Engineers have continuously been striving to improve the efficiency of conventional materials, solutions, and the testing methodology in civil engineering. With the advancement of materials science and different composite materials, complex mathematical problems have recently been introduced in civil engineering. As a result, the traditional methods of underlying theories and testing methods cannot be performed. Elsewhere, these modern solutions and materials may be exposed to extreme natural or non-natural loading circumstances during their service life and cause tremendous fatalities and property loss.

Machine learning (ML) provides a wide range of applications in our current society, including predicting, classifying, and solving complex mathematical problems in civil engineering. ML methods and techniques, including neural networks, evolutionary computation, fuzzy logic systems, deep learning, and image processing applications, have rapidly evolved in recent decades. Recently, ML algorithms have attracted close attention from researchers and have also been applied successfully to solve problems in civil engineering. For example, informing unmanned, intelligent, and fully automatic urban and regional planning, prediction of rainfall, hydrological problems, as well as developing new technologies, engineering design, construction, maintenance, and disaster management.

This Special Issue aims to publish original research and review articles on the application of ML in civil engineering structures, the application of modern solutions and methods under the modern testing rigs, particularly their applications in the structures under extreme loading or environmental conditions.

Potential topics include but are not limited to the following:

• ML for modern solutions
• ML for structural health monitoring
• ML for predicting engineering materials behavior
• ML for extreme loading
• ML for modeling of civil engineering
• Classification problems in civil engineering
• ML in civil engineering
• ML for predicting hydrological problems
• Intelligent engineering materials and structures
• Intelligent structural design and materials