Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Analysis of Building Information Modeling (bim) Performance Using Big Data From a Construction Project(01. Izmir Institute of Technology, 2023) Bostan, Berkay Batuhan; Çavka, Hasan BurakThis study aims to propose a systematical approach for evaluating BIM performance from a main contractor's perspective based on big data from a construction project. Retrospective case study is used as the research approach. Data is collected through interviews with the main contractor firm, and data from the logged project information in project databases including ACONEX and Microsoft Excel files. A framework containing performance metrics, specifically tailored to evaluate BIM performance based on big data, is developed from the combined analysis of literature review, interviews with main contractor, and overview of the project data. Collected project data and interview data are analyzed using the developed framework. Results of the data analysis are verified through follow-up interviews with the main contractor firm. Findings of the study suggest that it is possible to evaluate the BIM performance through analysis of collected BIM big data using the proposed systematical approach. Several performance problems were identified during the data analysis. Follow-up interviews revealed that identified performance problems from the data analysis largely coincided with the real-life experiences and accurate data entry is the key criterion for the analysis to yield correct results. The proposed framework should be tested in wider range of studies and may serve as a foundation for a future benchmarking system. Future work should focus on refining performance metrics, establishing a BIM big data database for benchmarking, exploring data's potential to be used for real-time performance assessment, and implementation of emerging Artificial Intelligence (AI) techniques for the analysis of big data.Master Thesis Recognition of Counterfactual Statements in Turkish(01. Izmir Institute of Technology, 2023) Acar, Ali; Tekir, SelmaCounterfactual statements describe an event that did not happen or cannot happen, and optionally the consequence of this event if it would happen. Counterfactual statements are the building blocks of human thought processes as people constantly reflect upon past happenings and consider their future implications. Counterfactual reasoning is essential for machine intelligence and explainable artificial intelligence studies. Detecting counterfactuals automatically with machine learning algorithms is very crucial for these areas. This thesis presents the development of the first-ever Turkish counterfactual detection dataset. It presents a comprehensive classification baseline and expands the scope of counterfactual detection to include the Turkish language.Master Thesis Application of Artificial Neural Networks To Structural Reliability Problems(01. Izmir Institute of Technology, 2023) Köroğlu, Fahri Baran; Aktaş, Engin; Maguire, MarcThe contemporary approach in structural engineering indirectly addresses uncertainties arising from load and resistance parameters by using safety factors. To consider these uncertainties in structural engineering, it is necessary to incorporate their statistical properties into the analysis and design process. However, this approach requires the calculation of challenging multi-fold probability integrals. Approximate methods known as FORM and SORM have been developed as an alternative to calculating those integrals. Unfortunately, these methods might have accuracy and convergence problems depending on the problem at hand. Simulation-based structural reliability methods have been developed to overcome the problems associated with approximate methods. The main problem with these methods is that they are often computationally expensive when along with finite element analysis, or it is hard to implement them when a more specific method is chosen to reduce computational costs. In this study, artificial neural networks have been applied to structural reliability problems to obtain accurate probability estimates with low computational cost. A special type of learning algorithm called Bayesian Regularization was used in the training of artificial neural networks. Additionally, details of the application of artificial neural networks to structural reliability problems are provided. At the end of the study, the advantages and disadvantages of applying artificial neural networks to structural reliability problems are presented and compared with other known structural reliability methods. Additionally, a new convergence criterion and an adaptive algorithm have been developed. It was observed that applying artificial neural networks to structural reliability problems provides both efficient and accurate probability estimates.Master Thesis Classification of Contradictory Opinions in Text Using Deep Learning Methods(01. Izmir Institute of Technology, 2020) Oğul, İskender Ülgen; Tekir, SelmaNatural language inference (NLI) problem aims to ensure consistency as well as accuracy of propositions while making sense of natural language. Natural language inference aims to classify the relationship between two given sentences as contradiction, entailment or neutrality. To accomplish the classification task, sentences or words must be translated into mathematical representations called vectors or embedding. Vectorization of a sentence is as important as the complexity of the classification model. In this study, both pre-trained (Glove, Fasttext, Word2Vec) and contextual word embedding methods (BERT) were used for comparison and acquire the best result. One of the natural language processing tasks NLI, is highly complex and requires solutions. Conventional machine learning methods are insufficient to carry out natural language processing solutions. Therefore, more advanced solutions are required. This study used deep learning methods to perform the classification task. Unlike conventional machine learning approaches, deep learning approaches reduce errors while increasing accuracy by repeating the data many times. Opinion sentences have complex grammatical structures that are difficult to classify. This study used Decomposable Attention and Enhanced LSTM for natural language inference to perform NLI classification task. Using the advanced LSTM deep learning method and Bert contextual vectors for natural language extraction on the SNLI dataset, an accuracy result 88.0% very close state of the art result 92.1% was obtained. In order to show the usability of the developed solution in different NLI tasks, an accuracy of 80.02% was obtained in the studies performed on the MNLI data set.Master Thesis A Language Modeling Approach To Detect Bias(Izmir Institute of Technology, 2020) Atik, Ceren; Tekir, SelmaTechnology is developing day by day and is involved in every area of our lives. Technological innovations such as artificial intelligence can strengthen social biases that already exist in society, regardless of the developers' intentions. Therefore, researchers should be aware of this ethical issue. In this thesis, the effect of gender bias, which is one of the social biases, on occupation classification is investigated. For this, a new dataset was created by collecting obituaries from the New York Times website and they were handled in two different versions, with and without gender indicators. Since occupation and gender are independent variables, gender indicators should not have an impact on the occupation prediction of models. In this context, in order to investigate gender bias on occupation estimation, a model in which occupation and gender are learned together is evaluated as well as models that make only occupation classification are evaluated. The results obtained from models state that gender bias has a role in classification occupation.