## 5 Conclusions  
For the non-staggered-step bridgehead settlement, the existing national standards do not stipulate its detection methods. The existing methods have low efficiency, intense subjectivity, low sensitivity to sectional settlement, and are vulnerable to the interference of deformation distresses. Given the above problems, this paper proposes to use the vehicular detection system to rapidly collect the longitudinal slope data and pavement images, forming the road profile line corresponding to the image position. Based on this, a calculation model and algorithm of the non-staggered-step bridgehead settlement are established. Moreover, a calculation index, namely, the settlement point ratio (SPR), is proposed and verified. The main conclusions of this paper are as follows:  
The accuracy of the road longitudinal slope collected by the vehicular detection system was verified by comparative tests. By testing three different vehicle speeds, the proportion of detection values with absolute error less than 1.5% is more than 95%. The results show that the longitudinal slope errors between the automatic detection and the manual measurement of 15 groups of distance gradients meet the requirements of relevant standards. The rules for selecting test objects and selecting model parameters were stipulated. Automatic and manual tests were carried out on 31 Ningbo, Zhejiang Province, bridges. In total, 50 SPRs were output through the established model based on automatic detection data. In addition, the correlation and regression analysis were carried out on the absolute value of 50 groups of longitudinal slope difference by manual measurement. The results indicate that the correlation coefficient of the two is 0.934, and the determination coefficient of the regression model is 0.872. The two are significantly correlated, confirming the capability of using this methodology to measure the non-staggered-step bridgehead settlement. Overall, the detection method proposed in this paper effectively supplements the research blank of bridgehead settlement detection and has significant application value for its automatic quantitative detection. However, this method is suitable for single-lane detection. Further research is needed for one-time detection of bridgehead settlement under multi-lanes. In the future, data sets from other regions need to be added to optimize the proposed detection method further to enhance the model's universality. Methodology, Y.P. ; Software, Z.C. ; Writing--original draft, H.L. ; Writing--review & editing, Z.Z. and M.Z. ; Funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript. Figure 8: Linear regression model between algorithm results and manual measurement results. **Funding:** This work was supported by the National Natural Science Foundation of China: [62206201], Technology Program Projects of Shanghai Municipal Commission of Science and Technology: [23692118200] and the Fundamental Research Funds for the Central Universities: [TTS2021-03]. **Data Availability Statement:** Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request. **Conflicts of Interest:** The authors declare that they have no conflict of interest or personal relationships that could have appeared to influence the work reported in this paper. **Appendix A. Results of Automatic and Manual Tests**