Entropy-Based Automated Method for Detection and Assessment of Spalling Severities in Reinforced Concrete Bridges

AbstractExisting bridges are aging and deteriorating rapidly, elevating concerns for public safety and preservation of these valuable assets. Large numbers of bridges exist in transportation networks, and the current budget limitations worsen the situation. This necessitates the development of an automated condition assessment and rating methods. Spalling is a common problem that majorly influences the health, safety, and structural integrity of bridges. The present study introduces a self-adaptive three-tier method for the automated detection and assessment of spalling using computer-vision technologies. The first model introduces a newly-developed segmentation model that adopts a multiobjective invasive weed optimization and information theory-based formalism of images for spalled concrete detection. In the second model, an integration of singular value decomposition and discrete wavelet transform are integrated for the efficient feature extraction of information in images. Additionally, the Elman neural network is coupled with the invasive weed optimization algorithm to enhance the accuracy of the evaluation of spalling severities by amplifying the exploration-exploitation trade-off mechanism of the Elman neural network. The third model is developed for the purpose of structuring a rating system of spalling severity based on its area and depth. A computerized platform is developed using C#.net language to facilitate the implementation of the developed method by the users. The results demonstrated that the developed multiobjective spalling segmentation model is capable of improving detection accuracy of spalling by 12.29% with respect to the region growing algorithm. It was also inferred that the developed quantification model outperformed other prediction models, such that it achieved a mean absolute percentage error, root mean-squared error, and root mean squared percentage error of 4.07%, 76.061, and 0.065, respectively, based on the original dataset. In this regard, it is expected that the developed computer-vision-based method can aid in establishing cost-effective bridge condition assessment models by transportation agencies.