Structural Health Monitoring with model based damage detection using nonlinear model adaption and Artificial Intelligence methods

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The evaluation of the condition of existing structures usually comprises visual inspections and assessments by engineers as well as supplemented local test procedures if damage is suspected. This involves a high dependency on the expert’s knowledge and judgement. In contrast, automated monitoring systems enable a continuous objective condition recording and thus an early detection and evaluation of damage as well as a continuous documentation of the aging process. On that basis, a purposive condition-related maintenance is facilitated instead of a conventional cost-intensive servicing strategy. Comprehensive concepts for an automated permanent monitoring provide an identification of loads and structural conditions and thus enable a condition prognosis. Particularly for highly stressed solid structures, significant challenges have to be met. For this purpose, the use of artificial intelligence methods in a model-based permanent monitoring is a promising approach. Aim of this research project is the development of a closed approach for an automated damage diagnosis as part of a continuous condition monitoring of open-air massive constructions. The approach comprises the identification of systems and loads based on a realistic adaptation of numerical calculation models on statically damage-sensitive measurement quantities. On that basis, objective statements on the building condition are allowed that include reliable information about the location and the extent of detected damages. For model adaptation, an optimization method is applied that is based on the use of nonlinear calculations combined with discrete structurally characteristic values. Identified systems represent the structural condition at the respective measurement time. The damage diagnosis is based on the comparison of identified systems at different measurement times: Changes of the structural characteristic values enable the determination of damages together with the location and extent. For solving the highly complex optimization tasks, evolutionary algorithms are applied; Cluster analysis methods are used to evaluate the reliability of the optimization task solutions. The elaborated methods are prototypically implemented for the assessment of bridge structures. Bridges are highly important infrastructural objects that are exposed to steadily increasing traffic load demands. Additionally, they represent a high investment volume and frequently exhibit damages due to their age composition. Model-based investigations are intended to verify the performance capacity of the approach. Through conjunction of innovative information science techniques and modern structural analysis methods as well as their assignment to realistic engineering problems, novel solution approaches are rendered possible for efficient structural monitoring systems and efficient maintenance.