EDITORIAL SCOPE: A PERSPECTIVE ON DATA LIMITATIONS AND MACHINE LEARNING APPLICATIONS IN CIVIL AND ENVIRONMENTAL ENGINEERING
Keywords:
machine learning, civil and environmental engineering, high-quality dataset, limited/incomplete data, generalisation capacityAbstract
Although data-driven machine learning (ML) techniques have been widely applied in civil and environmental engineering, their performance depends heavily on the availability of large, high-quality, and reliable datasets. In civil engineering, researchers often face significant challenges due to limited, incomplete, or inaccessible data. This editorial scope discusses the main reasons behind these challenges and explores potential solutions. It also emphasises the urgent need for standardised data collection practices and the development of new ML models with higher levels of reliability, accuracy, and generalisation.
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