Alluvial fans, valley fills and debris cones represent economically significant, natural aggregate resources in the alpine regions of Austria. Due to continual erosion they constitute renewable sources of sand and gravel. However, knowledge of their petrographic properties and resulting aggregate quality is scant. An automated evaluation method was developed to estimate petrographic characteristics and predict resource suitability. This method makes use of the fact that the properties of a gravel deposit depend on the morphology and geology of the provenance area. Area percentages of geological units in the source area were expected to mirror the litho-spectrum of the gravel resource. Petrographic analysis of 13 field samples shows that this is indeed the case. Discrepancies amount to 0–10%; larger deviations occur for grain size fractions <2 mm in the presence of soft rock types such as mica-schist or paragneiss. Forecasts of aggregate quality on the basis of lithological composition and grain size characteristics were compared to operational data of five gravel pits. The actual usage of the material agrees with predictions in four out of the five cases.
<p>Austrian loess and loess loam deposits represent an important source of raw materials for the heavy clay industry for centuries. Building material quality of loess and loess loam deposits and their suitability for different applications is significantly influenced by their heterogeneous properties. These depend on the geology of the source area, climatic conditions, geomorphological location, stratigraphic position, intensity of weathering and redeposition potential. The description of occurrences, properties and availability of these raw materials is therefore an important prerequisite to meet the industrial quality requirements. A large number of different sub-datasets exist at the Geological Survey of Austria, which comprise grain-size analysis, bulk rock composition, clay mineralogy, and geochemistry data of loess and loess loam. Within our project, these individual data sets underwent a thorough examination and have been merged into a coherent database to enable the joint regional and statistical analysis of the data. By applying a log-ratio approach the compositional nature of the analysis data has been taken into account for multivariate statistical methods.&#160;<br>Within our study we focused on the classic Austrian loess regions in the Northern Alpine foreland areas of Upper and Lower Austria and in the Vienna Basin. By transferring the results of the statistical analysis to a Geographic Information System (GIS) these served as the fundamental basis for our categorization of the loess and loess loam occurrences. Taking into account previously published approaches based on soil profile classifications as well as trends and patterns derived from the analysis data, we finally were able to delineate different districts of brick raw materials deposits. These will be made publically accessible to the industry and interested parties as part of the web application of the Austrian Interactive Raw Material Information System IRIS-Online.</p>
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