Shallow defects identification for urban roads using interpretable dynamic broad network

Abstract

Deep learning is extensively utilised in transport geotechnical engineering. However, deep architectures have large computational costs and update times, while failing to understand decisions. To this regard, we propose an interpretable dynamic broad network combined with ground-penetrating radar for internal defect identification in roadbeds. The method is more suitable for feature characterisation of two-dimensional data and satisfies incremental updates. The test results indicated that the proposed method has an average recognition accuracy of 0.9124 for the four types of internal defects in roadbeds. Compared to the other four classical machine learning methods, it balances training efficiency and recognition accuracy. Robustness analysis results demonstrated that the method is noise-resistant. However, comprehending the recognition results of intelligent algorithms is a key topic. Local interpretation approach is introduced to quantify the feature importance that affects the decision of the model. Based on the feature importance calculation, it is possible to distinguish between positive and negative regions in one sample that influence the decision of the detection model. These interpretative analyses can assist us in better understanding the reasons for decisions generated by the detection model that provide technical support for subsequent enhancements.