Purpose: The construction industry is an industry with a high incidence of safety accidents, and the interactions of unsafe behaviors of construction workers are the main cause of accidents. The neglect of the interactions may lead to serious underestimation of safety risks. This research aims to analyze the cascading vulnerability of unsafe behaviors of construction workers from the perspective of network modeling.Design/methodology/approach: An unsafe behavior network of construction workers and a cascading vulnerability analysis model were established based on 296 actual accident cases. The cascading vulnerability of each unsafe behavior was analyzed based on the degree attack strategy.Findings: Complex network with 85 unsafe behavior nodes is established based on the collected accidents in total. The results showed that storing in improper location, does not wear a safety helmet, working with illness and working after drinking are unsafe behaviors with high cascading vulnerability. Coupling analysis revealed that differentiated management strategies of unsafe behaviors should be applied. Besides, more focus should be put on high cascading vulnerability behaviors.Originality/value: This research proposed a method to construct the cascading failure model of unsafe behavior for individual construction workers. The key parameters of the cascading failure model of unsafe behaviors of construction workers were determined, which could provide a reference for the research of cascading failure of unsafe behaviors. Additionally, a dynamic vulnerability research framework based on complex network theory was proposed to analyze the cascading vulnerability of unsafe behaviors. The research synthesized the results of dynamic and static analysis and found the key control nodes to systematically control unsafe construction behaviors.

In the context of the increasingly severe energy crisis and global warming, green buildings and their energy-saving issues are being paid more attention in the world. Since envelope optimization can significantly reduce the energy consumption of green buildings, value engineering (VE) technology and building information modeling (BIM) technology are used to optimize the envelope of green buildings, which takes into account both energy saving and life cycle cost. The theoretical framework of optimization for green building envelope based on BIM-VE is proposed, including a BIM model for architecture, a life cycle cost analysis model, energy-saving analysis model, and a value analysis model. In the life-cycle cost model, a mathematical formula for the life-cycle cost is established, and BIM technology is used to generate a bill of quantity. In the energy-saving analysis model, a mathematical formula for energy saving is established, and BIM technology is used for the building energy simulation. In the scheme decision-making sub-model, VE technology integrating life cycle cost with energy saving is used to assess the envelope schemes and select the optimal one. A prefabricated project case is used to simulate and test the established methodology. The important results show that the 16 envelope schemes make the 16 corresponding designed buildings meet the green building evaluation standards, and the optimal envelope scheme is the “energy-saving and anti-theft door + exterior window 2+ floor 1+ exterior wall 1 + inner shear wall + inner partition wall 2 + planted roof” with the value 10.80 × 10⁻² MW·h/ten thousand yuan. A significant finding is that the value generally rises with the increase of energy-saving rate while the life cycle cost is irregular with the increase of energy-saving rate. Compared with previous efforts in the literature, this study introduces VE technology into architectural design to further expand the current boundary of building energy-saving theory. The findings and suggestions will provide a valuable reference and guidance for the architectural design industry to optimize the envelope of green buildings from the perspective of both energy saving and life cycle cost.

Purpose: The outbreak of COVID-19 pandemic has posed severe challenges to infrastructure construction in China. Particularly, the complex technology and high process uncertainty of deep foundation pit construction make its safety risk identification a challenging issue of general concern. To address these challenges, Building Information Modeling (BIM) can be used as an important tool to enhance communication and decision-making among stakeholders during the pandemic. The purpose of this study is to propose a knowledge management and BIM-integrated safety risk identification method for deep foundation pit construction to improve the management efficiency of project participants.Design/methodology/approach: This paper proposes a risk identification method that integrates BIM and knowledge management for deep foundation pit construction. In the framework of knowledge management, the topological relationships between objects in BIM are extracted and visualized in the form of knowledge mapping. After that, formal expressions of codes are established to realize the structured processing of specification provisions and special construction requirements. A comprehensive plug-in for deep foundation pit construction is designed based on the BIM software.Findings: The proposed method was verified by taking a sub-project in deep foundation pit project construction as an example. The result showed the new method can make full use of the existing specification and special engineering requirements knowledge. In addition, the developed visual BIM plug-in proves the feasibility and applicability of the proposed method, which can help to increase the risk identification efficiency and refinement.Originality/value: The deep foundation pit safety risk identification is challenged by the confusion of deep foundation pit construction safety knowledge and the complexity of the BIM model. By establishing the standardized expression of normative knowledge and special construction requirements, the efficiency and refinement of risk identification are improved while ensuring the comprehensiveness of results. Moreover, the topology-based risk identification method focuses on the project objects and their relations in the way of network, eliminating the problem of low efficiency from the direct BIM-based risk identification method due to massive data.

Due to the growing complexity of mechanical, electrical, and plumbing (MEP) designs and the rules that govern them, performing rule checks manually has become expensive. However, MEP-based rule checking has not received adequate attention compared to automated rule checking in other domains. Based on Knowledge Management and Building Information Modeling (BIM), an automated rule checking system integrated knowledge base management system (KBMS) for model information expansion, information extraction, system integrity checking, and element spacing checking was developed. MEP rules for automated rule checking were collected, optimized, and stored in the MEP knowledge base. The KBMS facilitates the management of MEP rules in the knowledge base. A Revit plug-in of MEP rule checking system was developed including functions of KBMS, Model Integrity Checking, Elements Space Checking, and Locating the non-compliant element in model view. This study integrated both KBMS and BIM technologies to achieve automated rule checking for MEP. This simplifies the process of rule checking of MEP systems in an automated manner.