# Load Testing and Analysis of a Large Span Through Simply-Supported Steel Box Arch Bridge
## 6 Conclusions
### Discussion  
Revised sentence: Conducting load testing on the bridge can provide an accurate reflection of its condition at the time of testing. To ensure the safety of the bridge during subsequent periods of use, it is recommended to regularly inspect the bridge in accordance with the _Technical Standards for Urban Bridge Maintenance_[35]. This will enable timely detection and resolution of any defects. Additionally, implementing long-term health monitoring for the bridge structure is particularly advantageous for its operation and maintenance. A concise yet comprehensive discussion on this topic follows:  
(1) Bridge structural health monitoring, as opposed to traditional manual monitoring, possesses all-weather and full-time domain capabilities, facilitating the accurate, real-time, and prompt accomplishment of bridge monitoring. Carrying out long-term bridge structural health monitoring enables the timely detection of abnormal changes and potential issues in the structure, enhancing bridge safety. It also facilitates the prompt identification of deterioration, damage, or fatigue phenomena in bridge structures, which can be addressed through timely repairs and reinforcements, thereby extending the bridge's service life. Moreover, through data analysis and the establishment of predictive models, decisions regarding bridge maintenance can be optimized, leading to cost reduction. (2) The accuracy of bridge structural health monitoring data is influenced by various factors, including sensor performance and installation location, the data processing system, and temperature fluctuations. Among these factors, the impact of temperature on bridge structural health monitoring should not be underestimated. A strong positive correlation exists between temperature and strain in the bridge structure. Therefore, it is crucial to thoroughly consider the effects of temperature and employ precise compensation methods to minimize or eliminate its influence on measurement results during long-term monitoring. (3) The field of bridge structural health monitoring is continuously advancing, with current research focusing on the integration of technologies such as the Internet of Things (IoT) and machine learning. However, further development is still required in this area. Future advancements include the utilization of more efficient and precise sensors, as well as the integration of multiple sensor types for multi-modal monitoring to provide comprehensive solutions for monitoring complex structures. Moreover, there is a need to develop more intelligent algorithms capable of processing large volumes of monitoring data in a precise and sophisticated manner. By combining machine learning with predictive models, real-time data can be used to dynamically assess the structure's condition and predict its remaining service life and maintenance requirements. The incorporation of these emerging technologies will significantly enhance the efficiency, intelligence, and accuracy of bridge structural health monitoring systems while greatly improving the safety and longevity of engineering structures. (4) In order to improve the predictive accuracy of finite element models, model updating techniques have become an effective approach. By incorporating experimental data into the calibration of model parameters, the accuracy of the numerical model can be significantly enhanced. Especially in modal analysis, optimizing the distribution of mass and stiffness in the model allows the numerical predictions of natural frequencies,mode shapes, and other modal parameters to better align with actual experimental results, thereby providing more reliable data support for structural health monitoring. Future research could further explore how to incorporate more measured data, consider more complex nonlinear effects, develop model updating programs, and improve the computational efficiency of algorithms in order to further enhance the accuracy and practicality of model updating. Conceptualization, Q.X. ; Methodology, Z.L. ; Software, Z.L., W.X. and M.S.