Civil Engineering

Permanent URI for this collectionhttps://hdl.handle.net/10679/9157

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Browsing by Author "Seyis, Senem"

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    Feature extraction for enhancing data-driven urban building energy models
    (European Council on Computing in Construction (EC3), 2023) Bolluk, Muhammed Said; Seyis, Senem; Aydoğan, Reyhan; Computer Science; Civil Engineering; KAZAZOĞLU, Senem Seyis; AYDOĞAN, Reyhan; Bolluk, Muhammed Said
    Building energy demand assessment plays a crucial role in designing energy-efficient building stocks. However, most studies adopting a data-driven approach feel the deficiency of datasets with building-specific information in building energy consumption estimation. Hence, the research objective of this study is to extract new features within the climate, demographic, and building use type categories and increase the accuracy of a non-parametric regression model that estimates the energy consumption of a building stock in Seattle. The results show that adding new features to the original dataset from the building use type category increased the regression results with a 6.8% less error and a 30.8% higher R2 Score. Therefore, this study shows that building energy consumption estimation can be enhanced via new feature extraction equipped with domain knowledge.
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    Multi-criteria decision-making model for risk management in modular construction projects
    (Taylor & Francis, 2024) Khodabocus, Sabah Fatima; Seyis, Senem; Civil Engineering; KAZAZOĞLU, Senem Seyis; Khodabocus, Sabah Fatima
    The modular sector needs a precise guide to determine the most efficient risk management approaches. The main research objective of this study is to develop a multi-criteria decision-making model to find the most efficient risk management approach according to the relevant risk criteria. The risk criteria and risk management approaches for modular construction projects were also identified and classified within this scope. A systematic literature review, semi-structured interviews, and open-ended questionnaires were performed for identification and classification purposes. For ranking and quantifying the identified risks and risk approaches, as well as developing the decision-making model, the Delphi method and the Analytical Hierarchy Process (AHP) were conducted. A two-round Delphi method, with eleven experts, was conducted to achieve efficient performance scores of the identified risk management approaches. The percentage standard deviation decreased, Relative Importance Index (RII), Cronbach’s alpha, and Kendall’s coefficient of concordance (Kendall’s W) were calculated to ensure the outputs’ reliability, validity, and agreement level. The AHP method opted to quantify the Delphi method outputs, solve the multi-criteria decision-making process, and develop the multi-criteria decision-making model for risk management of modular construction projects. Triangulation results show that the critical risk categories are supply chain, health and safety, stakeholders, and governmental support. Lean principles such as the Last Planner System, Value Stream Mapping, Just in Time, and Kaizen are top-rated risk management approaches. This research’s novelty is identifying and analyzing crucial risk categories, providing the relevant risk management approaches ranked according to efficiency performance, and presenting a decision-making model as a guideline for risk management of modular construction projects.