## 6 Conclusions  
1. In this paper, a set of accurate and efficient three-dimensional temperature field simulation methods was formed by the secondary development of Abaqus software Version 6.10. The comparison results between the simulated data and the experimental data of the temperature field show that the maximum deviation between the simulated temperature value and the measured value was only 3.3 \({}^{\circ}\)C. The calculation accuracy of the proposed algorithm can better meet the needs of engineering applications.
2. The real-time shadow occlusion state was successfully displayed through the improvement of the three-dimensional sunshine temperature field simulation algorithm in this paper and the cloud image customization function in Abaqus software Version 6.10., and the real-time sunshine shadow width identified by the algorithm was compared with the theoretical value calculated by the theoretical formula. The maximum difference between the two was only 19.1 mm, which proves that the sunshine shadow recognition algorithm proposed in this paper has extremely high recognition accuracy.
3. The results of the surface temperature difference analysis of the concrete bridge tower show that the temperature difference between the external surfaces of the structure can reach 11.6 \({}^{\circ}\)C under the influence of sunshine and shadow, which is far more than the recommended temperature difference value (\(\pm 5\)\({}^{\circ}\)C) given in the \"Design of Highway Cable-Stayed Bridge\" (JTG/T 3365-01-2020). Therefore, the principle of \"bridge-by-bridge analysis\" is recommended in this paper.
4. For concrete bridge towers, the temperature change was relatively obvious only at a range of 0.3 m from the external surface of the tower wall, and the temperature change was small in the remaining range.
5. The temperature distribution of the concrete bridge tower in the direction of thickness conforms to the exponential function \(T(x)=T_{0}e^{-ax}+C\). Additionally, the data fitting results indicate that using the temperature data at a distance of 0.8 m from the external surface as the calculation parameter in the function can achieve the ideal result, and the minimum correlation coefficient of the fitting result was 0.968.  
Conceptualization, [PERSON] and [PERSON]. ([PERSON]); methodology, [PERSON] and [PERSON]. ([PERSON]); software, S.Z. and [PERSON]; validation, [PERSON] ([PERSON]), [PERSON] ([PERSON]), and [PERSON]; formal analysis, Y.Z. and J.Z.; investigation, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]); data curation, S.Z.; writing--original draft preparation, S.Z. and [PERSON]. ([PERSON]); writing--review and editing, J.Z.; visualization, Y.Z.; supervision, [PERSON] ([PERSON]); project administration, S.Z.; funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
Figure 28: Temperature load loading mode. Note: \(T_{1}(x)\), \(T_{2}(x)\), \(T_{3}(x)\), and \(T_{4}(x)\) in the diagram are all the calculation relations of temperature in the thickness direction of each tower wall fitted by Formula (33).  
**Funding:** This research was funded by the Science and Technology Project of the Shaanxi Department of Transportation (grant number: No. 21-59K) and the Science and Technology Project of the Shaanxi Department of Transportation (grant number: No. 21-43K).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.