Prediction of moisture content of brick walls of historical buildings in southern Fujian based on neural network

historical buildings of Gulangyu Island in Xiamen is not only a typical sample of the historical buildings in southern Fujian, but also a significant part of the World Cultural Heritage of Gulangyu Island, showing rich history and culture. Based on the field investigation data, this paper uses artificial neural network model to numerically analyze and predict the mass moisture content of brick walls of 8 historical red brick buildings in Gulangyu Island. The experimental results show that the training, testing and verification of the data set based on the prediction method proposed in this paper all obtain a high R2 value. It shows that the prediction model constructed in this paper has good generalization performance and high prediction accuracy, and is suitable for the prediction of the moisture content of brick walls of historical buildings in southern Fujian Province.

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Publication Details

Published
2025-07-12
DOI
https://doi.org/10.1145/3759928.3759933
Primary Topic
Hydrological Forecasting Using AI
Type
article
Field-Weighted Citation Impact
0.00
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Prediction of moisture content of brick walls of historical buildings in southern Fujian based on neural network

Niwat Angkawisittpan, Xiao-li Fu, Yu-Chieh Lin, Guifen Lyu
Hydrological Forecasting Using AI
article

Prediction of moisture content of brick walls of historical buildings in southern Fujian based on neural network

Niwat Angkawisittpan, Xiao-li Fu, Yu-Chieh Lin, Guifen Lyu
article en

Abstract

historical buildings of Gulangyu Island in Xiamen is not only a typical sample of the historical buildings in southern Fujian, but also a significant part of the World Cultural Heritage of Gulangyu Island, showing rich history and culture. Based on the field investigation data, this paper uses artificial neural network model to numerically analyze and predict the mass moisture content of brick walls of 8 historical red brick buildings in Gulangyu Island. The experimental results show that the training, testing and verification of the data set based on the prediction method proposed in this paper all obtain a high R2 value. It shows that the prediction model constructed in this paper has good generalization performance and high prediction accuracy, and is suitable for the prediction of the moisture content of brick walls of historical buildings in southern Fujian Province.

Mahasarakham University (TH), Ningxia University (CN), Xiamen University of Technology (CN)
Openalex Percentile: Top 77%
Hydrological Forecasting Using AI
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