Most research on the application of machine learning to engineering problems have solved artificial problems. While research claimed to have reached results that would improve practice, these results have never been put to work by engineers themselves in solving their problems. A different approach of doing research on machine learning application is presented and a system design that may result in tangible practical results is outlined. The development of this system is underway. INTRODUCTION In order to make machine learning (ML) techniques usable for engineers, a methodological shift is required in the way ML research is perceived, planned, and executed. Past investigations that dealt with the development of ML techniques for solving engineering problems mostly developed ideas that were tested on simplified artificial problems. Thus, researchers could not demonstrate that their ideas had practical implications. With no direct connection between research and practice, researchers w...
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in Proceedings of the First Congress on Computing in Civil Engineering