For improving the seismic isolation effect, traditional rubber isolation bearing provides a smaller horizontal stiffness. However, it is unfavorable for the displacement control of the seismic isolation layer under rare earthquakes. In this paper, an improved lead-core rubber isolation bearing is proposed. The improved isolation bearing can provide a small horizontal stiffness to enhance the seismic isolation effect under small earthquakes. Under large earthquakes, it can provide a large horizontal stiffness to prevent over-limit failure due to excessive displacement. The mechanical properties of the improved isolation bearing were investigated using the finite element method (FEM), and the restoring force model of the improved isolation bearing was established. Based on the FEMA P-58 theory, the earthquake loss assessment in terms of repair cost and casualty indexes was carried out for normal frame structures, normal isolation structures, and improved isolation structures. The results show that the improved isolation bearing maintains a smaller horizontal stiffness before the displacement is limited, giving full play to the isolation performance. After that, the horizontal stiffness of the bearing is enhanced, which can effectively control the displacement of the seismic isolation layer. The lead-core can give full play to the energy dissipation characteristics. Under the four performance levels, the improved isolation structure has the highest safety reserve and the best collapse resistance. The use of improved isolation bearings can reduce the repair cost of the structure and casualties.

Historical seismic damage data show that most strong earthquakes are accompanied by multiple intense aftershocks. In general, the time interval between the main shock and the aftershocks is relatively short, and structure repair work is often not completed before the aftershocks occur. For a structure that has suffered damage from the main shock, the aftershock will further aggravate the damage and even cause complete collapse. Based on the incremental dynamic analysis (IDA) method, this paper establishes a probabilistic seismic demand model for the SRC framework‐core tube structure and plots the vulnerability curve of a structure under the action of the main aftershock sequence, which occurs following the actions of frequent earthquakes, fortification earthquakes, and rare earthquakes. The structure vulnerability matrix and the vulnerability index are used to evaluate the seismic performance of a structure. This study found that the occurrence of aftershocks leads the structure to a more unfavourable failure state. Taking the vulnerability index as an evaluation parameter, the structural vulnerability index when subjected to an intensity 8 earthquake under the action of the main aftershock is approximately 10% larger than under the action of a single main shock. Meanwhile, the SRC frame‐core structure designed according to the current Chinese specifications meets the expected seismic fortification target, even after being acted upon by the main aftershock ground motion sequence.

Murals are not merely independent visual artworks. Rather, they are an integral part of architectural heritage, directly attached to buildings’ structural elements, such as brick walls and vaults. However, murals are susceptible to various building-related types of damage, including structural cracks and moisture-induced peeling, due to long-term exposure to environmental factors and geological changes. As the progressive deterioration of these murals hastens the loss of mural value, professional assessment and restoration are urgently required. To tackle the issues of low efficiency in traditional structural damage detection and the absence of predictable repair plans, this paper presents a semi-automatic building-mural protection solution that integrates morphological assessment of mural deterioration with computer vision technology. This study establishes an image prediction system that integrates intelligent damage identification with virtual restoration. First, employing the PaddleSeg deep learning framework and the DeepLabv3 semantic segmentation model, this study used existing mural damage datasets to build a recognition model. The model allows for intelligent identification and labeling of multiple damage types. Subsequently, relying on the ComfyUI platform, Stable Diffusion was used to construct a virtual restoration model. LoRA (low-rank adaptation) technology was introduced to fine-tune the model specifically for the mural style, thus enhancing the directivity and accuracy of virtual restoration. Finally, by applying the results of the recognition model to the virtual restoration model, this study built an integrated system for mural damage diagnosis and virtual restoration. The results show that the damage recognition model achieved a mean intersection over union (mIoU) of 47.8% and a pixel accuracy of 77.97% on the test set, validating the feasibility of using semantic segmentation for mural damage detection. This study presents an integrated workflow framework integrating automatic damage identification and intelligent repair. As an expert-assisted tool, this framework shows application potential for preliminary exploration of mural disease diagnosis and virtual restoration plans, providing technical references for the digital protection of cultural heritage.