Akademik Veri Yönetim Sistemi
Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü, Türkiye
Tezin Onay Tarihi: 2019
Tezin Dili: İngilizce
Öğrenci: BİLAL UMUT AYHAN
Danışman: Onur Behzat Tokdemir
Özet:
The
predictive modeling is a popular research area among researchers. Most of
the proposed models cannot provide a solution for the needs of every contractor
as the existing ones served for only a specific task. Therefore, using these
systems become inevitably burden on contractors due to its difficulty of use.
The thesis aims to provide an AI-based safety assessment strategy for every
project. The assessment strategy encapsulated the detection of trends in safety
failures and corrective actions to prevent them. The study covered two parts.
The first part explained a hybrid model of ANN and Fuzzy Set Theory, based on
over 17,000 incident cases. The ANN model achieved to forecast 84% incident
within 90% confidence, and integrating the fuzzy inference system increased the
prediction performance slightly. The second part introduced the use of LCCA as Big Data analytics to address the heterogeneity problem. Although the model
employed around 5,000 cases for training, the prediction performance was quite
similar to the first part. Besides, this part included a comparison of CBR and ANN
to reveal which approach demonstrated better compliance with the incident data.
Results exhibited the inclusion of big data analytic improved the prediction performance
despite a significant decrease in sample size. The study advanced with the
fatal accident analysis to promote prevention measures. Measures offered attribute-based
corrections by examining the relationships between the attributes. Ultimately,
the proposed methodology can aid construction industry professionals in analyzing
prospective safety problems using the large-scale collected data during the construction.