## 5 Conclusions  
In this paper, the transverse connectivity and durability of the concrete hollow slab bridges are investigated in the field test using the surface damage and neural networks. A total of 13 hollow slab bridges in the Wu-He highway system were taken as the background bridge. The surface damage on the bridges was first checked in the visual inspection. A  
Figure 18: Predictions and test results: (**a**) hinge joint damage; (**b**) durability of the girder.  
static load test was conducted to evaluate the transverse connectivity of the hinge joints based on the girder responses. The hollow slab bridges were then demolished, and each girder was cut into six segments to evaluate the durability of the concrete girders. An evaluation method based on the neutral networks and surface damage was finally proposed and verified by the field test data. The following conclusions can be drawn:  
* Visual inspection showed that there were two types of the typical defects in the hollow slab bridges, i.e., the transverse cracks on the bottom plates of the girders and the longitudinal cracks in the hinge joints. The transverse cracking mainly occurred in the mid-span of the bridges, while the distribution of the longitudinal cracks on the joints was quite uniform along the longitudinal location.
* The static load test showed that the distribution of the deflection of each girder was non-uniform due to the weakening of the transverse connectivity, and the girder deflection increased with the increase of the joint damage. A damage index was proposed based on the theoretical and measured deflections, and the joint damage can be quantitatively assessed when the index value is larger than 0. The durability of the hollow slab girders was evaluated after cutting off the concrete girders. Results showed that the girders in the background bridges were within a moderate deterioration condition after 25 years' service life.
* An evaluation method of hollow slab girders based on the surface damage and neural networks was proposed and verified by the field test data. The maximum crack width at different locations of the bridge was used in the input layer of the neural network, and the hinge joint damage or the durability was considered as the output results. The prediction error of the method in the test set was within 15.0% for the hinge joint damage and within 40% for the durability result of the girder, indicating the feasibility of the evaluation method.  
Conceptualization, C.J. and W.X.; methodology, C.J.; software, [PERSON]; validation, C.J., [PERSON] and J.Y.; investigation, [PERSON]; resources, J.Y.; data curation, Z.W.; writing--original draft preparation, C.J.; writing--review and editing, W.X. and C.C.; supervision, W.X. and C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation of China (grant numbers 52108271, 52022021, and 51978160); Key Research and Development Program of Jiangsu Province of China (grant number BE2021089); China Postdoctoral Science Foundation (grant number 2022T150119).  
Not applicable.  
Data Availability Statement: Data available on request due to privacy/ethical restrictions.  
The authors would like to thank the anonymous reviewers for their constructive suggestions and comments to improve the quality of the paper.  
The authors declare no conflict of interest.