A Comparative Study of a Fully-Connected Artificial Neural Network and a Convolutional Neural Network in Predicting Bridge Maintenance Costs

Abstract

The cost assessment of bridge maintenance is a difficult topic to study, but it is critical for a bridge life cycle cost analysis. The maintenance costs sample database was established in this study according to actual engineering data, and a bridge maintenance cost prediction model was developed using a fully-connected artificial neural network (ANN) and convolutional neural network (CNN), respectively. First, eight main factors affecting maintenance costs were evaluated based on the random forest method, and the evaluation results were verified by an exploratory data analysis. The original data were then screened based on the isolation forest principle, and the recent gross domestic product (GDP) growth rate was used to illustrate the relationship between economic development and bridge maintenance costs. Finally, these two neural networks were used to establish maintenance cost prediction models, respectively. The results from the two models were compared and their prediction accuracies were analyzed. The prediction performance of the CNN model for bridge maintenance costs was found to be better than that of the traditional fully-connected ANN model. The results of this study will enhance the opportunity for bridge managers to balance lifecycle maintenance costs.