Root-mean-square distance Drms with characteristic of weighted-average is introduced in this article firstly. Drms can be used to capture the general proximity of a site to a dipping fault plane comparing with the rup...Root-mean-square distance Drms with characteristic of weighted-average is introduced in this article firstly. Drms can be used to capture the general proximity of a site to a dipping fault plane comparing with the rupture distance Drup and the seismogenic distance Dseis. Then, using Drup, Dseis and Drms, the hanging wall/footwall effects on the peak ground acceleration (PGA) during the 1999 Chi-Chi earthquake are evaluated by regression analysis. The logarithm residual shows that the PGA on hanging wall is much greater than that on footwall at the same Drup or Dseis when the Drup or Dseis is used as site-to-source distance measure. In contrast, there is no significant difference between the PGA on hanging wall and that on footwall at the same Drms when Drms is used. This result confirms that the hanging wall/footwall effect is mainly a geometric effect caused by the asymmetry of dipping fault. Therefore, the hanging wall/footwall effect on the near-fault ground motions can be ignored in the future attenuation analysis if the root-mean-square distance Drms is used as the site-to-source distance measure.展开更多
This study explores the influence of infill patterns on machine acceleration prediction in the realm of three-dimensional(3D)printing,particularly focusing on extrusion technology.Our primary objective was to develop ...This study explores the influence of infill patterns on machine acceleration prediction in the realm of three-dimensional(3D)printing,particularly focusing on extrusion technology.Our primary objective was to develop a long short-term memory(LSTM)network capable of assessing this impact.We conducted an extensive analysis involving 12 distinct infill patterns,collecting time-series data to examine their effects on the acceleration of the printer’s bed.The LSTM network was trained using acceleration data from the adaptive cubic infill pattern,while the Archimedean chords infill pattern provided data for evaluating the network’s prediction accuracy.This involved utilizing offline time-series acceleration data as the training and testing datasets for the LSTM model.Specifically,the LSTM model was devised to predict the acceleration of a fused deposition modeling(FDM)printer using data from the adaptive cubic infill pattern.Rigorous testing yielded a root mean square error(RMSE)of 0.007144,reflecting the model’s precision.Further refinement and testing of the LSTM model were conducted using acceleration data from the Archimedean chords infill pattern,resulting in an RMSE of 0.007328.Notably,the developed LSTM model demonstrated superior performance compared to an optimized recurrent neural network(RNN)in predicting machine acceleration data.The empirical findings highlight that the adaptive cubic infill pattern considerably influences the dimensional accuracy of parts printed using FDM technology.展开更多
基金Basic Science Research Foundation of Institute of Engineering Mechanics, China Earthquake Administration (2006B07) Natural Science Foundation of Heilongjiang Province (E2007-13)Joint Seismological Science Foundation of China (C07025)
文摘Root-mean-square distance Drms with characteristic of weighted-average is introduced in this article firstly. Drms can be used to capture the general proximity of a site to a dipping fault plane comparing with the rupture distance Drup and the seismogenic distance Dseis. Then, using Drup, Dseis and Drms, the hanging wall/footwall effects on the peak ground acceleration (PGA) during the 1999 Chi-Chi earthquake are evaluated by regression analysis. The logarithm residual shows that the PGA on hanging wall is much greater than that on footwall at the same Drup or Dseis when the Drup or Dseis is used as site-to-source distance measure. In contrast, there is no significant difference between the PGA on hanging wall and that on footwall at the same Drms when Drms is used. This result confirms that the hanging wall/footwall effect is mainly a geometric effect caused by the asymmetry of dipping fault. Therefore, the hanging wall/footwall effect on the near-fault ground motions can be ignored in the future attenuation analysis if the root-mean-square distance Drms is used as the site-to-source distance measure.
文摘This study explores the influence of infill patterns on machine acceleration prediction in the realm of three-dimensional(3D)printing,particularly focusing on extrusion technology.Our primary objective was to develop a long short-term memory(LSTM)network capable of assessing this impact.We conducted an extensive analysis involving 12 distinct infill patterns,collecting time-series data to examine their effects on the acceleration of the printer’s bed.The LSTM network was trained using acceleration data from the adaptive cubic infill pattern,while the Archimedean chords infill pattern provided data for evaluating the network’s prediction accuracy.This involved utilizing offline time-series acceleration data as the training and testing datasets for the LSTM model.Specifically,the LSTM model was devised to predict the acceleration of a fused deposition modeling(FDM)printer using data from the adaptive cubic infill pattern.Rigorous testing yielded a root mean square error(RMSE)of 0.007144,reflecting the model’s precision.Further refinement and testing of the LSTM model were conducted using acceleration data from the Archimedean chords infill pattern,resulting in an RMSE of 0.007328.Notably,the developed LSTM model demonstrated superior performance compared to an optimized recurrent neural network(RNN)in predicting machine acceleration data.The empirical findings highlight that the adaptive cubic infill pattern considerably influences the dimensional accuracy of parts printed using FDM technology.
