Συσχέτιση Σεισμικών Παραμέτρων και Ολικών Δεικτών Βλάβης σε Κατασκευές Οπλισμένου Σκυροδέματος

In the present work, a correlation is made between seismic intensity parameters and total damage indicators of reinforced concrete structures. For this purpose, natural ground motions recordings were used, from which intensity measures were calculated and alternatives were proposed. Then, through dynamic inelastic time history analyzes, damage indices were evaluated for 1st and 2nd order theory (P-Delta effects). The correlation between each seismic parameter and the corresponding damage index was made through polynomial regressions. Seismic parameters that do not take into account the characteristics of the building, showed a minimal or moderate correlation, while spectral parameters of velocities and energies explain the change of damage indices in percentages above 80 or even 90 per cent.Στην παρούσα εργασία πραγματοποιείται συσχέτιση μεταξύ σεισμικών παραμέτρων έντα-σης και ολικών δεικτών βλάβης σε κατασκευές οπλισμένου σκυροδέματος. Για αυτόν τον σκοπό, χρησιμοποιήθηκαν καταγραφές πραγματικών σεισμών, από τις οποίες υπολογίστηκαν παράμετροι, που χαρακτηρίζουν ένα επιταχυνσιογράφημα και προτάθηκαν εναλλακτικές τους. Στη συνέχεια μέσω δυναμικών ανελαστικών αναλύσεων χρονοϊστορίας αποτιμήθηκαν δείκτες βλάβης για θεωρία 1ης και 2ης τάξης. Η συσχέτιση μεταξύ του εκάστοτε ζεύγους σεισμικής παραμέτρου και δείκτη βλάβης έγινε μέσω πολυωνυμικών παλινδρομήσεων. Σεισμικές παρά-μετροι που δεν λαμβάνουν υπόψη τους τα χαρακτηριστικά του ταλαντωτή, παρουσίασαν ελάχιστη ή μέτρια συσχέτιση, ενώ φασματικές παράμετροι ταχυτήτων και ενεργειών ερμη-νεύουν τη μεταβολή των δεικτών βλάβης σε ποσοστά άνω του 80 ή και 90 τοις εκατό

Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms

Advanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well-known ground motion intensity measures, are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial ground motion sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology is deployed in a user-friendly web application.Keywords: seismic sequence; machine learning algorithms; repeated earthquakes; structural damage prediction; intensity measures; damage accumulation; machine learning; artificial neural networ

Structural Damage Prediction Under Seismic Sequence Using Neural Networks

Advanced machine learning algorithms, such as neural networks, have the potential to be successfully applied to many areas of system modelling. Several studies have been already conducted on forecasting structural damage due to individual earthquakes, ignoring the influence of seismic sequences, using neural networks. In the present study, an ensemble neural network approach is applied to predict the final structural damage of an 8-storey reinforced concrete frame under real and artificial ground motion sequences. Successive earthquakes consisted of two seismic events are utilised. We considered 16 well-known ground motion intensity measures and the structural damage that occurred by the first earthquake as the features of the machine-learning problem, while the final structural damage was the target. After the first seismic events and after the seismic sequences, both actual values of damage indices are calculated through nonlinear time history analysis. The machine-learning model is trained using the dataset generated from artificial sequences. Finally, the predictive capacity of the fitted neural network is accessed using the natural seismic sequences as a test set

Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms

Advanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well-known ground motion intensity measures, are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial ground motion sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology is deployed in a user-friendly web application

In-silico and in-vitro elucidation of BH3 binding specificity towards Bcl-2

Interactions between Bcl-2-like proteins and BH3 domains play a key role in the regulation of apoptosis. Despite the overall structural similarity of their interaction with helical BH3 domains, Bcl-2-like proteins exhibit an intricate spectrum of binding specificities whose underlying basis is not well understood. Here, we characterize these interactions using Rosetta FlexPepBind, a protocol for the prediction of peptide binding specificity that evaluates the binding potential of different peptides based on structural models of the corresponding peptide–receptor complexes. For two prominent players, Bcl-xL and Mcl-1, we obtain good agreement with a large set of experimental SPOT array measurements and recapitulate the binding specificity of peptides derived by yeast display in a previous study. We extend our approach to a third member of this family, Bcl-2: we test our blind prediction of the binding of 180 BIM-derived peptides with a corresponding experimental SPOT array. Both prediction and experiment reveal a Bcl-2 binding specificity pattern that resembles that of Bcl-xL. Finally, we extend this application to accurately predict the specificity pattern of additional human BH3-only derived peptides. This study characterizes the distinct patterns of binding specificity of BH3-only derived peptides for the Bcl-2 like proteins Bcl-xL, Mcl-1, and Bcl-2 and provides insight into the structural basis of determinants of specificity.National Institutes of Health (U.S.) (R01GM84181