.7% cross-validation accuracy in a bricklaying task and 86.6% cross-validation accuracy for a plastering task

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Toward Co-Robotic Construction: Visual Site Monitoring & Hazard Detection to Ensure Worker Safety

Construction has remained the least automated and productive as well as the most hazardous industry. Moreover, it has been plagued by a significant lack of diversity in its workforce as well as aging laborers. To address these issues, co-robotic construction has emerged as a new paradigm of construction. The industry is gradually gearing up to embrace robotic solutions, and many construction robots with various degrees of autonomy are under development or in the early stage of deployment. Presenting a different horizon of construction—harmonious co-existence and co-work between workers and robots—co-robotic construction is expected to reform labor-intensive construction into the more productive, safer, and more inclusive industry. However, an in-depth understanding of the robots’ situational intelligence is still lacking, particularly conclusive logic and technologies to ensure workers’ safety nearby autonomous (or semi-) robots, which is fundamental in realizing the co-robotic construction. To fill the gap, this research established a comprehensive robotic hazard detection roadmap and developed core technologies to realize it, leveraging unmanned aerial vehicles, computer vision, and deep learning. In this dissertation, I describe how the developed technologies with a conclusive logic can pro-actively detect the robotics hazards taking various forms and scenarios in an unstructured and dynamic construction environment. The successful implementation of the robotic hazard detection roadmap in co-robotic construction allows for timely interventions such as pro-active robot control and worker feedback, which contributes to reducing robotic accidents. Eventually, this will make human-robot co-existence and collaboration safer, while also helping to build workers’ trust in robot co-workers. Finally, the ensured safety and trust between robots and workers would contribute to promoting construction enterprises to embrace robotic solutions, boosting construction reformation toward innovative co-robotic construction.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167981/1/daeho_1.pd

Real-Time Monitoring and Prediction of Airspace Safety

The U.S. National Airspace System (NAS) has reached an extremely high level of safety in recent years. However, it will only become more difficult to maintain the current level of safety with the forecasted increase in operations, and so the FAA has been making revolutionary changes to the NAS to both expand capacity and ensure safety. Our work complements these efforts by developing a novel model-based framework for real-time monitoring and prediction of the safety of the NAS. Our framework is divided into two parts: (offline) safety analysis and modeling part, and a real-time (online) monitoring and prediction of safety. The goal of the safety analysis task is to identify hazards to flight (distilled from several national databases) and to codify these hazards within our framework such that we can monitor and predict them. From these we define safety metrics that can be monitored and predicted using dynamic models of airspace operations, aircraft, and weather, along with a rigorous, mathematical treatment of uncertainty. We demonstrate our overall approach and highlight the advantages of this approach over the current state-of-the-art through simulated scenarios