Our cultural and natural heritage are irreplaceable sources of life and inspiration. Caves and, in general, hypogean environments, belongs to the geologic heritage as they consists of geologic features and landforms which are part of our world and from which we may benefit during our lifetime, but which we also have the responsibility to preserve undiminished in quality and quantity. Such sites generally have great potential for scientific studies, use as outdoor classrooms, enhancing public understanding and enjoyment. Hypogean environments are fundamental among other things, to understanding surface processes, climatic changes over time, evolution of landforms, the origin of mineral deposits, and the hydrogeology of an area, rather then be preserved for their undoubt beauty and as they host, or have hosted in the past, sites of religious and social importance. To solve the problems related to traditional analyses, which can be in various ways expensive and difficult, the implementation of new digitalized methods, such as close and long range remote sensing techniques, has become essential to quantitatively describe the structural arrangement of rock masses in such sites over the last two decades. In recent years, the research has been focused mostly on the implementation of new algorithms and methods that take into account the needs related to geology and, in general, to the recognition of geometric shapes starting from three-dimensional data. For this reason, new techniques have been developed to standardize processes of recognizing, evaluating and, finally, extracting primitive geometries, such as planes and volumetric shapes that represent blocks and discontinuities. This implies the necessity of having robust and reliable methods to determine such features on a rock outcrop. Moreover these methods have to be easy to use, fast and accurate. This leads today to the tendency of developing automated methods, which often have limitations as concerns processing time, definition of parameters and, especially, accuracy. We present here an approach that takes into account both the necessity of downsampling a 3D point-cloud to speed up computing processing and the necessity of a supervising stage, so that the various step throughout the recognition and, most important, the extraction of sets, is not totally carried out by the machine, but take advantage of case sensitive observations made in situ. Finally, many characteristics useful for evaluating the stability of rock masses in a wide range of geological environments can therefore be evaluated. These characteristics, together with the analysis of possible kinematics, information on the hydrogeology of the study area and the analysis of the blocks, contribute to the geo-structural and geomechanical characterization of the rock mass in such important sites.
New stability evaluation methods based on discontinuity sets recognition from 3D point clouds aimed at the protection of underground sites
CARDIA S.;PARISE M.
2022-01-01
Abstract
Our cultural and natural heritage are irreplaceable sources of life and inspiration. Caves and, in general, hypogean environments, belongs to the geologic heritage as they consists of geologic features and landforms which are part of our world and from which we may benefit during our lifetime, but which we also have the responsibility to preserve undiminished in quality and quantity. Such sites generally have great potential for scientific studies, use as outdoor classrooms, enhancing public understanding and enjoyment. Hypogean environments are fundamental among other things, to understanding surface processes, climatic changes over time, evolution of landforms, the origin of mineral deposits, and the hydrogeology of an area, rather then be preserved for their undoubt beauty and as they host, or have hosted in the past, sites of religious and social importance. To solve the problems related to traditional analyses, which can be in various ways expensive and difficult, the implementation of new digitalized methods, such as close and long range remote sensing techniques, has become essential to quantitatively describe the structural arrangement of rock masses in such sites over the last two decades. In recent years, the research has been focused mostly on the implementation of new algorithms and methods that take into account the needs related to geology and, in general, to the recognition of geometric shapes starting from three-dimensional data. For this reason, new techniques have been developed to standardize processes of recognizing, evaluating and, finally, extracting primitive geometries, such as planes and volumetric shapes that represent blocks and discontinuities. This implies the necessity of having robust and reliable methods to determine such features on a rock outcrop. Moreover these methods have to be easy to use, fast and accurate. This leads today to the tendency of developing automated methods, which often have limitations as concerns processing time, definition of parameters and, especially, accuracy. We present here an approach that takes into account both the necessity of downsampling a 3D point-cloud to speed up computing processing and the necessity of a supervising stage, so that the various step throughout the recognition and, most important, the extraction of sets, is not totally carried out by the machine, but take advantage of case sensitive observations made in situ. Finally, many characteristics useful for evaluating the stability of rock masses in a wide range of geological environments can therefore be evaluated. These characteristics, together with the analysis of possible kinematics, information on the hydrogeology of the study area and the analysis of the blocks, contribute to the geo-structural and geomechanical characterization of the rock mass in such important sites.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
