Short time step prediction of moisture content of construction materials based on VAR modeling

The purpose of this paper is to explore the feasibility and effectiveness of utilizing Vector Autoregression (VAR) model to predict the moisture content of construction materials. The moisture content of construction materials has an important impact on their performance, durability and safety. Reasonable control of moisture content can prolong the service life of materials, improve the quality and efficiency of construction, reduce energy consumption, lower economic costs, and improve environmental adaptability. However, traditional water content prediction methods mostly rely on empirical data and simple statistical models, which are difficult to accurately capture the complex relationship between multiple variables. For this reason, this paper proposes a prediction method based on the VAR model in order to overcome the limitations of the traditional methods. Experimental results show that the VAR model exhibits high accuracy in predicting the water content of construction materials, and its MAE, RMSE, and MAPE are better than those of the commonly used deep learning models such as CNN and LSTM. This may be attributed to the lower computational complexity of the VAR model and its effective capture of stable relationships.