Research on Optimization of Intelligent Recognition Model for Bridge Cracks Based on Dual-Parameter Error Evaluation Indexes

The optimization model of intelligent identification for bridge cracks based on dual-parameter error indexes’ feedback mechanism is studied here. An interdisciplinary evaluation system of geometric morphology and fracture mechanics is proposed and established. The weighted average of two parameters is proposed as the index to evaluate the crack information model. The two parameters are as follows: (1) effective crack width index (ECWI), which reflects the geometric error of crack information vector graphics; (2) the tip curvature radius error (TCRE), which reflects the stress concentration degree of structural cracks. The aforementioned dual-parameter error evaluation indexes are processed by weighted averaging with reference to current specifications, and the recognition errors of cracks identified by the lightweight semantic segmentation model MobileNetV2-DeepLabv3+ are comprehensively evaluated. The above errors are fed back to the model training code, and parameters such as crack training hyperparameters and data augmentation parameters are adjusted for retraining. After iterative optimization from Version 1 to Version 5, the model’s prediction accuracy is improved: the Dice coefficient is increased by 3.5~32.4%, IoU by 5.3~56.5%, and PA by 0.42~1.33%, finally iterating to an optimized crack recognition model. This combined evaluation system of geometric morphology and fracture mechanics can optimize the information model through error feedback. Meanwhile, by virtue of this method, the disease photos from bridge inspections during the maintenance phase can be identified and converted into an information model of bridge diseases, which holds significant theoretical significance and engineering value for promoting digital maintenance.