Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Damage Assessment of Structures Following the February 6, 2023 Kahramanmaraş Earthquakes: A Dataset-Based Case Study in Gaziantep, Türkiye(Springer Heidelberg, 2025) Atasever, Kurtulus; Aydogdu, Hasan Huseyin; Narlitepe, Furkan; Goksu, Caglar; Demir, Ugur; Demir, Cem; Ilki, AlperFollowing the 2023 Kahramanmara & scedil; Earthquakes (Mw 7.7 and 7.6) that struck T & uuml;rkiye on February 6, 2023, the Ministry of Environment, Urbanization, and Climate Change (MoEUCC) initiated a large-scale post-earthquake damage assessment campaign, targeting more than 2,3 million structures within the affected region. A comprehensive field survey was carried out in and around Gaziantep, one of the most severely affected cities. The authors assessed more than 1700 structures representing a wide range of occupancy types, including residential, educational, healthcare, religious, administrative, industrial, and lodging structures. In this paper, the methodological process of post-earthquake data collection in and around Gaziantep is presented, together with the data on the distribution of damage with respect to construction period, number of stories, and building occupancy type, to ensure a complete understanding of the extent and characteristics of structural damage. The damage assessment employed two data sources: (i) the data gathered through the authors' newly developed, novel damage-assessment software, presented here for the first time, and (ii) the official post-earthquake damage database of the MoEUCC. A further novelty of this study is the presentation of the largest dataset to date for the investigated earthquake doublet, encompassing approximately 1700 buildings. Additionally, the relationship between damage states, peak ground accelerations, and fault distances is thoroughly investigated. The detailed earthquake-hit site investigations revealed that the examined structures displayed structural inadequacies akin to those witnessed in previous seismic events, with a notable focus on the arrangement of the structural system, the quality of construction materials and reinforcement detailing.Article An Alternative Software Benchmarking Dataset: Effort Estimation With Machine Learning(Elsevier Science Inc, 2026) Yurum, Ozan Rasit; Unlu, Huseyin; Demirors, OnurEffort estimation plays a vital role in software project planning, as accurate estimates of required human resources are essential for success. Traditional estimation models often depend on historical size and effort data, yet organizations frequently struggle to access reliable effort records. Public benchmarking datasets like ISBSG offer useful data but may lack coverage or involve licensing fees. To address this issue, we previously introduced a free, extendable benchmarking dataset that integrates functional size and effort data extracted from 18 studies. In this study, we examine the effectiveness of our dataset for predictive effort estimation and compare it with the widely used ISBSG dataset. Our analysis includes 337 records from our dataset and 732 ISBSG projects, focusing on those with COSMIC size data. We first developed and compared models using linear regression and nine machine learning algorithms - Bayesian Ridge, Ridge Regression, Decision Tree, Random Forest, XGBoost, LightGBM, k-Nearest Neighbors, Multi-Layer Perceptron, and Support Vector Regression. Then, we selected the best-performing models and applied them to an unseen evaluation dataset to assess their generalization performance. The results show that machine learning performance varies based on evaluation method and dataset characteristics. Despite having fewer records, our dataset enabled more accurate predictions than ISBSG in most cases, highlighting its potential for effort estimation. This study demonstrates the viability of our dataset for building predictive models and supports the use of machine learning in improving estimation accuracy. Expanding this dataset could offer a valuable, open-access resource for organizations seeking effective and lowcost estimation solutions.