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    Unsupervised learning in civil engineering
    (Nova Science Publishers, Inc., 2024) Abut, Yavuz; Abut, Serdar
    This chapter explores the application of unsupervised learning in civil engineering, focusing on its advantages and challenges. Unsupervised learning is a machine learning approach that is becoming increasingly popular in the field of civil engineering. This method utilizes the model's ability to learn from unlabeled datasets and focuses on uncovering structures and patterns within the data. This type of learning offers several benefits for civil engineers. One advantage of unsupervised learning methods is the ability to analyze large amounts of unlabeled data more effectively. Labeling datasets, especially in complex data types such as images or sensor data, can be a tedious and time-consuming task. Unsupervised learning provides a more efficient alternative to overcome this challenge. Another advantage is the capability to discover hidden structures and patterns within datasets, allowing for deeper analysis. For example, these methods can be utilized to detect early signs of deformation or damage in a structure. By identifying similarities and differences within the dataset, these methods can detect damaged areas or abnormal behavior. Furthermore, unsupervised learning methods can help civil engineers in discovering features within their datasets. This is particularly important in large datasets or those obtained from various sources. By extracting features from the dataset, unsupervised learning methods can improve data representation and yield better results. In conclusion, the application of unsupervised learning in civil engineering can enhance the data analysis and pattern discovery processes. These methods provide civil engineers with valuable insights by leveraging information from unlabeled datasets, thereby aiding in making better decisions. With the expected increase in unsupervised learning studies in civil engineering, we can anticipate more application areas and further advancements in techniques in the future. © 2024 by Nova Science Publishers, Inc. All rights reserved.