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Potential mapping: reliability assessment and integration of the inspection data in degradation models

潜力映射：可靠性评估以及退化模型中检查数据的集成

基本信息

批准号：
274637702
负责人：
Professor Dr.-Ing. Christoph Gehlen
金额：
--
依托单位：
Centrum Baustoffe und Materialprüfung (cbm)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2015
资助国家：
德国
起止时间：
2014-12-31 至 2016-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/274637702?language=en
关键词：
Potential mapping reliability assessment integration

项目摘要

In order to enable mobility of our modern civilization the maintenance of the ageing infrastructure requires knowledge, experience and professionalism of the judging engineer. In addition use of inspection data is needed to support decision-making and to reduce the inspection effort.The objective of this research project is to support the engineer with a tool that enables a reliable and spatially differentiated identification of corroding areas in reinforced concrete structures. Nowadays, probabilistic models for calculating the service life of reinforced concrete structures including initiation and propagation period of reinforcement corrosion are available. These models solely provide localized information, in other words in a structure assessed the failure probability is assumed to be equal at any location. Thus, spatial variability of material resistances and exposure are ignored. With the help of potential mapping spatial differentiated information about corrosion condition in reinforced concrete structure is available. Up to now, this spatial information is not used and is therefore lost.The reliability of the inspection method has to be known to enable a probabilistic update of the service life prediction. First the main factors influencing potential fields and their correlation will be analyzed. Afterwards, the reliability of potential mapping will be expressed through the evaluation of the probability of detection (POD). Then, the spatial differentiated update of the service life prediction by potential mapping data can be performed. This evaluation algorithm will be validated on suitable existing reinforced concrete structures.Due to this algorithm a condition-based inspection and maintenance planning can be done for the actual first time. Inspection data and probabilistic service life prediction can be combined mathematically to give an accurate picture of the condition of investigated reinforced concrete structures.

为了使我们的现代文明能够流动，老化的基础设施的维护需要评判工程师的知识、经验和专业精神。此外，还需要使用检测数据来支持决策和减少检测工作。本研究项目的目标是为工程师提供一种工具，使其能够可靠地和空间上区分地识别钢筋混凝土结构中的腐蚀区域。目前已有计算钢筋混凝土结构使用寿命的概率模型，包括钢筋锈蚀的起始期和传播期。这些模型仅提供局部信息，换句话说，在评估的结构中，假定在任何位置的失效概率相等。因此，材料电阻和曝光量的空间变异性被忽略。利用电位图可以获得钢筋混凝土结构中腐蚀状态的空间分辨信息。到目前为止，这种空间信息没有被使用，因此丢失了。必须知道检查方法的可靠性，才能对寿命预测进行概率更新。首先分析了影响势场的主要因素及其相互关系。然后，通过检测概率(POD)的评估来表示潜在映射的可靠性。然后，可以通过潜在的映射数据执行使用寿命预测的空间差异化更新。该评估算法将在合适的既有钢筋混凝土结构上进行验证。利用该算法，可以首次进行基于状态的检测和维护计划。检测数据和概率使用寿命预测可以在数学上结合起来，以准确地反映被调查钢筋混凝土结构的状况。