Knowledge Discovery in Bridge Monitoring Data: A Soft Computing Approach
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.
KeywordsSingle Event Vehicle Velocity Vehicle Weight Single Vehicle Axle Load
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- 1.WAVE: Weigh-in-Motion of Axles and Vehicles for Europe. General Report of the 4th FP Transport, RTD project, RO-96-SC, 403, Jacob, B. (ed.) LCPC, Paris (2001)Google Scholar
- 2.COST 323: European Specification on Weigh-in-Motion of Road Vehicles. EUCO-COST/323/8/99, LCPC Paris (1999)Google Scholar
- 3.Jacob, B., O’Brien, E.J.: Weigh-in-Motion: Recent Developments in Europe. In: Proceedings of the 4th International Conference on WIM, ICWIM4, Taipei, pp. 2–12 (2005)Google Scholar
- 4.Opitz, R., Kühne, R.: IM (Integrated Matrix) WIM Sensor and Future Trials. In: Proceedings of the 4th International Conference on WIM, ICWIM4, Taipei, pp. 61–71 (2005)Google Scholar
- 5.Brozovič, R., Žnidarič, A., Vodopivec, V.: Slovenian Experience of using WIM Data for Road Planning and Maintenance. In: Proceedings of the 4th International Conference on WIM, ICWIM4, Taipei, pp. 334–341 (2005)Google Scholar
- 8.Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery in Databases. AI Magazin, 37–54 (1996)Google Scholar
- 9.Lutzenberger, S., Baumgärtner, W.: Evaluation of measured Bridge Responses due to an instrumented Truck and free Traffic. In: Bridge Management, 4th edn., Ryall, Parke, Hardening. Thomas Telford, London (2000)Google Scholar
- 10.Schnellenbach-Held, M., Lubasch, P., Buschmeyer, W.: Evolutionary Algorithm based Assessment of Traffic Density Changes. In: IABSE Symposium, Budapest (2006)Google Scholar