scholarly journals Multi-Angle Fusion-Based Safety Status Analysis of Construction Workers

2021 ◽  
Vol 18 (22) ◽  
pp. 11815
Author(s):  
Hui Deng ◽  
Zhibin Ou ◽  
Yichuan Deng
Keyword(s):  
Mixed Model ◽  
Safety Management ◽  
Experimental Studies ◽  
Warning Signal ◽  
Construction Workers ◽  
Support Vector ◽  
Movement Trajectory ◽  
Construction Sites ◽  
Movement Trajectories ◽  
Detection Effect

Hazardous accidents often happen in construction sites and bring fatal consequences, and therefore safety management has been a certain dilemma to construction managers for long time. Although computer vision technology has been used on construction sites to identify construction workers and track their movement trajectories for safety management, the detection effect is often influenced by limited coverage of single cameras and occlusion. A multi-angle fusion method applying SURF feature algorithm is proposed to coalesce the information processed by improved GMM (Gaussian Mixed Model) and HOG + SVM (Histogram of Oriented Gradient and Support Vector Machines), identifying the obscured workers and achieving a better detection effect with larger coverage. Workers are tracked in real-time, with their movement trajectory estimated by utilizing Kalman filters and safety status analyzed to offer a prior warning signal. Experimental studies are conducted for validation of the proposed framework for workers’ detection and trajectories estimation, whose result indicates that the framework is able to detect workers and predict their movement trajectories for safety forewarning.

scholarly journals Safety Helmet Wearing Management System for Construction Workers Using Three-Axis Accelerometer Sensor

2018 ◽  
Author(s):  
SungHun Kim ◽  
Changwon Wang ◽  
Se Dong Min ◽  
Seung-Hyun Lee
Keyword(s):  
Construction Industry ◽  
Management System ◽  
Safety Management ◽  
High Accuracy ◽  
Construction Workers ◽  
Construction Sites ◽  
Accelerometer Sensor ◽  
Sensing Data ◽  
Develop Technology ◽  
Technical Studies

In the Korean construction industry, legal and institutional safety management improvements are continually being pursued. However, there was a 4.5% increase in the number of workers’ deaths at construction sites in 2017 compared to the previous year. Failure to wear safety helmets seems to be one of the major causes of the increase in accidents, and so it is necessary to develop technology to monitor whether or not safety helmets are being used. However, the approaches employed in existing technical studies on this issue have mainly involved the use of chinstrap sensors and have been limited to the problem of whether or not safety helmets are being worn. Meanwhile, improper wearing, such as when the chinstrap and harness fixing of the safety helmet are not properly tightened, has not been monitored. To remedy this shortcoming, a sensing safety helmet with a three-axis accelerometer sensor attached was developed in this study. Experiments were performed in which the sensing data were classified whether the safety helmet was being worn properly, not worn, or worn improperly during construction workers’ activities. The results verified that it is possible to differentiate among wearing status of the proposed safety helmet with a high accuracy of 97.0%

scholarly journals Factors Causing Health and Safety Hazards at Construction Sites

2020 ◽  
Vol 2 (1) ◽  
pp. 68-74
Author(s):  
Om Prakash Giri
Keyword(s):  
Risk Factors ◽  
Safety Management ◽  
Health And Safety ◽  
Construction Workers ◽  
Construction Sites ◽  
Safety Hazards ◽  
Safety Awareness ◽  
Awareness Programs ◽  
Health And Safety Hazards ◽  
Improve Health

 The Construction industry is prone to risk to health and safety hazards. Construction workers should have the knowledge of health and safety and apply the knowledge while working. The main objective of this paper was to review and to identify the factors that cause accidents at construction sites and the methods to improve health and safety. The review revealed that lack of awareness about site safety and negligence of workers in wearing Personal Protective Equipment (PPE) were the main causes of poor safety practices. It is necessary to create employer’s and contractor’s interests in safety management and enhance awareness on possible risk factors to reduce these risk factors among workers. Effective implementation of training and safety awareness programs among construction workers is vital to improve health and safety of construction workers.

scholarly journals Spatio-temporal analysis of trajectories for safer construction sites

2018 ◽  
Vol 7 (1) ◽  
pp. 80-100 ◽  
Author(s):  
Muhammad Arslan ◽  
Christophe Cruz ◽  
Ana-Maria Roxin ◽  
Dominique Ginhac
Keyword(s):  
Safety Management ◽  
Construction Safety ◽  
Construction Workers ◽  
Gps Data ◽  
Construction Site ◽  
Trajectory Data ◽  
Construction Sites ◽  
Content Type ◽  
Spatio Temporal ◽  
Construction Safety Management

Purpose The purpose of this paper is to improve the safety of construction workers by understanding their behaviors on construction sites using spatio-temporal (ST) trajectories. Design/methodology/approach A review of construction safety management literature and international occupational health and safety statistics shows that the major reasons for fatalities on construction sites are mobility-related issues, such as unsafe human behaviors, difficult site conditions, and workers falling from heights and striking against or being struck by moving objects. Consequently, literature has been reviewed to find possible technological solutions to track the mobility of construction workers to reduce fatalities. This examination has suggested that location acquisition systems, such as Global Positioning System (GPS), have been widely used for real-time monitoring and tracking of workers on construction sites for hazard prevention. However, the raw data captured from GPS devices are generally available as discrete points and do not hold enough information to understand the workers’ mobility. As a solution, an application to transform raw GPS data into ST trajectories using different preprocessing algorithms is proposed for enhancing worker safety on construction sites. Findings The proposed system preprocesses raw GPS data for stay point detection, trajectory segmentation and intersection of multiple trajectories to find significant places and movements of workers on a construction site to enhance the information available to H&S managers for decision-making processes. In addition, it reduces the size of trajectory data for future analyses. Originality/value Application of location acquisition systems for construction safety management is very well addressed in the existing literature. However, a significant gap has been found: the usage of preprocessed ST trajectories is still missing in workers’ safety monitoring scenarios in the area of construction management. To address this research gap, the proposed system uses preprocessed ST trajectories to monitor workers’ movements on a construction site to identify potentially unsafe behaviors.

Evaluating Construction Workers’ Understanding of Safety Signs

2013 ◽  
Vol 291-294 ◽  
pp. 3024-3027 ◽  
Author(s):  
Gwang Hee Kim ◽  
Seok Hoon Nam ◽  
Sang Jun Hwang ◽  
Hee Bok Choi ◽  
Yoon Seok Shin
Keyword(s):  
Safety Management ◽  
Construction Safety ◽  
Construction Workers ◽  
Construction Sites ◽  
Critical Part ◽  
Safety Accident

It has been suggested that many construction workers are never properly educated on the meaning of safety signs. In particular, considering that any safety accident can lead directly to a serious disaster, understanding safety signs is a critical part of safety management. Therefore, the purpose of this study is to analyze construction workers’ awareness of and understanding of safety signs. The research found that most construction workers perceived that safety signs play an important role in preventing safety accidents. However, almost half of construction workers did not understand what construction safety signs convey, which indicates an urgent need for education on safety signs at construction sites.

scholarly journals Safety Helmet Wearing Management System for Construction Workers Using Three-Axis Accelerometer Sensor

2018 ◽  
Vol 8 (12) ◽  
pp. 2400 ◽  
Author(s):  
Sung Hun Kim ◽  
Changwon Wang ◽  
Se Dong Min ◽  
Seung Hyun Lee
Keyword(s):  
Construction Industry ◽  
Management System ◽  
Safety Management ◽  
High Accuracy ◽  
Construction Workers ◽  
Construction Sites ◽  
Accelerometer Sensor ◽  
Sensing Data ◽  
Develop Technology ◽  
Technical Studies

In the Korean construction industry, legal and institutional safety management improvements are continually being pursued. However, there was a 4.5% increase in the number of workers’ deaths at construction sites in 2017 compared to the previous year. Failure to wear safety helmets seems to be one of the major causes of the increase in accidents, and so it is necessary to develop technology to monitor whether or not safety helmets are being used. However, the approaches employed in existing technical studies on this issue have mainly involved the use of chinstrap sensors and have been limited to the problem of whether or not safety helmets are being worn. Meanwhile, improper wearing, such as when the chinstrap and harness fixing of the safety helmet are not properly tightened, has not been monitored. To remedy this shortcoming, a sensing safety helmet with a three-axis accelerometer sensor attached was developed in this study. Experiments were performed in which the sensing data were classified whether the safety helmet was being worn properly, not worn, or worn improperly during construction workers’ activities. The results verified that it is possible to differentiate among wearing status of the proposed safety helmet with a high accuracy of 97.0%.

scholarly journals Applications of Computer Vision in Monitoring the Unsafe Behavior of Construction Workers: Current Status and Challenges

Buildings ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 409
Author(s):  
Wenyao Liu ◽  
Qingfeng Meng ◽  
Zhen Li ◽  
Xin Hu
Keyword(s):  
Computer Vision ◽  
Safety Management ◽  
Relevant Literature ◽  
Construction Workers ◽  
Current Status ◽  
Future Research ◽  
Analysis Method ◽  
Construction Sites ◽  
Video Information ◽  
Unsafe Behavior

The unsafe behavior of construction workers is one of the main causes of safety accidents at construction sites. To reduce the incidence of construction accidents and improve the safety performance of construction projects, there is a need to identify risky factors by monitoring the behavior of construction workers. Computer vision (CV) technology, which is a powerful and automated tool used for extracting images and video information from construction sites, has been recognized and adopted as an effective construction site monitoring technology for the identification of risky factors resulting from the unsafe behavior of construction workers. In this article, we introduce the research background of this field and conduct a systematic statistical analysis of the relevant literature in this field through the bibliometric analysis method. Thereafter, we adopt a content-based analysis method to depict the historical explorations in the field. On this basis, the limitations and challenges in this field are identified, and future research directions are proposed. It is found that CV technology can effectively monitor the unsafe behaviors of construction workers. The research findings can enhance people’s understanding of construction safety management.

Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks

2019 ◽  
Vol 19 (4) ◽  
pp. 232-241 ◽  
Author(s):  
Xuegong Chen ◽  
Wanwan Shi ◽  
Lei Deng
Keyword(s):  
Protein Interactions ◽  
Experimental Studies ◽  
Treatment Strategies ◽  
Computational Method ◽  
Biological Information ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Efficient Treatment ◽  
Disease Associations ◽  
Previous State

Background: Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic. Objective: In this work, we propose PCHS, an effective computational method for predicting disease comorbidity. Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores. Results and Conclusion: The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method.

scholarly journals Risk Perception in the Construction Industry: Differences Between Italian and Migrant Workers Before and After a Targeted Training Intervention

2021 ◽  
Vol 31 (1) ◽  
pp. 65-71
Author(s):  
Federico Ricci ◽  
Giulia Bravo ◽  
Alberto Modenese ◽  
Fabrizio De Pasquale ◽  
Davide Ferrari ◽  
...  
Keyword(s):  
Risk Perception ◽  
Construction Industry ◽  
Migrant Workers ◽  
Construction Workers ◽  
Construction Sites ◽  
Visual Tool ◽  
Occupational Risks ◽  
Linguistic Barriers ◽  
Before And After ◽  
The Difference

We developed a visual tool to assess risk perception for a sample of male construction workers (forty Italian and twenty-eight immigrant workers), just before and after a sixteen-hour training course. The questionnaire included photographs of real construction sites, and workers were instructed to select pictograms representing the occupational risks present in each photograph. Points were awarded for correctly identifying any risks that were present, and points were deducted for failing to identify risks that were present or identifying risks that were not present. We found: (1) Before the course, risk perception was significantly lower in immigrants compared to Italians ( p < .001); (2) risk perception improved significantly ( p < .001) among all workers tested; and (3) after the training, the difference in risk perception between Italians and immigrants was no longer statistically significant ( p = .1086). Although the sample size was relatively small, the results suggest that the training is effective and may reduce the degree to which cultural and linguistic barriers hinder risk perception. Moreover, the use of images and pictograms instead of words to evaluate risk perception could also be applied to nonconstruction workplaces.

scholarly journals Exploring the Relationship between Safety Climate and Worker Safety Behavior on Building Construction Sites in Taiwan

2021 ◽  
Vol 13 (6) ◽  
pp. 3326
Author(s):  
Wei Tong Chen ◽  
Hew Cameron Merrett ◽  
Ying-Hua Huang ◽  
Theresia Avila Bria ◽  
Ying-Hsiu Lin
Keyword(s):  
Safety Climate ◽  
Organizational Factors ◽  
Safety Training ◽  
Building Construction ◽  
Construction Workers ◽  
Occupational Accidents ◽  
Successful Implementation ◽  
Construction Sites ◽  
Safety Behavior ◽  
The Relationship

Construction occupational accidents are often attributed to workers’ having an insufficient perception of how their actions influence safety in the construction site. This research explores the relationship between safety climate (SC) and personnel safety behavior (SB) of construction workers operating on building construction sites in Taiwan. The study discovered a significant positive relationship between SC and SB of Taiwan’s building construction sites, and in turn SC level had a positive impact on SB participation and overall safety perceptions. The higher the SC cognition of Taiwan’s building construction workers, the better the performance of SB was found to be. The dimension of "safety commitment and safety training" had the greatest relationship with SB. Safety training also had a deep impact on the cognition of SB. Therefore, the organizational culture and attitudes to safety coupled with the successful implementation of safety education and training can effectively enhance SC and worker SB on building construction sites in Taiwan, thereby potentially reducing the impacts of the underlying organizational factors behind safety related incidents.

scholarly journals Development of a Safety Management System Tracking the Weight of Heavy Objects Carried by Construction Workers Using FSR Sensors

2021 ◽  
Vol 11 (4) ◽  
pp. 1378
Author(s):  
Seung Hyun Lee ◽  
Jaeho Son
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Safety Management ◽  
Tracking System ◽  
Construction Workers ◽  
Gradient Boosting ◽  
Prototype System ◽  
Construction Site ◽  
Average Accuracy ◽  
Smart Safety

It has been pointed out that the act of carrying a heavy object that exceeds a certain weight by a worker at a construction site is a major factor that puts physical burden on the worker’s musculoskeletal system. However, due to the nature of the construction site, where there are a large number of workers simultaneously working in an irregular space, it is difficult to figure out the weight of the object carried by the worker in real time or keep track of the worker who carries the excess weight. This paper proposes a prototype system to track the weight of heavy objects carried by construction workers by developing smart safety shoes with FSR (Force Sensitive Resistor) sensors. The system consists of smart safety shoes with sensors attached, a mobile device for collecting initial sensing data, and a web-based server computer for storing, preprocessing and analyzing such data. The effectiveness and accuracy of the weight tracking system was verified through the experiments where a weight was lifted by each experimenter from +0 kg to +20 kg in 5 kg increments. The results of the experiment were analyzed by a newly developed machine learning based model, which adopts effective classification algorithms such as decision tree, random forest, gradient boosting algorithm (GBM), and light GBM. The average accuracy classifying the weight by each classification algorithm showed similar, but high accuracy in the following order: random forest (90.9%), light GBM (90.5%), decision tree (90.3%), and GBM (89%). Overall, the proposed weight tracking system has a significant 90.2% average accuracy in classifying how much weight each experimenter carries.

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