首页|Study Data from Scuola Universitaria Superiore IUSS Pavia Update Knowledge of Ma chine Learning (Next-generation Non-linear and Collapse Prediction Models for Sh ort- To Long-period Systems Via Machine Learning Methods)
Study Data from Scuola Universitaria Superiore IUSS Pavia Update Knowledge of Ma chine Learning (Next-generation Non-linear and Collapse Prediction Models for Sh ort- To Long-period Systems Via Machine Learning Methods)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news originating from Pavia, Italy, by NewsRx correspondents, research stated, "Since the 1960 s, a cornerstone of e arthquake engineering has been estimating the non-linear response of structures based just on lateral strength, modal properties, and the anticipated seismic de mand. Over the years, several studies have quantified this empirical relationshi p and integrated it within seismic design and assessment methodologies." Our news journalists obtained a quote from the research from Scuola Universitari a Superiore IUSS Pavia, "These have been widely accepted for practical applicati on and adopted in building codes worldwide. While these models work reasonably w ell, there are still areas in which improvements can be made, especially concern ing their robust quantification of uncertainty. This is mainly due to the amount of data used to quantify these empirical relationships, the choice of functiona l forms during the fitting, the model fitting and testing process, and how the g round motion shaking intensity is characterised. This study tackles these issues via the non-linear analysis of single-degree-of-freedom oscillators to train se veral machine learning (ML) models. This was to examine the accuracy and applica bility of such models within a seismic engineering context and explore potential gains in quantifying the non-linear response of structures via next-generation intensity measures, namely average spectral acceleration, Saavg. The results sho w that the Decision Tree and XGBoost models worked well across a broad range of periods, accurately predicting collapse and non-collapse responses. Appraising t hese with existing models showed a notable improvement all around. It indicates that the models based on data-driven ML approaches represent a positive step and can be seamlessly integrated with seismic analysis methodologies utilised world wide."
NETL
NSTL
