Browsing by Author "Naser M.Z."
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Item Hiding in plain sight: What can interpretable unsupervised machine learning and clustering analysis tell us about the fire behavior of reinforced concrete columns?(Elsevier Ltd, 2022) Özyüksel Çiftçioğlu A.; Naser M.Z.The role of machine learning (ML) continues to rise in the structural fire engineering area. Noting the widespread of supervised ML approaches, such methods are being heavily utilized nowadays. On the other hand, little interest has been dedicated to unsupervised ML. Unlike supervised learning, unsupervised learning algorithms are trained using data that is neither classified nor labeled, thus, allowing the algorithm, and without guidance, to identify unseen patterns and decode hidden structures of complex phenomena residing in data. In this pursuit, this study presents findings obtained from one of the earliest investigations aimed to explore the potential of unsupervised and interpretable machine learning (ML) clustering analysis to investigate the response of reinforced concrete (RC) columns under fire conditions. We used four algorithms (namely, K-Means, Hierarchical, Fuzzy C-Means, and DBSCAN) to cluster over 140 fire-exposed RC columns. Results from our clustering analysis show that the performance of such columns can be distinctly grouped into unique clusters. Our findings allow structural fire engineers to 1) identify and avoid RC columns with poor fire performance and, when possible, 2) upgrade the features/characteristics of columns of poor fire response to achieve an improved behavior. Overall, our analysis indicates that fire-exposed RC columns naturally fit into four clusters – each with unique properties and response – that are governed by the material, geometric and loading features. © 2022 Institution of Structural EngineersItem Causal discovery and inference for evaluating fire resistance of structural members through causal learning and domain knowledge(John Wiley and Sons Inc, 2023) Naser M.Z.; Çiftçioğlu A.Ö.Experiments remain the gold standard to establish an understanding of fire-related phenomena. A primary goal in designing tests is to uncover the data generating process (i.e., the how and why the observations we see come to be); or simply what causes such observations. Uncovering such a process not only advances our knowledge but also provides us with the capability to be able to predict phenomena accurately. This paper presents an approach that leverages causal discovery and causal inference to evaluate the fire resistance of structural members. In this approach, causal discovery algorithms are adopted to uncover the causal structure between key variables pertaining to the fire resistance of reinforced concrete columns. Then, companion inference algorithms are applied to infer (estimate) the influence of each variable on the fire resistance given a specific intervention. Finally, this study ends by contrasting the algorithmic causal discovery with that obtained from domain knowledge and traditional machine learning. Our findings clearly show the potential and merit of adopting causality into our domain. © 2023 The Authors. Structural Concrete published by John Wiley & Sons Ltd on behalf of International Federation for Structural Concrete.Item Revisiting Forgotten Fire Tests: Causal Inference and Counterfactuals for Learning Idealized Fire-Induced Response of RC Columns(Springer, 2023) Naser M.Z.; Çiftçioğlu A.Ö.The expensive nature and unique facilities required for fire testing make it difficult to conduct comprehensive experimental campaigns. As such, engineers can often afford to test a small number of specimens. This complicates attaining a proper inference, especially when addressing questions in the form of what would have been the fire response of a particular specimen had it been twice as large? Or had it been made from a different material grade? In hindsight, answering causal and hypothetical (counterfactual) questions goes beyond the capacity of statistical and machine learning methods which were built to address observational data. To overcome the above challenges, this paper presents a causal approach to answering such questions. In this approach, principles of causal inference are adopted to reconstruct the deformation-time history of reinforced concrete (RC) columns and propose an idealized fire response for these columns. The findings of this study indicate the significant influence of the loading level, aggregate type, and longitudinal steel ratio on the deformation history of fire-exposed RC columns. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item 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.Item 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.Item Identifying and estimating causal effects of bridge failures from observational data(Elsevier B.V., 2024) Çiftçioğlu A.Ö.; Naser M.Z.This paper presents a causal analysis aimed at identifying and estimating causal effects with regard to bridge failures under extreme events. Observational data on about 299 bridge incidents were used to conduct this causal investigation and examine bridges’ performance. As causal investigations can also deliver counterfactual assessments of parallel worlds, a causal analysis can serve as a high-merit methodology to evaluate the performance of critical bridges. Our findings quantify the causal impacts of various factors spanning the characteristics of bridges, traffic demands, and incident type (i.e., fire, high wind, scour/flood, earthquake, and impact/collision). More specifically, our analysis reveals high causal effects related to the used structural system, construction materials, and demand served. © 2023 The AuthorsItem Fire resistance evaluation through synthetic fire tests and generative adversarial networks(Higher Education Press Limited Company, 2024) Çiftçioğlu A.Ö.; Naser M.Z.This paper introduces a machine learning approach to address the challenge of limited data resulting from costly and time-consuming fire experiments by enlarging small fire test data sets and predicting the fire resistance of reinforced concrete columns. Our approach begins by creating deep learning models, namely generative adversarial networks and variational autoencoders, to learn the spatial distribution of real fire tests. We then use these models to generate synthetic tabular samples that closely resemble realistic fire resistance values for reinforced concrete columns. The generated data are employed to train state-of-the-art machine learning techniques, including Extreme Gradient Boost, Light Gradient Boosting Machine, Categorical Boosting Algorithm, Support Vector Regression, Random Forest, Decision Tree, Multiple Linear Regression, Polynomial Regression, Support Vector Machine, Kernel Support Vector Machine, Naive Bayes, and K-Nearest Neighbors, which can predict the fire resistance of the columns through regression and classification. Machine learning analyses achieved highly accurate predictions of fire resistance values, outperforming traditional models that relied solely on limited experimental data. Our study highlights the potential for using machine learning and deep learning analyses to revolutionize the field of structural engineering by improving the accuracy and efficiency of fire resistance evaluations while reducing the reliance on costly and time-consuming experiments. © The Author(s) 2024.