Optimal sensor placement for timber architecture heritage poses a significant challenge due to the unique structural types and complex monitoring purposes. In this study, a three-stage method is proposed, taking a courtyard-style heritage, built 133 years ago, as an example. First, a finite element model that accounted for the parameter randomness and initial damage was constructed using a genetic algorithm (GA) and experimental results. Second, a new weighted fitness function of logarithmic type was developed for multi-type sensors and multi-objective monitoring. Third, a novel genetic algorithm, Meta-GA, was proposed, introducing competition group mechanisms and gene libraries to improve optimal capability while maintaining computational efficiency. The Meta-GA is then compared to the other two optimization modes using seven indexes. Finally, damage detection capability was tested for the proposed three schemes at noise levels of 0%, 5%, and 10%. The results reveal that the proposed three-stage method with Meta-GA can provide the best solution.

The bond–slip model plays an important role in the structural analysis of reinforced concrete structures. However, many factors affect the bond–slip behavior, which means that a large number of tests are required to establish an accurate bond–slip model. This paper aims to establish a data-driven method for the prediction of the bond–slip model of historical reinforced concrete with few test specimens and many features. Therefore, a new Mahalanobis-Meta-learning Net algorithm was proposed, which can be used to solve the implicit regression problem in few-shot learning. Compared with the existing algorithms, the Mahalanobis-Meta-learning Net achieves fast convergence, accurate prediction and good generalization without performing a large number of tests. The algorithm was applied to the prediction task of the bond–slip model of square rebar-reinforced concrete. First, the first large pretraining database for the bond–slip model, BondSlipNet, was established containing 558 samples from the existing literature. The BondSlipNet database can be used to provide a priori knowledge for learning. Then, another database, named SRRC-Net, was obtained by 16 groups of pull-out tests with square rebar. The SRRC-Net database can be used to provide the posteriori knowledge. Finally, based on the databases, the algorithm not only successfully predicted the bond–slip model of square rebar-reinforced concrete, but also that of the other 23 types of reinforced concrete. The research results can provide a scientific basis for the conservation of square rebar-reinforced concrete structures and can contribute to the bond–slip model prediction of the other types of reinforced concrete structures.