A Review of Data-Driven Accident Prevention Systems: Integrating Real-Time Safety Management in the Civil Infrastructure Context

2019 ◽  
Author(s):  
Amin Assadzadeh ◽  
Mehrdad Arashpour ◽  
Ali Rashidi ◽  
Alireza Bab-Hadiashar ◽  
Sajad Fayezi
Keyword(s):  
Real Time ◽  
Safety Management ◽  
Accident Prevention ◽  
Data Driven ◽  
Civil Infrastructure

scholarly journals Overview and Analysis of Digital Technologies Designed for Construction Safety Management

10.29007/zvfp ◽  
2018 ◽  
Author(s):  
Brian H.W. Guo ◽  
Eric Scheepbouwer ◽  
Tak Wing Yiu ◽  
Vicente González
Keyword(s):  
Real Time ◽  
Construction Industry ◽  
Safety Management ◽  
Digital Technologies ◽  
Construction Safety ◽  
Accident Prevention ◽  
Geographical Information ◽  
Research Trend ◽  
Physiological Status ◽  
Case Based Reasoning

Digital technologies are increasingly used to support safety management in the construction industry. Previous efforts were made to identify digital technologies for safety in the construction industry. However, limited research has been done to conceptualize the roles played by digital technologies in safety management and accident prevention. This paper surveys state-of-the-art research between 2000 and 2016 in order to categorize digital technologies for construction safety, identify research trend, and analyse their roles in accident prevention. The research employs a systematic process to review the existing literature on digital technologies in the area of construction safety. Five academic databases, Science Direct, Taylor & Francis, the ASCE Library, Engineering village, and Web of Science, were selected for the survey due to the comprehensive coverage of relevant academic papers. The survey identified 15 digital technologies: real-time location system and proximity warning, building information modelling, augmented reality, virtual reality, game technology, e-safety-management-system, case-based reasoning, rule-based reasoning, motion sensor, action/object recognition, laser scanning, physiological status monitoring, virtual prototyping, geographical information system, and ubiquitous sensor network. Three emerging safety functions claimed and/or promoted by DTs were discussed: enhanced safety planning, real-time hazard management, and safety knowledge engineering. It is concluded that DTs have great potential to improve safety performance by engineering resilience and adaptiveness at the individual level, while how DTs embody safety values and how safety values in turn influence the adoption of DTs remain an open question.

Real-time Worker Safety Management System Using Deep Learning-based Video Analysis Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 25-30
Author(s):  
So Yeon Jeon ◽  
Jong Hwa Park ◽  
Sang Byung Youn ◽  
Young Soo Kim ◽  
Yong Sung Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Analysis ◽  
Management System ◽  
Safety Management ◽  
Worker Safety ◽  
Safety Management System ◽  
Analysis Algorithm

scholarly journals Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
E. Bertino ◽  
M. R. Jahanshahi ◽  
A. Singla ◽  
R.-T. Wu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Original Data ◽  
Local Analysis ◽  
Civil Infrastructure ◽  
Time Requirements ◽  
Iot Devices ◽  
Learning Reinforcement ◽  
The Times ◽  
Acquisition Processes

AbstractThis paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

scholarly journals Work-Related Accident Prevention in Norwegian Road and Maritime Transport: Examining the Influence of Different Sector Rules

2021 ◽  
Vol 6 (5) ◽  
pp. 72
Author(s):  
Tor-Olav Nævestad ◽  
Beate Elvebakk ◽  
Karen Ranestad
Keyword(s):  
Safety Management ◽  
Qualitative Interviews ◽  
Road Transport ◽  
Accident Prevention ◽  
Small Scale ◽  
Maritime Transport ◽  
The Road ◽  
Work Related ◽  
Safety Regulations ◽  
Framework Conditions

About 36% of fatal road accidents in Norway involve at least one driver who is “at work”. It has been argued that the implementation of rules clearly defining the responsibility of road transport companies to prevent work related accidents, by implementing safety management systems (SMS), could lead to increased safety. In the present study we tested the validity of this suggestion, by examining the influence of different sector rules on work-related accident prevention in Norwegian road and maritime transport. In contrast to the road sector, the maritime sector has had rules requiring SMS for over 20 years, clearly defining the shipping companies responsibility for prevention of work-related accidents. The aims of the study were to: (1) examine how the different sector rules influence perceptions of whether the responsibility to prevent work-related accidents is clearly defined in each sector; and (2) compare respondents’ perceptions of the quality of their sectors’ efforts to prevent work-related accidents, and factors influencing this. The study was based on a small-scale survey (N = 112) and qualitative interviews with sector experts (N = 17) from companies, authorities, and NGOs in the road and the maritime sectors. Results indicate that respondents in the maritime sector perceive the responsibility to prevent work-related accidents as far more clearly defined, and they rate their sector’s efforts to prevent accidents as higher than respondents in road. Multivariate analyses indicate that this is related to the scope of safety regulations in the sectors studied, controlled for several important framework conditions. Based on the results, we conclude that the implementation of SMS rules focused on transport companies’ responsibility to prevent work-related accidents could improve safety in the road sector. However, due to barriers to SMS implementation in the road sector, we suggest starting with a simplified version of SMS.

A data-driven real-time stability metric for SST-based microgrids

2022 ◽  
Vol 134 ◽  
pp. 107397
Author(s):  
Farzad Ferdowsi ◽  
Hesan Vahedi ◽  
Ali Jafarian Abianeh ◽  
Chris S. Edrington ◽  
Touria Elmezyani
Keyword(s):  
Real Time ◽  
Data Driven ◽  
Time Stability ◽  
Real Time Stability

scholarly journals Identification of Granule Growth Regimes in High Shear Wet Granulation Processes Using a Physics-Constrained Neural Network

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 737
Author(s):  
Chaitanya Sampat ◽  
Rohit Ramachandran
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Data Driven ◽  
Wet Granulation ◽  
Manufacturing Processes ◽  
High Shear ◽  
Process Data ◽  
Physical Constraints ◽  
Granule Growth

The digitization of manufacturing processes has led to an increase in the availability of process data, which has enabled the use of data-driven models to predict the outcomes of these manufacturing processes. Data-driven models are instantaneous in simulate and can provide real-time predictions but lack any governing physics within their framework. When process data deviates from original conditions, the predictions from these models may not agree with physical boundaries. In such cases, the use of first-principle-based models to predict process outcomes have proven to be effective but computationally inefficient and cannot be solved in real time. Thus, there remains a need to develop efficient data-driven models with a physical understanding about the process. In this work, we have demonstrate the addition of physics-based boundary conditions constraints to a neural network to improve its predictability for granule density and granule size distribution (GSD) for a high shear granulation process. The physics-constrained neural network (PCNN) was better at predicting granule growth regimes when compared to other neural networks with no physical constraints. When input data that violated physics-based boundaries was provided, the PCNN identified these points more accurately compared to other non-physics constrained neural networks, with an error of <1%. A sensitivity analysis of the PCNN to the input variables was also performed to understand individual effects on the final outputs.

Advanced Driver Assists and Safety Management System

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Dipti Chavan ◽  
Aniket Kamble ◽  
Aditya Khadsare ◽  
Vaibhav Chougule ◽  
Vaibhav Chougule
Keyword(s):  
Real Time ◽  
Automobile Industry ◽  
Safety Management ◽  
Public Utilities ◽  
Sensor Platform ◽  
Key Points ◽  
Different Types ◽  
Important Field ◽  
And Control ◽  
Web Platform

Electronics and communication is the most important field. In this paper, we can describe how much safety is in the Automobile industry. In this paper, we are using uno-Arduino. The different types of sensors facilities are also provided using key points. The different sensors are provided to check visitor count. In this system, we can monitor and control all the safety precautions their one IoT web platform. This helps in the proper utilization of drivers and helps in avoiding accidents. This paper can be implemented in any two-wheelers, heavily loaded trucks, small SUVs, compact cars. In our paper, the electronics machine/components will be automatically working with using of Arduino program. The proposed wireless sensor platform is an attempt to develop more safety devices that can be used in multiple areas such as homes, schools, and public utilities to reduce accidents. This Advanced Driver Assists system will provide real-time accident detections and monitoring usage information that helps in real-time by using GSM, GPS, and sensors.

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