Machine Learning Techniques for Civil Engineering Problems

Abstract

The growing volume of information databases presents opportunities for advanced data analysis techniques from machine learning (ML) research. Practical applications of ML are very different from theoretical or empirical studies, involving organizational and human aspects and various other constraints. Despite the importance of applied ML, little has been discussed in the general ML literature on this topic. In order to remedy this situation, I studied practical applications of ML and developed a proposal for a seven–step process that can guide practical applications of ML in engineering. The process is illustrated by relevant applications of ML in civil engineering. This illustration shows that the potential of ML has only begun to be explored but also cautions that in order to be successful, the application process must carefully address the issues related to the seven–step process.