Several aspects of vehicle-pavement interaction have been studied and discussed in this thesis. Initially the pavement response is studied through a quasi-static and a dynamic computationally efficient framework under moving traffic loads. Subsequently, a non-stationary stochastic solution has been developed in order to account for the effect of pavement surface deterioration on pavement service life.The quasi-static procedure is based on a superposition principle and is computationally favourable, as it requires only a reduced incremental problem to be solved numerically. Using the developed framework, the effect of vehicle configuration and traffic characteristics on the damage induced in pavements is investigated numerically. It is shown that the developed numerical model provides a more accurate explanation of different distress modes.In the dynamic approach the pavement roughness and vehicle suspension system are linked to a dynamic pavement model in order to account for the dynamic effects of vehicle-pavement interaction on pavement response. A finite element method is employed in order to establish the response function for a linear viscoelastic pavement structure with dynamic effects taken into account. The developed computational procedure is applied to evaluate the effect of the pavement surface roughness on the pavement structure response to truck traffic loadings.Furthermore, the deterioration trends for the flexible pavement surface have been investigated based on field measurements of longitudinal profiles in Sweden. A predictive function is proposed for surface deterioration that is based on the average gradient of yearly measurements of the road surface profiles in Swedish road network. The developed dynamic framework is further elaborated to a non-stationary stochastic approach. The response of the flexible pavement is given for a non-stationary random case as the pavement surface deteriorates in pavement service life, thus influencing the magnitude of the dynamic loads induced by the vehicles. The effect of pavement surface evolution on the stress state induced in the pavement by moving traffic is examined numerically. Finally the effect of surface deterioration on pavement service life has been investigated and discussed in the thesis by incorporating the proposed prognostic surface deterioration model into a ME design framework. The results are discussed for different case studies with different traffic regimes. It was indicated that the predicted pavement service life decreases considerably when the extra dynamic loads, as a result of pavement surface deterioration, has been taken into account. Furthermore, the effect of performing a predictive rehabilitation process (i.e. resurfacing) has been studied by employing a LCC framework. The application of preventive maintenance was shown to be effective, especially when the deterioration rate is high. / <p>QC 20141119</p>

The field of bridge monitoring is one of rapid development. Advances in sensor technologies, in data communication and processing algorithms all affect the possibilities of Structural Monitoring in Bridges. Bridges are a very critical part of a country’s infrastructure, they are expensive to build and maintain, and many uncertainties surround important factors determining their serviceability and deterioration state. As such, bridges are good candidates for monitoring. Monitoring can extend the service life and avoid or postpone replacement, repair or strengthening works. The amount of resources saved, both to the owner and the users, by reducing the amount of non-operational time can easily justify the extra investment in monitoring. This thesis consists of an extended summary and five appended papers. The thesis presents advances in sensor technology, damage identification algorithms, Bridge Weigh-In-Motion systems, and other techniques used in bridge monitoring. Four case studies are presented. In the first paper, a fully operational Bridge Weigh-In-Motion system is developed and deployed in a steel railway bridge. The gathered data was studied to obtain a characterization of the site specific traffic. In the second paper, the seasonal variability of a ballasted railway bridge is studied and characterized in its natural variability. In the third, the non-linear characteristic of a ballasted railway bridge is studied and described stochastically. In the fourth, a novel damage detection algorithm based in Bridge Weigh-In-Motion data and machine learning algorithms is presented and tested on a numerical experiment. In the fifth, a bridge and traffic monitoring system is implemented in a suspension bridge to study the cause of unexpected wear in the bridge bearings. Some of the major scientific contributions of this work are: 1) the development of a B-WIM for railway traffic capable of estimating the load on individual axles; 2) the characterization of in-situ measured railway traffic in Stockholm, with axle weights and train configuration; 3) the quantification of a hitherto unreported environmental behaviour in ballasted bridges and possible mechanisms for its explanation (this behaviour was shown to be of great importance for monitoring of bridges located in colder climate) 4) the statistical quantification of the nonlinearities of a railway bridge and its yearly variations and 5) the integration of B-WIM data into damage detection techniques. / <p>QC 20140910</p>

The field of monitoring is one of rapid development. Advances in sensor technologies, in data communication paradigms and data processing algorithms all influence the possibilities of Structural Health Monitoring, damage detection, traffic monitoring and other implementations of monitoring systems. Bridges are a very critical part of a country’s infrastructure, they are expensive to build and maintain, and many uncertainties surround important factors determining the serviceability and deterioration of bridges. As such, bridges are good candidates for monitoring. Monitoring can extend the service life and avoid or postpone replacement, repair or strengthening work. Many bridges constitute a bottleneck in the transport network they serve with few or no alternative routes. The amount of resources saved, both to the owner and the users, by reducing the amount of non-operational time can easily justify the extra investment in monitoring. This thesis consists of an extended summary and three appended papers. The thesis presents advances in sensor technology, damage identification algorithms and Bridge Weigh-In-Motion techniques. Two case studies are carried out. In the first a bridge and traffic monitoring system is implemented in a highway suspension bridge to study the cause of unexpected wear in the bridge bearings. In the second a fully operational Bridge Weigh-In-Motion system is developed and deployed in a steel railway bridge. The gathered data was studied to obtain a characterization of the site specific traffic. / QC 20111122

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