Behavioral model construction of architectural heritage for digital twin

Wooden architectural heritage, an irreplaceable repository of cultural value, is highly vulnerable to factors such as component aging, biological infestations, and various forms of damage. Most current preservation methods primarily focus on salvage and repair after damage occurs, lacking the ability to predict changes in the structural components in advance. This study addresses this gap by constructing a digital twin behavioral model through the design of a TLSA-PSO prediction network grounded in the broader digital twin framework. Using typical ancient wooden architectural heritage in China as a case study, the validity and accuracy of the behavioral prediction model are verified. Additionally, a digital twin behavioral model visualization system is developed to display the prediction results. Experimental outcomes demonstrate that the behavioral prediction model can accurately forecast the behavioral changes of architectural heritage, achieving a goodness of fit of 0.99. This makes preventive protection of architectural heritage feasible.