Browsing by Subject "Concrete"

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    Unsupervised Machine Learning for Fire Resistance Analysis
    (Springer Science and Business Media Deutschland GmbH, 2023) Çiftçioğlu A.Ö.; Naser M.Z.
    Due to its inert nature, concrete has good fire resistance properties. As such, concrete has often been favored for construction – especially where fire hazard is expected. However, this does not mean that reinforced concrete cannot catch fire. It can still be affected by heat and, if exposed to high temperatures, can eventually break down. Therefore, the fire resistance of the reinforced concrete (RC) columns is a critical concern. There are many ways for assessing the fire resistance of structures, but it is difficult to quantify the fire resistance in quantitative terms. The purpose of this work is to investigate the use of unsupervised machine learning by means of clustering to examine the fire resistance of RC columns. A database of over 144 RC columns subjected to standard fire conditions has been collected and then examined via the interpretable Fuzzy C-Means algorithm (FCM) and the Classification and Regression Tree (CART) model. Our results indicate that this clustering technique groups RC columns into four natural groups – each with specific properties and characteristics. Moreover, the CART model is used to analyze the variables used as the basis for the clustering of RC columns. Accordingly, when RC columns are separated into four natural clusters, the first split occurs due to restrictions, and the second separation is controlled by the compressive strength and reinforcement ratios of the columns. This research might be the first to attempt to leverage clustering analysis to investigate the fire response of RC columns. The findings of the study clearly show that unsupervised machine learning can provide valuable insights to fire engineers often missing from traditional supervised learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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    Fire Resistance Analysis Through Synthetic Fire Tests
    (Springer Science and Business Media Deutschland GmbH, 2023) Çiftçioğlu A.Ö.; Naser M.Z.
    Fire resistance analysis is a complex procedure. In this pursuit, engineers design experiments. However, fire tests are expensive and complex and require specialized equipment that is not accessible to many engineers. This further constrains the ability to test and advance fire research. In order to overcome the above challenges, this paper adopts novel machine learning to generate synthetic fire test data via Generative Adversarial Networks (GANs) from real fire tests to expand our knowledge database. Thus, with the addition of new tests, engineers will have access to a much larger pool of data that can help us to better analyze and design structures for fire. In addition, the availability of more data allows us to seriously integrate machine learning into the fire domain. Thus, this paper presents a new approach to expanding fire test data and applying regression and classification machine learning to predict the fire response of reinforced concrete columns. GANs provide an efficient way to generate synthetic data from real fire tests. Moreover, new data additions contribute to improving predictions of classification-based machine learning in comparison with regression-based machine learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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    The Application of Machine Learning on Concrete Samples
    (Springer Science and Business Media Deutschland GmbH, 2023) Çiftçioğlu A.Ö.
    Machine learning is a branch of artificial intelligence that helps computers to learn from data and make predictions based on patterns identified in big data. The purpose of this study is to explore the applicability of machine learning models in classifying the compressive strength of concrete specimens with different types of ingredients. Despite the investigations in the literature about estimating concrete density, there is no relevant study on categorizing compressive strength. To address this gap, in this study, three machine learning classification algorithms (Decision Tree, Naive Bayes Classifier, and K-Nearest Neighbors) are employed to classify concrete samples. The performance of each algorithm is evaluated and compared. The results show that the Decision Tree classifier provides the best performance with an average precision and recall of 99%, f1-score of 0.99, and accuracy of 99%. Moreover, the study provides insights into the application of ML algorithms in a real-world dataset. This study demonstrates that machine learning is a powerful tool that can be used to improve the accuracy of concrete strength classification. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.