Special Issue on Application of Artificial Neural Networks for Seismic Design and Assessment
Source abstract
Abstract
The application of methods and techniques of Machine Learning (ML) in many scientific fields has been increasing rapidly over recent decades [...]
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short
Research context
OpenAlex topics: Seismology and Earthquake Studies · Seismic Imaging and Inversion Techniques · Drilling and Well Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Earthquake Planning and Protection Organization (EPPO-ITSAK), Terma Dasylliou, 55535 Thessaloniki, Greece
Department of Civil Engineering, Aristotle University of Thessaloniki, Aristotle University Campus, 54124 Thessaloniki, Greece
References (5)
Show source-provided references
1. Lazaridis, P.C., Kavvadias, I.E., Demertzis, K., Illiadis, L., and Vasiliadis, L.K. (2022). Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms. Appl. Sci., 12.10.20944/preprints202203.0188.v1 ↗
2. Morfidis, K., and Kostinankis, K. (2022). Rapid Prediction of Seismic Incident Angle’s Influence on the Damage Level of RC Buildings Using Artificial Neural Networks. Appl. Sci., 12.10.3390/app12031055 ↗
3. Harirchian, E., Kumari, V., Jadhav, K., Rasulzade, S., Lahmer, T., and Das, R.R. (2021). A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings. Appl. Sci., 11.10.3390/app11167540 ↗
4. Yuan, X., Tanksley, D., Li, L., Zhang, H., Chen, G., and Wunsch, D. (2021). Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks. Appl. Sci., 11.10.3390/app11219844 ↗
5. Thaler, D., Elezaj, L., Bamer, F., and Markert, D. (2022). Training Data Selection for Machine Learning-Enhanced Monte Carlo Simulations in Structural Dynamics. Appl. Sci., 12.10.3390/app12020581 ↗