首页|Researchers at Bauhaus-Universitat Weimar Release New Study Findings on Machine Learning (Utilizing advanced machine learning approaches to assess the seismic f ragility of non-engineered masonry structures)
Researchers at Bauhaus-Universitat Weimar Release New Study Findings on Machine Learning (Utilizing advanced machine learning approaches to assess the seismic f ragility of non-engineered masonry structures)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news originating from Weimar, Germany, by NewsRx editors, the research stated, "Seismic fragility assessment provides a su bstantial tool for assessing the seismic resilience of these buildings. However, using traditional numerical methods to derive fragility curves poses significan t challenges." Funders for this research include Deutsche Forschungsgemeinschaft; Bauhaus-unive rsitat Weimar. Our news editors obtained a quote from the research from Bauhaus-Universitat Wei mar: "These methods often overlook the diverse range of buildings found in diffe rent regions, as they rely on standardized assumptions and parameters. Consequen tly, they may not accurately capture the seismic response of various building ty pes. Alternatively, extensive data collection becomes essential to address this knowledge gap by understanding local construction techniques and identifying the relevant parameters. This data is crucial for developing reliable analytical ap proaches that can accurately derive fragility curves. To overcome these challeng es, this research employs four Machine Learning (ML) techniques, namely Support Vector Regression (SVR), Stochastic Gradient Descent (SGD), Random Forest (RF), and Linear Regression (LR), to derive fragility curves for probability of collap se in terms of Peak Ground Acceleration (PGA). To achieve the research objective, a comprehensive input/output dataset consisting of on-site data collected from 646 masonry walls in Malawi is used. Adopted ML models are trained and tested u sing the entire dataset and then again using only the most highly correlated fea tures. The study includes a comparative analysis of the efficiency and accuracy of each ML approach and the influence of the data used in the analyses. Random F orest (RF) technique emerges as the most efficient ML approach for deriving frag ility curves for the surveyed dataset in terms of achieved lowest values for eva luation metrics of the ML methods."
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