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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.

为了实现现代文明的流动性，老化基础设施的维护需要评审工程师的知识、经验和专业精神。此外，还需要使用检查数据来支持决策并减少检查工作。该研究项目的目标是为工程师提供一种工具，该工具能够可靠且在空间上差异化地识别钢筋混凝土结构中的腐蚀区域。如今，可以使用概率模型来计算钢筋混凝土结构的使用寿命，包括钢筋腐蚀的起始和扩展周期。这些模型仅提供局部信息，换句话说，在评估的结构中，假设任何位置的故障概率都是相等的。因此，材料电阻和暴露的空间变化被忽略。借助电位图，可以获得有关钢筋混凝土结构腐蚀状况的空间差异信息。到目前为止，该空间信息尚未使用，因此丢失。必须了解检查方法的可靠性，才能对使用寿命预测进行概率更新。首先分析影响位场的主要因素及其相关性。之后，潜在映射的可靠性将通过检测概率（POD）的评估来表达。然后，可以通过电位映射数据对使用寿命预测进行空间差异化更新。该评估算法将在合适的现有钢筋混凝土结构上进行验证。由于该算法，可以首次实际完成基于条件的检查和维护计划。检查数据和概率使用寿命预测可以通过数学方式结合起来，以准确地了解所研究的钢筋混凝土结构的状况。