K. Morfidis

Konstantinos Morfidis, Fotios Karaoulanis, Christos Karakostas, 2019, Proceedings of the 7th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2015).

An interpretable machine learning method for the prediction of R/C buildings' seismic response

L. Iliadis, K. Morfidis, K. Kostinakis, 2022, Journal of Building Engineering.

Comparative evaluation of MFP and RBF neural networks’ ability for instant estimation of r/c buildings’ seismic damage level

Konstantinos Morfidis, Konstantinos Kostinakis, K. Morfidis, 2019, Engineering Structures.

Approaches to the rapid seismic damage prediction of r/c buildings using artificial neural networks

Konstantinos Morfidis, Konstantinos Kostinakis, K. Morfidis, 2018, Engineering Structures.

Study of Artificial Neural Networks Based Methods for the Rapid Estimation of R/C Buildings’ Seismic Damage

Konstantinos Morfidis, Konstantinos Kostinakis, K. Morfidis, 2018 .

Seismic parameters' combinations for the optimum prediction of the damage state of R/C buildings using neural networks

Konstantinos Morfidis, Konstantinos Kostinakis, K. Morfidis, 2017, Adv. Eng. Softw..

Effects of nonlinear soil–structure-interaction on seismic damage of 3D buildings on cohesive and frictional soils

Konstantinos Morfidis, Konstantinos Kostinakis, Dimitrios Sotiriadis, 2017, Bulletin of Earthquake Engineering.

Correlation between ground motion intensity measures and seismic damage of 3D R/C buildings

Asimina Athanatopoulou, Konstantinos Morfidis, Konstantinos Kostinakis, 2015 .

Practical Implementation of EC8 for Seismic Design of R/C Buildings – Flowcharts and Commentary

Agathoklis Giaralis, Asimina Athanatopoulou, Ioannis Avramidis, 2016 .

Bending of beams on three-parameter elastic foundation

Ioannis Avramidis, Konstantinos Morfidis, K. Morfidis, 2006 .

S2HM in Some European Countries

Paulo B. Lourenço, Maria Pina Limongelli, Maria Giovanna Masciotta, 2019, Seismic Structural Health Monitoring.

Classification of buildings' potential for seismic damage using a machine learning model with auto hyperparameter tuning

L. Iliadis, K. Morfidis, K. Kostinakis, 2023, Engineering Structures.

USE OF ARTIFICIAL NEURAL NETWORKS IN THE R/C BUILDINGS’ SEISMIC VULNERABILTY ASSESSMENT: THE PRACTICAL POINT OF VIEW

Konstantinos Morfidis, Konstantinos Kostinakis, K. Morfidis, 2019, Proceedings of the 7th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2015).

APPROACH TO PREDICTION OF R/C BUILDINGS’ SEISMIC DAMAGE AS PATTERN RECOGNITION PROBLEM USING ARTIFICIAL NEURAL NETWORKS

Konstantinos Morfidis, Konstantinos Kostinakis, K. Morfidis, 2017 .

The Cephalonia, Greece, January 26 (M6.1) and February 3, 2014 (M6.0) earthquakes: near-fault ground motion and effects on soil and structures

B. N. Margaris, Ch. Papaioannou, Alexandros Savvaidis, 2015, Bulletin of Earthquake Engineering.

A Comparative Evaluation of Machine Learning Algorithms for the Prediction of R/C Buildings' Seismic Damage

L. Iliadis, K. Morfidis, K. Kostinakis, 2022, ArXiv.