Safety management in hoisting is the key issue to determine the development of prefabricated building construction. However, the security management in the hoisting stage lacks a truly effective method of information physical fusion, and the safety risk analysis of hoisting does not consider the interaction of risk factors. In this paper, a hoisting safety risk management framework based on digital twin (DT) is presented. The digital twin hoisting safety risk coupling model is built. The proposed model integrates the Internet of Things (IoT), Building Information Modeling (BIM), and a security risk analysis method combining the Apriori algorithm and complex network. The real-time perception and virtual–real interaction of multi-source information in the hoisting process are realized, the association rules and coupling relationship among hoisting safety risk factors are mined, and the time-varying data information is visualized. Demonstration in the construction of a large-scale prefabricated building shows that with the proposed framework, it is possible to complete the information fusion between the hoisting site and the virtual model and realize the visual management. The correlative relationship among hoisting construction safety risk factors is analyzed, and the key control factors are found. Moreover, the efficiency of information integration and sharing is improved, the gap of coupling analysis of security risk factors is filled, and effective security management and decision-making are achieved with the proposed approach.

With the development of the next generation of information technology, an increasing amount of attention is being paid to smart residential spaces, including smart cities, smart buildings, and smart homes. Building indoor safety intelligence is an important research topic. However, current indoor safety management methods cannot comprehensively analyse safety data, owing to a poor combination of safety management and building information. Additionally, the judgement of danger depends significantly on the experience of the safety management staff. In this study, digital twins (DTs) are introduced to building indoor safety management. A framework for an indoor safety management system based on DT is proposed which exploits the Internet of Things (IoT), building information modelling (BIM), the Internet, and support vector machines (SVMs) to improve the level of intelligence for building indoor safety management. A DT model (DTM) is developed using BIM integrated with operation information collected by IoT sensors. The trained SVM model is used to automatically obtain the types and levels of danger by processing the data in the DTM. The Internet is a medium for interactions between people and systems. A building in the bobsleigh and sled stadium for the Beijing Winter Olympics is considered as an example; the proposed system realises the functions of the scene display of the operation status, danger warning and positioning, danger classification and level assessment, and danger handling suggestions.

Highway infrastructure plays an important role in assuring the proper function of the nation’s transportation. Highway alignment is an essential part of the highway planning and design phase, which has significant effects on the surroundings. Identifying optimal highway routes while using traditional methods requires significant time, cost, and effort, since it requires a comprehensive assessment of multiple factors, such as cost and environmental impacts. This study proposes an approach for managing highway alignment in the context of a larger landscape that integrates building information modelling (BIM) and geographic information system (GIS) capabilities. To support this integration, semantic web technologies are used to integrate data on a semantic level. Moreover, the approach also uses genetic algorithms (GAs) for optimizing highway alignments. A fully automated model is developed that enables data interoperability between BIM and GIS systems and also allows for data exchange between the integration model and the optimization algorithm. The model enables the full exploitation of features of the project and its surroundings for highway alignment planning. The proposed model is also applied to a real highway project to validate its effectiveness. The visualization model of the highway project and its surroundings provides a realistic three-dimensional image that produces a comprehensive virtual environment, where the project could be effectively planned and designed. That can help to reduce design errors and miscommunication, which, in turn, reduces project risks. Moreover, geological and geographical analyses help to identify geohazards and environmentally sensitive regions. The proposed model facilitates highway alignment planning by providing a cross-disciplinary approach to close the gap between the infrastructural and geotechnical planning processes.

The operation and maintenance (O&M) of buildings plays an important role in ensuring that the buildings work normally, as well as reducing the damage caused by functional errors. There are obvious problems in the traditional O&M modality, and an effective way to solve them is to make the model smarter. In this paper, a digital twin framework for building operation is proposed, which consists of two key components: a digital twin O&M model and a machine learning algorithm. The process of establishing the digital twin model is introduced in detail, and the method is explained according to the structure, equipment, and energy consumption characteristics of the model. A mechanism of fusing the digital twin and machine learning algorithm is proposed and the prediction process based on an artificial neural network (ANN) is shown. Finally, based on a systematic summary of the modeling process and fusion mechanism, the development path and overall structure of the intelligent O&M system utilizing digital twins is proposed.

Under the background of global warming and the energy crisis, the Chinese government has set the goal of carbon peaking and carbon neutralization. With the rapid development of machine learning, some advanced machine learning algorithms have also been applied to the control and prediction of carbon emissions due to their high efficiency and accuracy. In this paper, the current situation of machine learning applied to carbon emission prediction is studied in detail by means of paper retrieval. It was found that machine learning has become a hot topic in the field of carbon emission prediction models, and the main carbon emission prediction models are mainly based on back propagation neural networks, support vector machines, long short-term memory neural networks, random forests and extreme learning machines. By describing the characteristics of these five types of carbon emission prediction models and conducting a comparative analysis, we determined the applicable characteristics of each model, and based on this, future research ideas for carbon emission prediction models based on machine learning are proposed.

The hoisting of prefabricated components (PCs) is a key step during the construction of prefabricated buildings. Aiming at the problems existing in the control of PC hoisting, an innovative hoisting management system framework based on the digital twin (DT) is established in this paper. The system framework comprehensively utilizes the building information model (BIM) and Internet of Things (IoT) to establish a digital twin model (DTm) for PC hoisting control and uses Dijkstra’s algorithm to conduct hoisting route planning according to the BIM data in the model. Meanwhile, long-range radio (LoRa) technology was used for data acquisition and transmission to monitor the movement state of the PCs in the hoisting process. By testing it in a prefabricated building project, the DT control method was conducted to realize the functions of real-time information collection, hoisting path planning and PC positioning, which proved the feasibility and effectiveness of the method. As a key technology to realize intelligent manufacturing, DT has been widely studied in academia. The DTm of the hoisting process of PCs is established in this study; it improves the level of intelligent management of prefabricated building construction and provides a new idea for intelligent building construction.