Zhe Zheng

Intelligent construction (IC) has emerged as a new approach to transforming the architecture, engineering, and construction (AEC) industry through the integration of advanced information technologies such as artificial intelligence (AI) and the Internet of Things (IoT). However, due to its interdisciplinary nature, the relevant documents on IC are diverse and fragmented. To provide a comprehensive understanding of research progress and future opportunities in IC and to offer suggestions for both developing and developed countries, this study employed VOSviewer and Gephi to conduct a comparative review of relevant literature from the 21st century. A keyword search of Web of Science (WOS) identified 2788 relevant documents which were subjected to an overall co-citation and co-authorship analysis. To illustrate the differences between developing and developed countries, China, a representative developing country, was taken as the candidate to be compared with developed countries via a co-occurrence analysis. Differences between China and developed countries in the three sub-directions of IC, research foundation and domain knowledge transformation; information perception, fusion, and decision making; and embodied AI, were qualitatively discussed. Finally, four future research directions were suggested: (1) data fusion and decision-making, (2) improving the accuracy and efficiency of knowledge representation, learning, and utilization, (3) the establishment of large, pre-trained models in the field, and (4) embodied AI for taking actions according to the decisions made. This paper provides an overview of the relevant literature and the IC context for practitioners and scholars in the AEC industry in countries with different levels of development, as well as suggestions for the future development of IC. The findings of this study can serve both academia and industry in promoting IC in the AEC industry.

The collapse of engineering structures can cause significant casualties and have negative social effects. Collapse accident investigation can elucidate the potential causes and mechanisms of the collapse accident, thus remediating future structural collapse and enhancing the resilience. However, there are some obstacles to investigating complicated collapse accidents using conventional methods. For example, the out‐syncs between on‐site investigation and simulation analysis are intractable and can make discovering the cause of collapse accidents difficult. Hence, a digital twin‐based investigation method for collapse accidents was proposed. First, basic virtual digital building models are established using real‐world information. Then, after mapping the data from the real world into the virtual space, the corresponding highly realistic multistage models before and after the building collapse accident are constructed and synchronized. Using the digital twin method, investigators with multidisciplinary knowledge can efficiently integrate, update, and check the models. Finally, the potential collapse mechanism was revealed with the assistance of the corresponding models. To demonstrate the effectiveness of the proposed digital twin‐based investigation method, a real collapse accident investigation is utilized as an example. These results validated our method.

Reliable and high-fidelity virtual ruin scenarios for collapsed buildings are essential for post-earthquake emergency search and rescue training. However, the existing research on the distribution of ruins caused by building collapse is insufficient for supporting post-earthquake rescue training. Therefore, this paper proposes a hybrid framework for simulating building collapse and ruin scenarios, using a finite element (FE) model and a physics engine. Based on this framework, the following methods are proposed: (1) geometric model conversion from the FE model to the physics engine; (2) determination of the initial moment of collapse; and (3) data mapping of the FE simulation results. In addition, a corresponding program, Finite Element Method to Rigid Body Dynamics (FEM2RBD), is developed for the hybrid framework. The proposed framework simulates the entire process of building collapse and the distribution of ruins. The accuracy of the framework is validated using a shaking table test of a three-story reinforced concrete frame. The collapse process and ruin scenario of a real-world library building is simulated as a case study. The results show that the proposed framework combines the advantages of the FE model during the small-deformation stage with the advantages of physics engines during the large-deformation stage. The proposed framework can be valuable in simulating building collapse and ruin scenarios for post-earthquake rescue training.

The outbreak of COVID-19 resulted in severe pressure on the existing medical infrastructure in China. Several Chinese cities began to construct temporary hospitals for the centralized treatment of COVID-19 patients. The harmful exhaust air from the outlets of these hospitals may have a significant adverse impact on the fresh-air intakes and surrounding environment. Owing to the need to rapidly construct these hospitals within 6–10 days, just a few hours are allowed for the analysis of the impact of this exhaust air on the environment. To overcome this difficulty, a high-efficiency simulation framework is proposed in this study. Based on the open-source computational fluid dynamics software, FDS, the proposed framework is adaptive and incorporates building information with different levels of detail during various design phases of the hospital, and has been applied in the design of the Wuhan Huoshenshan Hospital, the first typical COVID-19 temporary hospital in China. According to the simulation results, neither the fresh-air intakes nor the surrounding buildings would be polluted by the harmful air discharged from the air outlets of the Huoshenshan hospital. The proposed simulation framework can provide a reference for the design and overall planning of similar hospitals in China and other affected countries.