TY - THES A1 - Viefhues, Eva T1 - Subspace-based damage detection in engineering structures considering reference uncertainties and temperature effects N2 - Automated vibration-based damage detection is of increasing interest for structural health monitoring of engineering structures. In this context, stochastic subspace-based damage detection (SSDD) compares measurements from a testing state to a data-driven reference model in a statistical framework. In this thesis theoretical developments have been proposed to improve the robustness of SSDD for realistic applications conditions. First, a statistical test has been proposed considering the statistical uncertainties about the model obtained from the reference data. This leads to a precise description of the test’s distribution properties and damage detection thresholds. Second, an approach has been developed to account for environmental effects in SSDD. Based on reference measurements at few different environmental conditions, a test is derived with respect to an adequate interpolated reference. The proposed methods are validated in numerical simulations and applied to experimental data from the laboratory and outdoor structures. KW - Damage detection KW - Subspace methods KW - Vibrations KW - Uncertainty quantification KW - Environmental effects KW - Civil structures PY - 2021 SP - 1 EP - 191 CY - Universite de Rennes AN - OPUS4-55774 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - THES A1 - Wiehle, Philipp T1 - Einfluss der Feuchtigkeit auf das Tragverhalten von Lehmmauerwerk N2 - Im Mittelpunkt der vorliegenden Arbeit steht der Einfluss der Feuchte auf die mechanischen Eigenschaften von Lehmmauerwerk. Der Wissensstand zum feuchteabhängigen Tragverhalten von Lehm(mauerwerk) ist bisher lückenhaft, sodass keine explizite Berücksichtigung der Bauteilfeuchte bei der Bemessung tragender Konstruktionen erfolgt. Aktuelle und verlässliche Daten zum Einfluss der Feuchte auf die mechanischen Kenngrößen moderner Lehmbaustoffe fehlen bisher ebenso wie Messwerte in Bezug auf die Bauteilfeuchte unter natürlichen Klimabedingungen. Deswegen wurden im Rahmen dieser Arbeit umfangreiche Untersuchungen zum mechanischen und hygrothermischen Verhalten von Lehmmauerwerk durchgeführt. Die experimentellen Untersuchungen bestehen im Wesentlichen aus Druckversuchen an Lehmsteinen, -mörteln, kleinformatigen Lehmmauerwerksprobekörpern und geschosshohen Lehmmauerwerkswänden. Um das Feuchteverhalten beschreiben zu können, fanden außerdem erstmalig magnetresonanzspektroskopische Messungen an Lehmsteinen statt und es wurden die tatsächlich auftretenden Feuchtegehalte an einer Lehmmauerwerkswand unter natürlichen Klimabedingungen in Form von Langezeitmessungen ermittelt. Es konnte festgestellt werden, dass ein linearer Zusammenhang zwischen Druckfestigkeit und relativer Luftfeuchte besteht, wobei sich die Druckfestigkeit umgekehrt proportional zur relativen Luftfeuchte verhält. Je Prozent Steigerung der relativen Luftfeuchte kommt es zur Abnahme von einem Prozent der Druckfestigkeit. Gleiches gilt für das Elastizitätsmodul. Weiterhin konnte auf Basis der feuchtetechnischen Untersuchungen ein numerisches Modell zur Berechnung des instationären hygrothermischen Verhaltens für Lehmbaustoffe kalibriert werden. Anhand dieses Modells gelang es die bemessungsrelevanten Feuchtegehalte unter Berücksichtigung des instationären hygrothermischen Verhaltens realitätsnah zu berechnen. Die maximalen Feuchtegehalte im Lehmmauerwerk konnten somit in Form einer Parameterstudie in Abhängigkeit des Anwendungsfalls ermittelt werden, wodurch eine explizite Berücksichtigung des Feuchtegehaltes bei der Bemessung ermöglicht wurde. Die Verknüpfung der Erkenntnisse aus den mechanischen und hygrothermischen Untersuchungen dieser Arbeit bildet die Grundlage für das Bemessungskonzept der im Juni 2023 veröffentlichten DIN 18940: Tagendes Lehmsteinmauerwerk. KW - NMR KW - Lehm KW - Mauerwerk KW - Druckfestigkeit KW - Feuchtigkeit PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-637794 DO - https://doi.org/10.14279/depositonce-20800 SP - 1 EP - 114 CY - Berlin AN - OPUS4-63779 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - THES A1 - Schneider, Ronald T1 - Time-variant reliability of deteriorating structural systems conditional on inspection and monitoring data N2 - The current practice of operating and maintaining deteriorating structural systems ensures acceptable levels of structural reliability, but it is not clear how efficient it is. Changing the current prescriptive approach to a risk-based approach has great potential to enable a more efficient management of such systems. Risk-based optimization of operation and maintenance strategies identifies the strategy that optimally balances the cost for controlling deterioration in a structural system with the achieved risk reduction. Inspections and monitoring are essential parts of operation and maintenance strategies. They are typically performed to reduce the uncertainty in the structural condition and inform decisions on future operation and maintenance actions. In risk-based optimization of operation and maintenance strategies, Bayesian updating is used to include information contained in inspection and monitoring data in the prediction of the structural reliability. All computations need to be repeated many times for different potential inspection and monitoring outcomes. This motivates the development of robust and efficient approaches to this computationally challenging task. The reliability of deteriorating structural systems is time-variant because the loads on them and their capacities change with time. In most practical applications, the reliability analysis of deteriorating structural systems can be approached by dividing their lifetime into discrete time intervals. The time-variant reliability problem can then be represented by a series of time-invariant reliability problems. Using this methodology as a starting point, this thesis proposes a novel approach to compute the time-variant reliability of deteriorating structural systems for which inspection and monitoring data are available. The problem is formulated in a nested way in which the prediction of the structural condition is separated from the computation of the structural reliability conditional on the structural condition. Information on the structural condition provided by inspections and monitoring is included in the reliability assessment through Bayesian updating of the system deterioration model employed to predict the structural condition. The updated system reliability is obtained by coupling the updated deterioration model with a probabilistic structural model utilized to calculate the failure probability conditional on the structural condition. This approach is the first main outcome of this thesis and termed nested reliability analysis (NRA) approach. It is demonstrated in two numerical examples considering inspected and monitored steel structures subject to high-cycle fatigue. An alternative – recently developed – approach, which also follows the strategy of discretizing time, describes deteriorating structural systems with hierarchical dynamic Bayesian networks (DBN). DBN combined with approximate or exact inference algorithms also enable the computation of the time-variant reliability of deteriorating structural systems conditional on information provided by inspection and monitoring data. In this thesis – as a proof of concept – a software prototype is developed based on the DBN approach, which can be used to assess the reliability of a corroding concrete box girder for which half-cell potential measurements are available. This is the second main outcome of this thesis. Both approaches presented in this thesis enable an integral reliability analysis of inspected and monitored structures that accounts for system effects arising from (a) the correlation among deterioration states of different structural elements, (b) the interaction between element deterioration and system failure, and (c) the indirect information gained on the condition of all unobserved structural elements from inspecting or monitoring the condition of some structural elements. Thus, both approaches enable a systemwide risk-based optimization of operation and maintenance strategies for deteriorating structural systems. The NRA approach can be implemented relatively easily with subset simulation, which is a sequential Monte Carlo method suitable for estimating rare event probabilities. Subset simulation is robust and considerably more efficient than crude Monte Carlo simulation. It is, however, still sampling-based and its efficiency is thus a function of the number of inspection and monitoring outcomes, as well as the value of the simulated event probabilities. The current implementation of the NRA approach performs separate subset simulation runs to estimate the reliability at different points in time. The efficiency of the NRA approach with subset simulation can be significantly improved by exploiting the fact that failure events in different years are nested. The lifetime reliability of deteriorating structural systems can thus be computed in reverse chronological order in a single subset simulation run. The implementation of the DBN approach is much more demanding than the implementation of the NRA approach but it has two main advantages. Firstly, the graphical format of the DBN facilitates the presentation of the model and the underlying assumptions to stakeholders who are not experts in reliability analysis. Secondly, it can be combined with exact inference algorithms. In this case, its efficiency neither depends on the number of inspection and monitoring outcomes, nor on the value of the event probabilities to be calculated. However, in contrast to the NRA approach with subset simulation, the DBN approach with exact inference imposes restrictions on the number of random variables and the dependence structure that can be implemented in the model. T3 - BAM Dissertationsreihe - 168 KW - Reliability KW - Structural systems KW - Deterioration KW - Bayesian analysis KW - Inspection KW - Monitoring KW - Zuverlässigkeit KW - Tragstrukturen KW - Schädigungsprozesse KW - Bayes'sche Analyse KW - Inspektion KW - Monitoring PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-512977 SN - 1613-4249 VL - 168 SP - 1 EP - 188 PB - Bundesanstalt für Materialforschung und -prüfung (BAM) CY - Berlin AN - OPUS4-51297 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -