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Energy saving ordinances requires that buildings must be designed in such a way that the heat transfer surface including the joints is permanently air impermeable. The prefabricated roof and wall panels in lightweight steel constructions are airtight in the area of the steel covering layers. The sealing of the panel joints contributes to fulfil the comprehensive requirements for an airtight building envelope. To improve the airtightness of steel sandwich panels, additional sealing tapes can be installed in the panel joint. The influence of these sealing tapes was evaluated by measurements carried out by the RWTH Aachen University - Sustainable Metal Building Envelopes. Different installation situations were evaluated by carrying out airtightness tests for different joint distances. In addition, the influence on the heat transfer coefficient was also evaluated using the Finite Element Method (FEM). The combination of obtained air volume flow and transmission losses enables to create an "effective heat transfer coefficient" due to transmission and infiltration. This summarizes both effects in one value and is particularly helpful for approximate calculations on energy efficiency.
As part of a novel approach to automatic sewer inspection, this paper presents a robust algorithm for automatic flow line detection. A large image repository is obtained from about 50,000 m sewers to represent the high variability of real world sewer systems. Automatic image processing combines Canny edge detection, Hough transform for straight lines and cost minimization using Dijkstra's shortest path algorithm. Assuming that flow lines are mostly smoothly connected horizontal structures, piecewise flow line delineation is reduced to a process of selecting adjacent line candidates. Costs are derived from the gap between adjacent candidates and their reliability. A single parameter α enables simple control of the algorithm. The detected flow line may precisely follow the segmented edges (α = 0.0) or minimize gaps at joints (α = 1.0). Both, manual and ground truth-based analysis indicate that α = 0.8 is optimal and independent of the sewer's material. The algorithm forms an essential step to further automation of sewer inspection.
Various models have been proposed for the prediction of the necessary support pressure at the face of a shallow tunnel. To assess their quality, the collapse of a tunnel face was modelled with small-scale model tests at single gravity. The development of the failure mechanism and the support force at the face in dry sand were investigated. The observed displacement patterns show a negligible influence of overburden on the extent and evolution of the failure zone. The latter is significantly influenced, though, by the initial density of the sand: in dense sand a chimney-wedge-type collapse mechanism developed, which propagated towards the soil surface. Initially, loose sand did not show any discrete collapse mechanism. The necessary support force was neither influenced by the overburden nor the initial density. A comparison with quantitative predictions by several theoretical models showed that the measured necessary support pressure is overestimated by most of the models. Those by Vermeer/Ruse and Léca/Dormieux showed the best agreement to the measurements.
Different analytical approaches exist to describe the structural substance or wear reserve of sewer systems. The aim is to convert engineering assessments of often complex defect patterns into computational algorithms and determine a substance class for a sewer section or manhole. This analytically determined information is essential for strategic rehabilitation planning processes up to network level, as it corresponds to the most appropriate rehabilitation type and can thus provide decision-making support. Current calculation methods differ clearly from each other in parts, so that substance classes determined by the different approaches are only partially comparable with each other. The objective of the German R&D cooperation project ‘SubKanS’ is to develop a methodology for classifying the specific defect patterns resulting from the interaction of all the individual defects, and their severities and locations. The methodology takes into account the structural substance of sewer sections and manholes, based on real data and theoretical considerations analogous to the condition classification of individual defects. The result is a catalogue of defect patterns and characteristics, as well as associated structural substance classifications of sewer systems (substance classes). The methodology for sewer system substance classification is developed so that the classification of individual defects can be transferred into a substance class of the sewer section or manhole, eventually taking into account further information (e.g. pipe material, nominal diameter, etc.). The result is a validated methodology for automated sewer system substance classification.