Integrative evolutionary-based method for modeling and optimizing budget assignment of bridge maintenance priorities

Recently, the number of deteriorating bridges has drastically increased. As such, enormous amounts 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 a three-tier platform to model and allocate limited resources in maintenance 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 the time, cost, greenhouse gases, and utilization rates of resource allocation plans using a database generated from the previous model. The third constitutes the formulation of a multiobjective differential evolution optimization model subject to the utilization rates of the involved resources and their dispersion. Results manifest superiority in cost prediction accuracies, achieving a 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 of well-performing metaheuristics, yielding a hypervolume indicator, generational distance, spacing, diversity, spread, and coverage of 81.721%, 0.029%, 0.1881%, 0.5229%, 0.9618%, and 0.4087%, respectively. The results also demonstrate the developed multiobjective optimization model accomplished an improvement in the minimization time, cost, and greenhouse gases by 71.01%, 27.87%, and 39.29%, respectively, when compared against a genetic algorithm. The developed models are automated through the hybrid programming of C#.net and MATLAB. It is expected that the developed method can enable the practitioners and transportation agencies to establish timely-efficient, cost-effective, and sustainable resource allocation plans while accommodating the efficacious utilization of resources.