Moreover, effective additional validation ensures that models are sound, have low complexity, are fair and provide clear explanations for decisions made. Specifically, the most efficient machine learning algorithms were used in a large-scale comparison study in a sophisticated dataset of 3D R/C buildings. The current paper proposes a fully validated interpretable ML method for predicting seismic damage of R/C buildings. However, a lack of expertise associated with the use of complex ML architectures can affect the performance of the intelligent model and, ultimately, reduce the algorithm's reliability and generalization which should characterize these systems. The advancement of computer power has resulted in the development of modern soft computing methods based on the use of Machine Learning (ML) algorithms. Several researchers have proposed methods for estimating the damage response of buildings subjected to earthquake motions without conducting time-consuming analyses. ![]() Building seismic assessment is at the forefront of modern scientific research.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |