A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Machine Learning Applications in Geotechnical Earthquake Engineering: Progress, Gaps, and Opportunities
The rise of data capture and storage capabilities have led to greater data granularity and sharing of data sets in geotechnical earthquake engineering. This broader shift to big data requires ways to process and extract value from it and is aided by the progress in methodologies from the computer science domain and advancements in computer hardware capabilities. General machine learning (ML) models typically receive a set of input parameters and run them through an algorithm to gain outputs with no constraints on the parameters or algorithm process. Three topic areas of ML applications in geotechnical earthquake engineering are reviewed and summarized in this paper: seismic response, liquefaction triggering analysis, and performance-based assessments (lateral displacements and settlement analysis). The current progress of ML is summarized, while the challenges and potential in adopting such approaches are addressed.
Machine Learning Applications in Geotechnical Earthquake Engineering: Progress, Gaps, and Opportunities
The rise of data capture and storage capabilities have led to greater data granularity and sharing of data sets in geotechnical earthquake engineering. This broader shift to big data requires ways to process and extract value from it and is aided by the progress in methodologies from the computer science domain and advancements in computer hardware capabilities. General machine learning (ML) models typically receive a set of input parameters and run them through an algorithm to gain outputs with no constraints on the parameters or algorithm process. Three topic areas of ML applications in geotechnical earthquake engineering are reviewed and summarized in this paper: seismic response, liquefaction triggering analysis, and performance-based assessments (lateral displacements and settlement analysis). The current progress of ML is summarized, while the challenges and potential in adopting such approaches are addressed.
Machine Learning Applications in Geotechnical Earthquake Engineering: Progress, Gaps, and Opportunities
Cheng, Katherine (author) / Ziotopoulou, Katerina (author)
Geo-Congress 2023 ; 2023 ; Los Angeles, California
Geo-Congress 2023 ; 493-505
2023-03-23
Conference paper
Electronic Resource
English
| British Library Conference Proceedings | 2023
Geotechnical earthquake engineering
| TIBKAT | 1996
Geotechnical earthquake engineering
| TIBKAT | 2025
Geotechnical earthquake engineering
| TIBKAT | 2008
Geotechnical Earthquake Engineering
| Online Contents | 1997