Background and motivation
The potentials of “Smart City” concepts have been reflected in many sectors of society, and these concepts are continuously being implemented in civil engineering as means of addressing the rising challenges of urbanization and climate change. Smart city concepts encompass technical, environmental, economic, and social innovations, aiming to render cities more sustainable, environmentally friendly, and socially inclusive. Smart city concepts have increasingly been applied to digitalize civil infrastructure in the form of digital roads. Digital roads leverage cloud-based and IoT-based applications and utilize smart sensor networks with structural, environmental, vehicular, and traffic data, in an attempt to generate, analyze, and store information about civil infrastructure. The comprehensive information generated by digital roads is provided to authorities, companies, and citizens in real time to facilitate planning of infrastructure maintenance, to advance decision-making processes, and to improve the quality of life. However, the information cannot systematically be used in engineering applications because current modeling and design concepts do not meet the requirements of digital roads..
Objectives and expected results
The research project aims to provide a basis for fully utilizing information generated by digital roads. In addition, a methodology, tailored to digital roads, will be developed to record and to analyze stresses caused by traffic loads on civil infrastructure as time-dependent probability distributions, coupling sensor data and probabilistic concepts. The probability distributions of the stresses will be used for predicting material fatigue and deterioration. This research project is a follow-on project of the DFG project entitled “Semi-probabilistic, sensor-based design concepts for intelligent structural systems”. The methods developed in the previous project will be transferred to the field of digital roads. For this purpose, digital roads will mathematically be described, using categorical algebra, automata theory and spatio-temporal logic (Figure 1), linked with concepts of traffic load detection and sensor-based Bayesian model updating. Finally, the methodology will be validated, focusing on road bridges, and simulated as a “digital road twin”. It is expected that a scientifically sound foundation will be provided to improve predictions of structural deterioration and life time of digital roads and to offer the transportation sciences a valid set of methods that help meet the growing challenges associated with urbanization and climate change.
This research is a joint project collaboratively conducted by Professor Dr. Smarsly and Professor Dr. Kraus.
Professor Dr. Kay Smarsly
Hamburg University of Technology
Institute of Digital and Autonomous Construction
Professor Dr. Matthias Kraus
Bauhaus University Weimar
Chair of Steel Construction
Marienstr. 13 d