The safety of prestressed steel structures in service has been studied widely. However, traditional safety assessment methods for prestressed steel structures involve few sample points, do not provide accurate predictions, and consume substantial human and material resources. The digital twin technology can be used to monitor the structural behavior, state, and activity of a steel structure throughout its life cycle, which is equivalent to performing a safety assessment of the structure. The purpose of this study is to establish a digital twin multidimensional model of prestressed steel structures. Based on this model, the support vector machine and prediction model are trained using the relevant structural history data, and the safety risk level of the structure is then predicted based on the measured data. Finally, a proportional reduction model of the wheel-spoke cable truss structure is used to verify the feasibility of the proposed method. The results show that digital twin technology can achieve real-time monitoring of prestressed steel structures in use and can provide timely predictions of the safety level. This represents a new method for the safety risk assessment of prestressed steel structures.

The post-earthquake retrofitting and repair process of a building is a key factor in improving its seismic capability. A thorough understanding of retrofitting methods and processes will aid in repairing post-earthquake buildings and improving seismic resilience. This study aims to develop a visualization framework for the post-earthquake retrofitting of buildings which builds models based on building information modeling (BIM) and realizes visualization using augmented reality (AR). First, multi-level representation methods and coding criteria are used to process the models for a damaged member. Then, an information collection template is designed for integrating multi-dimensional information, such as damage information, retrofitting methods, technical solutions, and construction measures. Subsequently, a BIM model is presented in three dimensions (3D) using AR. Finally, the visualization process is tested through experiments, which demonstrate the feasibility of using the framework to visualize the post-earthquake retrofitting of a building.

The most negative effects caused by earthquakes are the damage and collapse of buildings. Seismic building retrofitting and repair can effectively reduce the negative impact on post-earthquake buildings. The priority to repair the construction after being damaged by an earthquake is to perform an assessment of seismic buildings. The traditional damage assessment method is mainly based on visual inspection, which is highly subjective and has low efficiency. To improve the intelligence of damage assessments for post-earthquake buildings, this paper proposed an assessment method using CV (Computer Vision) and AR (Augmented Reality). Firstly, this paper proposed a fusion mechanism for the CV and AR of the assessment method. Secondly, the CNN (Convolutional Neural Network) algorithm and gray value theory are used to determine the damage information of post-earthquake buildings. Then, the damage assessment can be visually displayed according to the damage information. Finally, this paper used a damage assessment case of seismic-reinforced concrete frame beams to verify the feasibility and effectiveness of the proposed assessment method.