**6. Conclusions**

This paper proposes a modeling method for the high formwork monitoring data. The details of establishing the ARMA model and BPNN have been presented, and the algorithm of model order estimation by BPNN is introduced. Through the method of Monte Carlo simulation, we studied the accuracy of the three methods under different coefficients, different sequence lengths, and different model orders. For the actual measured stress series, we used three methods to estimate the model order and then used the least square method to estimate the model coefficients. Finally, we applied the established model to the symmetrical high formwork monitoring data. According to the simulation and application results, the following conclusions can be made.


Due to the influence of computing resources, the model of all orders is not calculated when considering sequence coefficients and sequence length. In addition, the model established in this paper does not consider the impact of accidental factors. We will discuss the effects of accidental factors on model-building in future studies, and further study the methods of predicting periodic data.

**Author Contributions:** Conceptualization, Y.Y. and G.Y.; methodology, Y.Y. and G.Y.; software, Y.Y. and L.Y.; formal analysis, Y.Y. and L.Y.; writing—original draft preparation, Y.Y. and G.Y.; writing— review and editing, Y.Y. and G.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was funded by National Key R&D Program of the Ministry of Science and Technology (No. 2019YFD1101005-4), Fundamental Research Funds for the Central Universities (2020CDJQY-A067) and the 111 project of the Ministry of Education and the Bureau of Foreign Experts of China (No. B18062).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Please contact the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.
