İnşaat sektöründe standart ve düzenlileştirilmiş lojistik regresyon modelleri kullanılarak iş kazalarının şiddetinin tahmini

Yıl 2023, , 778 - 798, 15.07.2023

Şura Toptancı , Nihal Erginel , Ilgın Poyraz Acar

https://doi.org/10.28948/ngumuh.1212385

Öz

İnşaat sektöründe iş kazaları diğer sektörlere kıyasla daha sık meydana gelmektedir. İnşaat iş kazaları hâlâ istenilen düzeyde önlenememiştir. Literatürde klasik istatistiksel ve makine öğrenmesi teknikleri kullanılarak bu kazaların meydana gelme sıklığını tahmin etmek için birçok çalışma yapılmaktadır. Ancak, büyük boyutlu ve çok sayıda kategorik değişken içeren veriler analiz edilirken, veri setinde bulunan dengesizlik ve çoklu bağlantı sorunlarına ilişkin bazı problemler dikkate alınmamaktadır. Bu çalışma daha doğru sonuçlar elde edebilmek için bahsedilen problemleri dikkate alarak, ölümcül olmayan inşaat kazalarının şiddetini tahmin etmeyi amaçlamaktadır. Bu çalışmada, inşaat sektöründe iş günü kaybının tahmini için standart ikili lojistik regresyon, Firth, Ridge, Lasso ve Elastik Net düzenlileştirilmiş lojistik regresyon modelleri kullanılmış ve sonuçlar karşılaştırılmıştır. Kullanılan veriler kazazede, iş yeri, kaza zamanı, kaza ve olaylar zinciri ve kaza sonrası durumla ilgili değişkenler olmak üzere beş gruba ayrılmıştır. Sonuçlar, Firth'in lojistik modelinin en iyi performans gösteren model olduğunu ve yaş, eğitim, mesleki eğitim, işyeri büyüklüğü, proje türü, çalışılan ortam, kaza ayı ve yılı, genel ve özel faaliyetler, kullanılan materyal, yaranın türü ve yaranın vücuttaki yerinin en önemli değişkenler olduğunu göstermiştir. Yorumlanabilir makine öğrenimi araçları sağlayan bu çalışma, literatürde inşaat güvenliği alanında önerilen modelleri kullanmaya yönelik ilk girişimdir.

Anahtar Kelimeler

İş kazaları, İnşaat sektörü, Lojistik regresyon, Makine öğrenmesi, Kaza şiddeti

Destekleyen Kurum

Eskişehir Teknik Üniversitesi Bilimsel Araştırma Projeleri Komisyonu (1705F427 nolu proje). Bu çalışma, birinci yazarın hazırladığı doktora tezinden üretilmiştir.

Proje Numarası

BAP 1705F427

Teşekkür

Yazarlar, çalışmada kullanılan verinin temini için katkı sağlayan Sosyal Güvenlik Kurumu ve Hizmet Sunumu Genel Müdürlüğü’ne teşekkür eder.

Predicting the severity of occupational accidents in the construction industry using standard and regularized logistic regression models

Öz

Occupational accidents in the construction industry occur more frequently when compared with other industries. Construction occupational accidents still have not been prevented at the desired level. Several studies in the literature have been conducted to predict the occurrence frequency of these accidents using classical statistical and machine-learning techniques. However, some challenges regarding imbalanced and multicollinearity problems present in the dataset are not considered while analyzing data with a large size and a large number of categorical variables. This study aims to predict the severity of non-fatal construction accidents considering mentioned challenges to obtain more accurate results. In this study, standard binary logistic regression, Firth, Ridge, Lasso, and Elastic Net Regularized logistic regression models were used for the prediction of lost workdays in the construction industry and results were compared. The data used were classified into five groups: victim, workplace, accident time, accident and sequence of events, and post-accident state-related variables. The results showed that Firth’s logistic model is the best-performing model and age, education, vocational education, workplace size, project type, working environment, accident month and year, general and specific activities, material agent, type of injury, and part of body injured are the most significant variables. This study, by providing interpretable machine learning tools, is the first attempt to use proposed models in the area of construction safety in the literature.

Anahtar Kelimeler

Occupational accidents, Construction industry, Logistic regression, Machine learning, Accident severity

Proje Numarası

BAP 1705F427

Kaynakça

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