This research focuses on the seismic responses of the historic masonry minarets,conducted through the creation of a digital twin model using finite element methods.The study initiated the development of a comprehensiv...This research focuses on the seismic responses of the historic masonry minarets,conducted through the creation of a digital twin model using finite element methods.The study initiated the development of a comprehensive model in the ANSYS Workbench,supplemented by operational modal analysis(OMA),to ascertain the dynamic characteristics of the minaret.The alignment of numerical and experimental frequency data was achieved using the response surface method(RSM)within ANSYS Workbench DesignXplorer.This process resulted in the establishment of a digital twin,accurately representing the physical minaret in a virtual environment.Blender^(■)software was then used to simulate the effects of two consecutive earthquakes in Türkiye that occurred on February 6,2023.The simulations highlighted the heightened susceptibility of the minaret,especially in its upper sections,to consecutive seismic activities,culminating in significant damage and collapse.This innovative approach,merging traditional engineering methods with a cutting-edge digital simulation,provides a profound insight into the seismic behavior of historical structures.The research underscores the importance of advanced seismic modeling for the effective preservation and resilience of architectural heritage sites against earthquake risks.展开更多
The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor...The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor-intensive and require significant expertise,often complicated by the coexistence of other minerals.This study presents a novel approach leveraging deep learning techniques combined with hyperspectral imaging to automate the identification process of quartz minerals.The utilizied four advanced deep learning models—PSPNet,U-Net,FPN,and LinkNet—has significant advancements in efficiency and accuracy.Among these models,PSPNet exhibited superior performance,achieving the highest intersection over union(IoU)scores and demonstrating exceptional reliability in segmenting quartz minerals,even in complex scenarios.The study involved a comprehensive dataset of 120 thin sections,encompassing 2470 hyperspectral images prepared from 20 rock samples.Expert-reviewed masks were used for model training,ensuring robust segmentation results.This automated approach not only expedites the recognition process but also enhances reliability,providing a valuable tool for geologists and advancing the field of mineralogical analysis.展开更多
基金product of the research project titled,Video Camera Based Structural Health Monitoring of Historic Masonry Minarets and Development of a Long-Term Decision-Making Model Based on Deep Learning Method(Project No.222M140)supported by TÜBİTAK 1001-Scientific and the Technological Research Projects Support Program.
文摘This research focuses on the seismic responses of the historic masonry minarets,conducted through the creation of a digital twin model using finite element methods.The study initiated the development of a comprehensive model in the ANSYS Workbench,supplemented by operational modal analysis(OMA),to ascertain the dynamic characteristics of the minaret.The alignment of numerical and experimental frequency data was achieved using the response surface method(RSM)within ANSYS Workbench DesignXplorer.This process resulted in the establishment of a digital twin,accurately representing the physical minaret in a virtual environment.Blender^(■)software was then used to simulate the effects of two consecutive earthquakes in Türkiye that occurred on February 6,2023.The simulations highlighted the heightened susceptibility of the minaret,especially in its upper sections,to consecutive seismic activities,culminating in significant damage and collapse.This innovative approach,merging traditional engineering methods with a cutting-edge digital simulation,provides a profound insight into the seismic behavior of historical structures.The research underscores the importance of advanced seismic modeling for the effective preservation and resilience of architectural heritage sites against earthquake risks.
文摘The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor-intensive and require significant expertise,often complicated by the coexistence of other minerals.This study presents a novel approach leveraging deep learning techniques combined with hyperspectral imaging to automate the identification process of quartz minerals.The utilizied four advanced deep learning models—PSPNet,U-Net,FPN,and LinkNet—has significant advancements in efficiency and accuracy.Among these models,PSPNet exhibited superior performance,achieving the highest intersection over union(IoU)scores and demonstrating exceptional reliability in segmenting quartz minerals,even in complex scenarios.The study involved a comprehensive dataset of 120 thin sections,encompassing 2470 hyperspectral images prepared from 20 rock samples.Expert-reviewed masks were used for model training,ensuring robust segmentation results.This automated approach not only expedites the recognition process but also enhances reliability,providing a valuable tool for geologists and advancing the field of mineralogical analysis.
