Semi-probabilistic, sensor-based design concepts for intelligent structural systems

Engineering structures are increasingly equipped with structural health monitoring systems. From the sensor data obtained, valuable sensor-based information is available during the whole life-cycle of a structure. Continuously updated in real time, sensor-based information can be communicated online and coupled with information from further sources to derive knowledge about the structure (Industry 4.0, Semantic Web, Internet of Things, "Intelligent Bridge"). Both, that is engineering structure and intelligent structural health monitoring system, are considered as one unit, referred to as "intelligent structural system". However, current design standards have not kept pace with the rapid developments in intelligent sensing technologies: Sensor-based information provided by structural health monitoring systems is not considered in current structural design concepts. For example, Eurocodes and corresponding partial safety factor concepts are adapted from the assumption that no sensor-based information is available throughout the whole life-cycle of a structure; rather, a hypothetical, unknown structural state is taken as a basis for the design. This state has to reflect different influences and associated uncertainties the structure is exposed to during its life time. This assumption may result in oversizing and additional costs as well as in rehabilitation and repair works that cannot be planned optimally.

The goal of this research project is twofold:

  • First, the implications of integrating sensor-based information into structural design concepts are to be investigated and design concepts particularly for intelligent structural systems are to be proposed.
  • Second, based on the first goal, a general development strategy for structural health monitoring systems for intelligent structural systems is to be developed.

To achieve these goals, the proposed research addresses the following key issues. Upon analyzing current design concepts, a semi-probabilistic safety concept will be developed for intelligent structural systems, taking into account the availability of additional sensor-based information on the structural state. A major part of this work is devoted to investigating to which extent loads and resistances (as well as their correlation) can be captured by sensors and how they should formally be represented. Clearly, when incorporating additional sensor-based information into structural design concepts, uncertainties with respect to the actual state of a structure can significantly be reduced. However, new uncertainties arise, which are caused by the hardware and software components of the structural health monitoring system installed on the intelligent structural systems (e.g. sensor failures, miscalibrations, and interrupted data lines). These additional uncertainties will be addressed and integrated into the safety concept to be proposed as an outcome of this research project.

Project type
German Research Foundation (DFG): Research grant
Principal investigators: Prof. Smarsly, Prof. Kraus

2018 - 2021

Project-related publications (selection)

  • Ibanez, S. & Dragos, K., 2018. Quality indicators for embedded stochastic subspace identification algorithms in wireless structural health monitoring systems. In: Proceedings of the 30th Forum Bauinformatik. Weimar, 09/19/2018.

  • Legatiuk, D. & Smarsly, K., 2018. An abstract approach towards modeling intelligent structural systems. In: Proceedings of the 9th European Workshop on Structural Health Monitoring (EWSHM) 2018. Manchester, UK, 07/10/2018.

  • Ibanez, S., Fitz, T. & Smarsly, K., 2019. A semantic model for wireless sensor networks in cognitive buildings. In: Proceedings of the International Conference on Computing in Civil Engineering (I3CE) 2019. Atlanta, GA, USA, 06/17/2019.

  • Gürlebeck, K., Legatiuk, D., Nilsson, H. & Smarsly, K., 2019. Conceptual modelling: Towards detecting modelling errors in engineering applications. Mathematical Methods in the Applied Sciences.

Professor Dr. Kay Smarsly
Bauhaus University Weimar
Computing in Civil Engineering
Coudraystraße 13 b, Room 004
99423 Weimar
E-Mail: kay.smarsly[at]