Sensor-based Hybrid Partial Models for Decentralized Condition Assessment of Civil Engineering Structures

Goal of this research is the investigation of hybrid partial models in civil engineering. Here, the term "hybrid" describes partial models that are located in different domains and on different scales, i.e. both in software systems and in hardware components, such as measuring devices or wireless sensor nodes. A major objective is to embed mathematical partial models into wireless sensor nodes in order to enable an automated assessment of the structural conditions of civil infrastructure, i.e. the structural assessment will technically be done directly at the sensor level through intelligently cooperating, autonomous sensor nodes. In addition to the integration of mathematical partial models, it is envisaged to integrate numerical models into the sensor nodes, which still is an open problem in the field of microcontroller and sensor technologies due to the multi-scale and multi-dimensional nature of the problem. As a result of this research, it is expected to gain new insights into sensor-based embedded computing strategies applied for structural health monitoring of civil infrastructure. Also, the quality of the partial models will be assessed. For this research project, existing measurement data can be used collected from different reference structures within previous and existing projects of the Research Training Group.

Project type
German Research Foundation (DFG): Research Training Group (GRK 1462)

2014 - 2017

Project-related publications (selection)

  • Dragos, K. & Smarsly, K., 2017. Decentralized infrastructure health monitoring using embedded computing in wireless sensor networks. In: Sextos, A. & Manolis, G. D. (eds.). Dynamic Response of Infrastructure to Environmentally Induced Loads. Pp. 183-201. Cham, Switzerland: Springer International Publishing AG.

  • Dragos, K. & Smarsly, K., 2017. An embedded algorithm for detecting and accommodating synchronization problems in wireless structural health monitoring systems. In: Proceedings of the 24th International Workshop on Intelligent Computing in Engineering (EG-ICE). Nottingham, UK, 07/10/2017 (submitted).

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

Dipl.-Ing. Kosmas Dragos, M.Sc.
Bauhaus University Weimar
Research Training Group 1462
Coudraystraße 13 a, Room 308
99423 Weimar
Email: kosmas.dragos[at]