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Conference Papers Year : 2014

Inferring Asset Live Load Distributions from Traffic Flow Data: A New SHM Opportunity?


With the continuous ageing of infrastructures, live load distributions related to actual traffic has become one of the key inputs in asset management. However, it is also one fraught with difficulty, due to its complex and dynamic nature, which can only be addressed at a network level. At this level, it is impossible to envisage in-situ SHM systems installed for all critical assets. In this context, the development of reliable alternative techniques to estimate live load distributions would be a valuable addition to infrastructure asset management tools. As is well known, the accuracy of such estimates depends on several factors such as road capacity, asset condition/performance, traffic composition and seasonal effects, among others. Thus, the deployment of in-situ asset-specific systems needs to be complemented with other types of monitoring systems based on inexpensive and easy to install traffic flow sensors (point and line) in order to infer, with acceptable accuracy, rather than measure directly the live load distributions pertinent to different asset types on the network. This paper presents an approach to derive load distributions based on a Transport Infrastructure Utilisation and Maintenance Framework by utilizing recent advances achieved in two, often non-communicating, fields: structural engineering and transportation engineering. From the realm of transport analysis, the parameter ÔflowÕ has been combined with the parameter Ôlive loadÕ pertinent to structural performance. Taking advantage of traffic flow sensor systems, the aim is to examine how information related to the former enables the understanding and modelling of the latter, thus paving the way for smart transport mobility technology to be harnessed by the structural asset management community.
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hal-01020377 , version 1 (08-07-2014)


  • HAL Id : hal-01020377 , version 1


Helder Sousa, Konstantinos Zavitsas, J.W. Polak, M.K. Chryssanthopoulos. Inferring Asset Live Load Distributions from Traffic Flow Data: A New SHM Opportunity?. EWSHM - 7th European Workshop on Structural Health Monitoring, IFFSTTAR, Inria, Université de Nantes, Jul 2014, Nantes, France. ⟨hal-01020377⟩
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