Evaluation of Risks of OSH Management System with Artificial Neural Networks

Year 2018, Volume: 6 Issue: 3, 73 - 83, 30.09.2018

Ercüment Neşet Dizdar , Oğuz Koçar

https://doi.org/10.21541/apjes.426502

Abstract

In the study, different models were developed for interpreting the probabilities of occupational accidents in institutions where severe OSH hazards are present, by taking into account the ANN. The coefficients of the performance determinants (R2) and the Root Mean Square Error of the developed models were evaluated. Models developed with Artificial Neural Networks, training of input and wanted output values by repeatedly applying to the network, learning realized by predicting possible accidents by minimizing faults. In this way, the frequencies of the probable occupational accidents according to the months of the new entries are estimated according to the experiment, and the top management provides reliable decision support.
Effective and continuous protection must be provided at a high level in industrial establishments in order to minimize the loss of possible accidents to people and the environment. In this context, institutions are expected to use the “Quantitative Risk Assessment Methodologies” to make decisions with the highest possible accuracy. In the OSH Management Systems, quantitative risk assessments are attempted to reduce the frequencies of possible consequences of the accidents that are scenarios for the hazards. The Artificial Neural Networks (ANN) approach is designed to support the OSH Management System as a Quantitative Risk Assessment.
All hazards and risks that may be encountered in Occupational Safety and Health are systematically and efficiently managed, and the process is solved through Occupational Safety and Health Management Systems (OSHMS) within the framework of the continuous improvement approach.

Keywords

OSH Management Systems, Quantitative Risk Assessment, Artificial Neural Networks, Occupational Safety and Health

İş Sağlığı Ve Güvenliği Yönetim Sistemlerinde Risklerin Yapay Sinir Ağlarıyla Değerlendirilmesi

Abstract

İş Sağlığı ve Güvenliğinde karşılaşılabilecek tüm tehlike ve
risklerin sistematik ve verimli biçimde yönetilmesi, sürecin sürekli
iyileştirme yaklaşımı çerçevesinde çözümlenmesi İş Sağlığı ve Güvenliği Yönetim
Sistemleri (İSG-YS) vasıtasıyla olur. Endüstriyel kuruluşlarda muhtemel
kazaların insanlara ve çevreye olan zararlarının en aza indirilmesi amacıyla,
yüksek seviyede, etkili ve sürekli korumanın sağlanması gerekmektedir. Bu
bağlamda, kurumların Risk Değerlendirme Metotlarını kullanarak, mümkün olan en
yüksek seviyede ki doğrulukla karar vermesi beklenmektedir. İSG-YS’nde, risk
değerlendirme metotlarından olan nicel yöntemle tehlikeler için üretilen senaryolarda kazaların muhtemel
sonucunun meydana gelme sıklıkları düşürülmeye çalışılmaktadır. Bu çalışmada,
Yapay Sinir Ağları (YSA) yaklaşımı, Nicel (Kantitatif) Risk Değerlendirmesi
olarak İSG-YS’ne destek olacak şekilde tasarlanmıştır. İSG tehlikelerinin
bulunduğu kurumlardaki kazaların vuku bulma olasılıkları YSA ile ele alınarak
yorumlanması için farklı modeller geliştirilmiştir. Geliştirilen modellerin
performansları belirleme katsayısı (R²) ve Karesel Hataların Karekökü
(Root Mean Square Error) ölçütleri içinde değerlendirilmiştir. Yapay Sinir
Ağları ile geliştirilen model ile, giriş ve istenilen çıkış değerlerinin tekrar
tekrar ağa uygulanmasıyla eğitimi gerçekleştirilerek, hataların minimize
edilmesiyle olası kazaların sıklığını öngören öğrenmeler gerçekleştirilmiştir.
Bu şekilde, yeni girişlerin kazandığı deneyime göre olası iş kazalarının aylara
göre frekansları tahmin edilerek, üst yönetime güvenilir karar desteği
sağlanmıştır.

Keywords

İSG Yönetim Sistemleri, Yapay Sinir Ağları, Nicel Risk Değerlendirme, İş Sağlığı ve Güvenliği

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