7.2 Ingenieurbau
Filtern
Dokumenttyp
- Beitrag zu einem Tagungsband (16) (entfernen)
Referierte Publikation
- nein (16) (entfernen)
Schlagworte
- Structural Health Monitoring (4)
- Structural health monitoring (3)
- Offshore (2)
- Repair (2)
- Analysis of variance (1)
- Artificial intelligence (1)
- BIM (1)
- Bayesian System Identification (1)
- Bayesian methods (1)
- Bayesian updating (1)
Organisationseinheit der BAM
In this contribution, we consider two applications in which probabilistic approaches can potentially complement or enhance the design and assessment of offshore wind turbine foundations. First, we illustrate in a numerical example that probabilistic modelling can be helpful in dealing with chang-es in turbine locations during the planning phase of an offshore wind farm. In this case, spatial probabilistic modelling of the ground conditions enables (a) an inference of the soil properties at the modified turbine location from field data collected at different locations across an offshore wind farm site and (b) an optimisation of further site investigations. Second, we discuss the uncer-tainties and risks associated with the installation of large diameter monopiles in soils with hetero-geneities such as strong layers and/or embedded boulders. Subsequently, we present a concept for modelling, understanding, and managing these risks based on a probabilistic model of the subgrade conditions, monopile, and subgrade-pile-interaction.
Grouting is a universal repair and strengthening technique, which is constantly used for structural remediation of concrete components, trenches, mine subsidence, dam joints, restoration of masonry structures, and geological stabilizations. Having an extremely small particle size of only few microns, ultrafine cements are ideal for grouting applications due to their superior permeability and compressive strength properties of the hardened cement paste compared to that of the less-expensive, but coarser ordinary Portland cements. Supplementary cementitious materials (SCMs) are often used to replace ultrafine cement in order to modify certain properties and to reduce costs. The aim of this experimental study is to investigate the effect of three supplementary materials: microsilica (MS), fly ash (FA), and metakaolin (MK) on the workability, and mechanical properties of an ultrafine cement based grout with a constant water-binder ratio and constant superplasticizer content. Maximum percentages of replacement with ultrafine cement were 6% by volume of cement for MS and 16% for FA, and MK. In general, results suggest that the workability is improved by addition of FA, whereas is reduced, when modified with MS and MK. The compressive strength of grout after cement replacement remains comparable to that of pure cement grout. However, there is a tendency of the MS to positively affect the compressive strength opposite to FA, whereas flexural strength is positively affected by FA. Based on the results, it is evident that grouts with Hägerman cone flow more than 500 mm and compressive strength of more than 90 MPa after 28 days can be produced.
Remediation of Cracks Formed in Grouted Connections of Offshore Energy Structures under Static Loads
(2018)
The future energy demand necessitates the exploration of all potential energy sources both onshore and offshore. Global trend has shifted towards offshore energy, which can be obtained from either carbon intensive or renewable options, hence requiring structures such as rigs, platforms, and monopiles. Most of these structures adopt easily installable construction techniques, where lower foundation need to be connected with the super structure by mean of grouted composite joints. Generally, these composite connections have exterior sleeve, interior pile and infill grout. Being located in remote offshore conditions, connections can experience considerable adverse loading during their lifetimes. Degradations were reported inside similar connections, which were installed in last three decades. Besides, grouting in the offshore sites may often be proven difficult, which eventually leads to reduced capacity of connections in the long run. Thus, repair and rehabilitation of such connections should be planned ahead to minimize operational delays and costs in the future. This study aims at characterizing the nature of crack generation in grouted connections and thereby identifying the potential of repair using suitable repair material. Scaled grouted joints were manufactured using a novel mold, and connections were loaded under static load to visualize the main failure pattern. The failure mechanism and loading capacity are found compatible to previous results from earlier literature. Grouted connection was then repaired using cementitious injectable grout. The effectiveness of the repair system is also discussed.
Structural health monitoring (SHM) intends to improve the management of engineering structures. The number of successful SHM projects – especially SHM research projects – is ever growing, yielding added value and more scientific insight into the management of infrastructure asset. With the advent of the data age, the value of accessible data becomes increasingly evident. In SHM, many new data-centric methods are currently being developed at a high pace. A consequent application of research data management (RDM) concepts in SHM projects enables a systematic management of raw and processed data, and thus facilitates the development and application of artificial intelligence (AI) and machine learning (ML) methods to the SHM data. In this contribution, a case study based on an institutional RDM framework is presented. Data and metadata from monitoring the structural health of the Maintalbrücke Gemünden for a period of 16 months are managed with the RDM system BAM Data Store, which makes use of the openBIS data management software. An ML procedure is used to classify the data. Feature engineering, feature training and resulting data are performed and modelled in the RDM system.
Gradual or sudden changes in the state of structural systems caused, for example, by deterioration or accidental load events can influence their load-bearing capacity. Structural changes can be inferred from static and/or dynamic response data measured by structural health monitoring systems. However, they may be masked by variations in the structural response due to varying environmental conditions. Particularly, the interaction of nominally load-bearing components with nominally non-load bearing components exhibiting characteristics that vary as a function of the environmental conditions can significantly affect the monitored structural response. Ignoring these effects may hamper an inference of structural changes from the monitoring data. To address this issue, we adopt a probabilistic model-based framework as a basis for developing digital twins of structural systems that enable a prediction of the structural behavior under varying ambient condition. Within this framework, different types of data obtained from real the structural system can be applied to update the digital twin of the structural system using Bayesian methods and thus enhance predictions of the structural behavior. In this contribution, we implement the framework to develop a digital twin of a simply supported steel beam with an asphalt layer. It is formulated such that it can predict the static response of the beam in function of its temperature. In a climate chamber, the beam was subject to varying temperatures and its static response wass monitored. In addition, tests are performed to determine the temperature-dependent properties of the asphalt material. Bayesian system identification is applied to enhance the predictive capabilities of the digital twin based on the observed data.
Inspection and maintenance contribute significantly to the lifetime cost of bridges. There is significant potential in using information obtained through structural health monitoring to update predictive models of the condition and performance of such structures, and thus ena-ble an improved decision-making regarding inspection and maintenance activities. Within the AISTEC project funded by the German Federal Ministry of Education and Research, we develop vibration-based structural health monitoring systems aimed at continuously provid-ing information on the structural condition of bridges. Environmental variations such as changing ambient temperatures can significantly influence the dynamic characteristics of bridges and thus mask the effect of structural changes and damages. It remains a challenge to account for such influences in structural health monitoring. To study the effect of ambient temperatures on the dynamic characteristics of beam structures, we monitor the vibration response of a reinforced concrete beam in the uncracked and cracked state at varying tem-peratures in a climate chamber. We postulate a set of competing parameterized probabilistic structural models, which explicitly account for the effect of varying ambient temperatures on the mechanical properties of the system. We then combine the information provided by the structural models with the information contained in the recorded vibration data to learn the parameters of the temperature-dependent structural models and infer the plausible state of the beam using Bayesian system identification and model class selection.