This paper proposes a meta-model-based parametric Historic Building Information Modelling (HBIM) approach to preserving and renewing traditional timber dwellings, specifically focusing on traditional Bai ethnic residential architecture. The study integrates traditional architectural principles with contemporary digital construction techniques. Traditional Bai dwellings have complex timber structural and spatial characteristics with various components. Results from the application of HBIM demonstrate improved efficiency in documenting and managing structural information, facilitating the maintenance and preservation of heritage buildings. The study concludes that HBIM, supported by parametric and generative design approaches, offers significant advantages in the digital preservation of architectural heritage. This approach not only ensures the structural integrity and historical accuracy of the models but also provides a scalable solution for managing and preserving traditional dwellings in the face of modernization pressures. This research broadens the scope of parametric design within digital construction theory, particularly concerning ancient timber structures. It offers a crucial framework that can inform both future studies and practical efforts in the preservation of heritage buildings.

This study presents an innovative approach to sustainable timber construction by integrating traditional woodworking techniques with advanced robotic technology. The research focuses on three key objectives: preserving traditional craftsmanship, enhancing material conservation, and improving production efficiency. A meta-model-based framework is developed to capture the woodcrafts of mortise and tenon joints, which are prevalent in traditional Chinese wooden architecture. The study employs parametric design and robotic arm technology to digitize and automate the production process, resulting in significant improvements in material utilization and processing efficiency. Specifically, this study utilizes genetic algorithm strategies to resolve the problem of complex mortise and tenon craftsmanship optimization for robotic arms. Compared to conventional CNC machining, the proposed method demonstrates superior performance in path optimization, reduced material waste, and faster production times. The research contributes to the field of sustainable architecture by offering a novel solution that balances the preservation of cultural heritage with modern construction demands. This approach not only ensures the continuity of traditional woodworking skills but also addresses contemporary challenges in sustainable building practices, paving the way for more environmentally friendly and efficient timber construction methods.

A dendritic neuron model (DNM) is a deep neural network model with a unique dendritic tree structure and activation function. Effective initialization of its model parameters is crucial for its learning performance. This work proposes a novel initialization method specifically designed to improve the performance of DNM in classifying high-dimensional data, notable for its simplicity, speed, and straightforward implementation. Extensive experiments on benchmark datasets show that the proposed method outperforms traditional and recent initialization methods, particularly in datasets consisting of high-dimensional data. In addition, valuable insights into the behavior of DNM during training and the impact of initialization on its learning performance are provided. This research contributes to the understanding of the initialization problem in deep learning and provides insights into the development of more effective initialization methods for other types of neural network models. The proposed initialization method can serve as a reference for future research on initialization techniques in deep learning.