This paper proposes a new two-stage machine learning approach for bridge damage detection using the responses measured on a passing vehicle. In the first stage, an artificial neural network (ANN) is trained using the vehicle responses measured from multiple passes (training data set) over a healthy bridge. The vehicle acceleration or Discrete Fourier Transform (DFT) spectrum of the acceleration is used. The vehicle response is predicted from its speed for multiple passes (monitoring data set) over the bridge. Root-mean-square error is used to calculate the prediction error, which indicates the differences between the predicted and measured responses for each passage. In the second stage of the proposed method, a damage indicator is defined using a Gaussian process that detects the changes in the distribution of the prediction errors. It is suggested that if the bridge condition is healthy, the distribution of the prediction errors will remain low. A recognizable change in the distribution might indicate a damage in the bridge. The performance of the proposed approach was evaluated using numerical case studies of vehicle–bridge interaction. It was demonstrated that the approach could successfully detect the damage in the presence of road roughness profile and measurement noise, even for low damage levels.

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Agents frequently collaborate to achieve a shared goal or to accomplish a task that they cannot do alone. However, collaboration is difficult in open multi-agent systems where agents share constrained resources to achieve both individual and shared goals. In current approaches to collaboration, agents are organised into disjoint groups and social reasoning is used to capture their capabilities when selecting a qualified set of collaborators. These approaches are not useful when agents are in multiple, overlapping groups; depend on each other when using shared resources; have multiple goals to achieve simultaneously; and have to share the overall costs and benefits. In this article, agents use social reasoning to enhance their understanding of other agents’ goals and their dependencies, and self-adaptive techniques to adapt their level of self-interest in a collaborative process, with a view to contributing to lowering shared costs or increasing shared benefits. This model aims at improving the extent to which agents’ goals are met while improving shared resource usage efficiency. For example, in a public transport system where each mode of transport has limited capacity, commuters will be enabled to make choices that avoid over-capacity in different modes, or in a smart energy grid with limited capacity, users can make choices as to when they increase their demand. The model simultaneously helps avoid overloading a shared resource while allowing users to achieve their own goals. The proposed model is evaluated in an open multi-agent system with 100 agents operating in multiple overlapping groups and sharing multiple constrained resources. The impact of agents’ varying levels of social dependencies, mobility, and their groups’ density on their individual and shared goal achievement is analysed.