Abstract
Road and motorway traffic has increased dramatically in Europe within the last decades. Apart from a disproportionate enlargement of the total number of heavy goods vehicles, overloaded vehicles are observed frequently. The knowledge about actual traffic loads including gross vehicle weights and axle loads as well as their probability of occurrence is of particular concern for authorities to ensure durability and security of the road network’s structures.
The paper presents in detail an evolutionary algorithm based data mining approach to determine gross vehicle weights and vehicle velocities from bridge measurement data. The analysis of huge amounts of data is performed in time steps by considering data of a corresponding time interval. For every time interval a population of vehicle combinations is optimized. Within this optimization process knowledge gained in the preceding time interval is incorporated. In this way, continuously measured data can be analyzed and an adequate accuracy of approximation is achieved. Single vehicles are identified in measured data, which may result from one or multiple vehicles on the bridge at a given point of time.
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Lubasch, P., Schnellenbach-Held, M., Freischlad, M., Buschmeyer, W. (2006). Knowledge Discovery in Bridge Monitoring Data: A Soft Computing Approach. In: Smith, I.F.C. (eds) Intelligent Computing in Engineering and Architecture. EG-ICE 2006. Lecture Notes in Computer Science(), vol 4200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11888598_39
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DOI: https://doi.org/10.1007/11888598_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46246-0
Online ISBN: 978-3-540-46247-7
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