Application of Machine Learning Algorithms to Rocking Problems

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Δομοστατικός Σχεδιασμός και Ανάλυση των Κατασκευών

Seismic Fragility Curves of RC Buildings Subjected to Aging

A large number of existing reinforced concrete (RC) buildings have surpassed their anticipated service life and show signs of degradation due to aging; this degradation is a function of the construction practices adopted in the past as well as environmental conditions. This paper discusses seismic fragility and the risk assessment of RC structures, emphasizing the impact of corrosion due to concrete aging and the associated deterioration mechanisms. The literature on this topic is critically reviewed, and a methodology for studying the seismic fragility of deteriorated RC buildings is proposed. As a case study, a four-story RC building designed according to contemporary code provisions is examined. The investigation encompasses the derivation of fragility curves, considering critical parameters such as the corrosion rate, the initiation time, and the cover depth. The proposed approach enables the evaluation and quantification of the impact of corrosion mechanisms on the seismic performance of buildings

Site amplification prediction model of shallow bedrock sites based on machine learning models

Prediction of the site amplification is of primary importance for a site-specific seismic hazard assessment. A large suite of both empirical and simulation-based site amplification models has been proposed. Because they are conditioned on a few simplified site proxies including time-averaged shear wave velocity up to a depth of 30 m (VS30) and site period (TG), they only provide approximate estimates of the site amplification. In this study, site amplification prediction models are developed using two machine learning algorithms, which are random forest (RF) and deep neural network (DNN). A comprehensive database of site response analysis outputs obtained from simulations performed on shallow bedrock profiles is used. Instead of simplified site proxies and ground motion intensity measures, matrix data which include the response spectrum of the input ground motion and shear wave velocity profile. Both machine learning based models provide exceptional prediction accuracies of both the linear and nonlinear amplifications compared with the regression-based model, producing accurate predictions of both binned mean and standard deviation of the site amplification. Among two machine learning techniques, DNN-based model is revealed to produce better predictions