WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

Browse

Filters

2023 - 20252

Settings

Access type: Open accessAuthor: search.filters.author.Cavka, Hasan Burak

Search Results

Now showing 1 - 2 of 2
  • Article
    Retrospective Bim Performance Analysis Based on Construction Big Data
    (Emerald Group Publishing Ltd, 2025) Bostan, Berkay Batuhan; Çavka, Hasan Burak; Cavka, Hasan Burak; Citipitioglu, Ahmet Muhtar; Pehlivan, Deniz Ziya
    PurposeThe literature suggests employing big data and Building Information Modeling (BIM) to examine building projects from several perspectives. Nevertheless, the literature is deficient in thorough BIM performance evaluation methods grounded in big construction project data. This paper presents an evaluation framework outlining the data input requirements and necessary data to conduct research leveraging big data for the analysis of BIM performance.Design/methodology/approachData parameters and performance metrics included in the evaluation framework are derived from a synthesis of literature review, data overview and interviews. The construction data was analyzed using PowerBI after undergoing a quality control process. Analysis results were verified through interviews with the main contractor. The project data served to assess the evaluation framework.FindingsThe evaluation framework has ten data parameters, and six performance metrics categorized into three main categories. The findings indicate that the evaluation framework can be utilized to comment on BIM performance in a project, with a level of accuracy. Results indicated that ensuring the quality of tracked project data is crucial for obtaining reliable analysis results. Determining performance metrics and data parameters prior to data recording processes can help simplify the analysis process and ensure accurate analysis results.Originality/valueThe proposed framework offers a comprehensive performance evaluation methodology that leverages the innovative application of unique and challenging to acquire big data, allowing practitioners to assess BIM performance in relation to project time, cost and scope. Identified data parameters and novel performance metrics may provide the foundation of a guideline for construction project data logging to facilitate accurate BIM performance monitoring.
  • Conference Object
    Decoding and Predicting the Attributes of Urban Public Spaces With Soft Computing Models and Space Syntax Approaches
    (Ecaade-education & Research Computer Aided Architectural design Europe, 2023) Yonder, Veli Mustafa; Dogan, Fehmi; Cavka, Hasan Burak; Tayfur, Gokmen; Dulgeroglu, Ozum
    People spend a considerable amount of time in public spaces for a variety of reasons, albeit at various times of the day and during season. Therefore, it is of utmost importance for both urban designers and local authorities to try to gain an understanding of the architectural qualities of these spaces. Within the scope of this study, squares and green parks in Izmir, the third largest city in Turkey, were analyzed in terms of their dimensions, landscape characteristics, the quality of their semi-open spaces, their landmarks, accessibility, and overall aesthetic quality. Using linear predictor, general regression neural networks, multilayer feed-forward neural networks (2-3-4-5-6 nodes), and genetic algorithms, soft computing models were trained in accordance with the results of the conducted analyses. Meanwhile, using space syntax methodologies, a visibility graph analysis and axial map analysis were conducted. The training results (i.e., root mean square error, mean absolute error, bad prediction rates for testing and training phases, and standard deviation of absolute error) were obtained in a comparative table based on training times and root mean square error values. According to the benchmarking table, the network that most accurately predicts the aesthetic score is the 2-node MLFNN, whereas the 6-node MLFN network is the least successful network.