A Bayesian framework is presented for finding the optimal locations of strain sensors in a plate with a crack with the goal of identifying the crack properties, such as crack location, size, and orientation. Sensor grids of different type and size are considered. The Bayesian optimal sensor placement framework is rooted in information theory, and the optimal grid is the one which maximizes the expected information gain (Kullback-Liebler divergence) between the prior and posterior probability density functions of the crack parameters. The uncertainty in the crack parameters is accounted for naturally within the Bayesian framework through the prior probability density functions. The framework is demonstrated for a thin plate with crack, subjected to static loading. A finite element model is used to simulate the strain distributions in the plate given the crack properties. To verify the effectiveness of the proposed optimal sensor placement methodology, the estimated optimal sensor grids are used to perform Bayesian crack identification using simulated data. Parametric analyses are carried out giving emphasis on the effect of the number of sensors, grid type, and experimental data noise levels in the identification results. KEYWORDSbayesian inference, crack identification, information gain, KL-divergence, optimal sensor placement Struct Control Health Monit. 2018;25:e2137.wileyonlinelibrary.com/journal/stc

No abstract

Der Entwurfsprozess von Stahlkonstruktionen erfolgt unter Berücksichtigung verschiedener probabilistischer Annahmen, wie bspw. für die zu erwartenden Belastungen, das Materialverhalten oder die Strukturgeometrie. Der Bemessung liegt ein Sicherheitskonzept zugrunde, das sich auf die Formulierung einer Versagenswahrscheinlichkeit stützt. Dabei müssen die verschiedenen im Bereich des Bauwesens auftretenden Unsicherheiten sachgerecht erfasst und berücksichtigt werden [1], um im Ergebnis eine robuste Konstruktion zu gewährleisten und den gewünschten Sicherheitsanforderungen im Lebenszyklus des Bauwerks zu genügen. Gemäß DIN EN 1990 [2] ist ein Tragwerk so zu planen und auszuführen, dass es während der Errichtung und in der vorgesehenen Nutzungsdauer mit angemessener Zuverlässigkeit und Wirtschaftlichkeit den möglichen Einwirkungen und Einflüssen standhält und die geforderten Anforderungen an die Gebrauchstauglichkeit erfüllt.

Structural systems are in general quite sophisticated in terms of interactions between individual members with respect to their stressing and resistance. In addition, the stochastic nature of different loads and resistances, respectively, is quite different. A consistent statistical representation of the random variables for a structural component/member is therefore quite complex. In this context, the implementation of intelligent structures and the incorporation of the measuring data can help to improve the prediction of random variables for a certain member and therefore to update models in the design process. Depending on the obtained measurement data, the updating can either refer to the statistical representation quality of different values, but also to the quantity of random variables or even to the update of the mechanical model and its boundary conditions. For that reason, a classification of updating possibilities in the context of the design process described by different hierarchical levels is useful. The aim of this article is to give an overview and insight on corresponding concepts of sensor‐based design strategies for steel structures. In addition, first research results regarding the effect of measurement‐based updating on the quality improvement of the design process are presented.

Structural systems are in general quite sophisticated in terms of interactions between individual members with respect to their stressing and resistance. In addition, the stochastic nature of different loads and resistances, respectively, is quite different. A consistent statistical representation of the random variables for a structural component/member is therefore quite complex. In this context, the implementation of intelligent structures and the incorporation of the measuring data can help to improve the prediction of random variables for a certain member and therefore to update models in the design process. Depending on the obtained measurement data, the updating can either refer to the statistical representation quality of different values, but also to the quantity of random variables or even to the update of the mechanical model and its boundary conditions. For that reason, a classification of updating possibilities in the context of the design process described by different hierarchical levels is useful. The aim of this article is to give an overview and insight on corresponding concepts of sensor‐based design strategies for steel structures. In addition, first research results regarding the effect of measurement‐based updating on the quality improvement of the design process is presented.