Recently, the number of deteriorating bridges has drastically increased. As such, enormous amount of resources are invested yearly to maintain the performance of bridges within acceptable levels. This entails the development of bridge management systems to manage the imbalance between the extensive needs for maintenance, repair and rehabilitation actions, and the limited available funds. In this regard, the present study introduces three-tier platform to model and allocate limited resources in bridge deck replacement projects. The first model involves building a discrete event simulation model to mimic the bridge deck replacement process. The second encompasses structuring an efficient and straightforward surrogate machine learning model for mimicking the computationally expensive discrete event simulation model. In the second phase, a novel hybrid Elman neural network-Invasive weed optimization model is developed for predicting time, cost, greenhouse gases and utilization rates of resource allocation plans using database generated from the previous model. The third constitutes formulation of a multi-objective differential evolution optimization model subject to the utilization rates of the involved resources and their dispersion. Results manifest superiority in cost prediction accuracies; achieving mean absolute percentage error, mean absolute error and root-mean squared error of 4.873%, 78.466 and 39.515, respectively. Additionally, the developed multiobjective optimization model significantly outperformed a set well-performing meta-heuristics;