Improving Hazard-Recognition Performance and Safety Training Outcomes: Integrating Strategies for Training Transfer

Namian, Mostafa; Albert, Alex; Zuluaga, Carlos M.; Jaselskis, Edward J.

doi:10.1061/(asce)co.1943-7862.0001160

Cited by 97 publications

(52 citation statements)

References 40 publications

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“…Among other ways, hazard recognition is one of the first steps in effective safety management [6][7][8]. However, results on the effectiveness across the globe have shown that up to 50% of construction hazards remain unrecognized despite that training and certification are provided [6][7][8][9][10][11][12]. Among the contributing factors for such poor hazard awareness are worker's paying attention on their task, ignoring predominantly some types of hazards, and having low capabilities in "visual search".…”

Section: Introductionmentioning

confidence: 99%

Case Study on Mobile Virtual Reality Construction Training

Wolf¹,

Teizer²,

Ruse³

2019

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

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Recent surveys among construction firms found, a majority has a hard time filling craft worker/hourly positions and salaried jobs. Among the ways they are trying to create more is in-house training. However, existing learning methods have been lagging effectiveness or are outdated. New approaches, like mobile virtual reality, are being investigated. In this paper, the authors describe their approach to a low cost virtual reality training that offers personalized feedback for trainees or workers. The developed approach utilizes elements of gamification for motivational purposes. While the training requirements were gathered in dialogue with leading companies in the construction and engineering industry sectors, the research conducted focused on prototyping and testing the novel learning concept. As a result, the authors developed a mobile virtual reality application that utilizes the Google Daydream SDK that runs on Google Cardboard, Samsung Gear VR, Oculus Go or compatible other inexpensive devices. The application was tested and evaluated by industry representatives. An outlook provides the path forward in research and development.

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Section: Introductionmentioning

confidence: 99%

Case Study on Mobile Virtual Reality Construction Training

Wolf¹,

Teizer²,

Ruse³

2019

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

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“…The methods used in a wide variety of research itegrasi these risks include SPSS can use to measure project risks can and should be integrated with the environmental risk assessment [1]. Potential risks can be determined by Multi Attribute Group Decision Making (MAGDM) and analyzed by GTOPSIS [21]. The hybrid motode Economic Input-Output Life Cycle Assessment (EIO-LCA) can to help decision makers make better decisions about the construction industry, adding the quality of the environment and sustainable development [38].…”

Section: B Identification Of Risk Assessment Techniquesmentioning

confidence: 99%

A Review on Risk Factors in the Project of Oil and Gas Industry

Rodhi

Anwar

Wiguna

2017

JTS

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Abstract In Each activity in the oil and gas industry there are risks that could hamper the project and also there is a huge risk to the environment, social and economic. Besides oil and gas industry projects also have the potential to be dangerous to life, property and the environment, the hazard can occur if the activity is not controlled and regulated by appropriate. Basically, the disaster is certainly not possible to be planned and can occur at any time. But the circumstances of a disaster can be anticipated, that is the way to plan preventive measures and control.Based on the description above, this paper aims to discuss risk factors and risk assessment techniques that can help sustainable development programs. From the results of this study note that the risk factors of projects in the oil and gas industry is very complex, so a risk analysis must be done from various aspects, namely the project itself, the environment and disaster and should be integrated with aspects of sustainable development. As for the suggested technique is a combination of monte carlo and system dynamics. KeywordsSustainable Development, Risk Assessment Techniques, Combination Monte Carlo and System DynamicsAbstrak Dalam setiap kegiatan di industri minyak dan gas ada risiko yang dapat menghambat proyek dan juga ada risiko besar terhadap lingkungan, sosial dan ekonomi. Selain proyek industri migas juga berpotensi membahayakan kehidupan, properti dan lingkungan, bahaya bisa terjadi jika aktivitasnya tidak terkontrol dan diatur dengan tepat. Pada dasarnya, bencana ini tentu tidak mungkin direncanakan dan bisa terjadi kapan saja. Namun keadaan bencana dapat diantisipasi, itulah cara untuk merencanakan tindakan pencegahan dan pengendalian. Berdasarkan uraian di atas, makalah ini bertujuan untuk membahas faktor risiko dan teknik penilaian risiko yang dapat membantu program pembangunan berkelanjutan. Dari hasil penelitian ini diketahui bahwa faktor risiko proyek di industri minyak dan gas bumi sangat kompleks sehingga analisis risiko harus dilakukan dari berbagai aspek, yaitu proyek itu sendiri, lingkungan dan bencana dan harus diintegrasikan.

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“…The effectiveness of these methods depends on the quality of the input data, i.e. the data on oil supply, consumption, and natural loss [11,12]. However, it is very difficult to collect a reliable set of input data, because they exist in large quantities and with high diversity [13].…”

Section: Introductionmentioning

confidence: 99%

Detection of Abnormal Oil Data Based on Feature Selection

Li¹,

Fan²,

Yang³

et al. 2019

RIA

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To achieve effective oil management, it is critical to disclose the laws of oil supply, consumption, and natural loss through data analysis. However, the accuracy of data analysis is often suppressed by the mistakes and irrelevance of the input data, which are inevitable due to the large size and diversity of the data collected from the oil depots. To solve the problem, this paper proposes an abnormal oil data detection approach based on feature selection (AODDFS). In the AODDFS, the format of the input data was preprocessed to satisfy the requirements of feature selection; the fisher score was then employed to compute the relevance of each entry with normal features; finally, the abnormal entries were located based on the relevance values. Then, the AODDFS results were analyzed with boxplot and standard deviation. Finally, the AODDFS was verified through a case study on the data collected from several large oil depots. The results show that the AODDFS can effectively detect abnormal oil data with a precision of 85.00% and a recall of 80.94%.

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Improving Hazard-Recognition Performance and Safety Training Outcomes: Integrating Strategies for Training Transfer

Cited by 97 publications

References 40 publications

Case Study on Mobile Virtual Reality Construction Training

Case Study on Mobile Virtual Reality Construction Training

A Review on Risk Factors in the Project of Oil and Gas Industry

Detection of Abnormal Oil Data Based on Feature Selection

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