Triggered by the emergence of new technologies such as automation, smart equipment, and the wide application of mobile technologies, a huge amount of data is being generated in civil engineering. This brings about challenges—such as how to analyze this data—as well as opportunities for governments, organizations, communities, and individuals to utilize this data. Thus, this has led to the emergence of a completely different paradigm for decision making.

Data mining has been widely used in civil engineering, making it a hot research topic due to its importance. For example, data mining techniques such as regression and classification have been used to analyze landslide susceptibility, suspended sediment load modelling, accident severity prediction, and concrete property estimation. Data mining can support decision-making and provide new insights for civil engineers, which inevitably involves experts from both data analytics and specialized civil engineering.

The aim of this Special Issue is to collect state-of-the-art research findings on the latest developments and challenges in the field of data mining for civil engineering. High-quality original research papers that present theoretical frameworks, methodologies, and application case studies from a single- or cross-country perspective are welcome, as well as review articles.

Potential topics include but are not limited to the following:

• Analyses or meta-analyses of existing data in civil engineering applications
• Data mining techniques, including tracking patterns, classification, association, outlier detection, clustering, regression, and prediction, for decision-making, used in civil engineering
• Cutting-edge data mining methods, such as hybrid machine learning techniques, for data mining in civil engineering application
• Web/internet data mining and application technology for civil engineering, e.g., information retrieval and web search, social network analysis, web crawling, information integration, opinion mining, and sentiment analysis
• Model updating using large-scale data in civil engineering
• Real-world civil engineering case studies of data mining, such as slope stability prediction, default detection, material property prediction, and software development for civil practitioners etc.