A well building design support system can not only meet the rules but also automatically recommend the appropriate alternatives for designers, but most modifications now are conducted in the manual way. Although the method of automatic rule checking can effectively identify the compliance of rules in Building Information Modeling (BIM) models, recommendation supports are still lacked in applications. This paper aims to propose a design support system, using automatic rule checking to identify the compliance of rules and adopting case-based reasoning to provide recommendations via ontology and semantics. The AHP-TOPSIS (Analytic hierarchy process-Technique for Order Preference by Similarity to an Ideal Solution) method is used to give reliable recommendations rank. A real case is adopted as an illustrative example. Results show that the proposed system can increase the design efficiency in both design checking and modifying. Similar applications can be extended to other fields and rules.

Risk management of deep excavation is always an important issue. One of the core problems is to accurately simulate and predict the time series of displacement collected from the site sensors to monitor the risk variation. Meanwhile, the applications of building information modelling (BIM) and geographic information system (GIS) can integrate the construction structures into the surrounding environment, visualizing various information and supporting decision making for risk treatment. Therefore, this paper proposes a risk management system to monitor the risk variation for deep excavation based on optimized grey Verhulst model (GVMm), BIM-3DGIS framework and risk monitoring. The grey Verhulst model (GVM) has demonstrated well performance on saturation curve, such as displacement of deep excavation. This paper establishes the GVMm by improving GVM to predict the displacement more precisely. BIM-3DGIS framework is also built by integrating BIM and 3DGIS in the application level for the efficiency of system operation and the interaction with the risk management platform. BIM-3DGIS framework, working with the risk management platform, can monitor the risk variation of deep excavation effectively and provide visual decision-making supports. A real case of deep excavation is used as an illustrative example to verify the practicability. The results show that the prediction precision of GVMm is better than that of GVM. The application scenarios also demonstrate the effectiveness of the risk management system.

Construction has long been seen as a high-risk industry. Many studies conduct risk management by controlling construction risk indicators, but few studies associate risk indicators with time and space to propose long-term risk management methods. Therefore, this study proposes a dynamic analysis and visual tracking method based on behavior-based safety (BBS). This study establishes a BBS observation checklist and records workers’ unsafe behavior. The risk level of unsafe behavior is then determined by grey clustering model. When a high risk occurs, an improved grey correlation model is used to track key indicators that affect risk. In order to achieve visual risk management, this study develops semantic logic to predefine the relationship between components and space. In schedule simulation of BIM, the key indicators of BBS are transformed into an executable visual inspection between work items, components, and space through ARC. This method makes it easier for construction managers to combine time and space to manage safety risk and to adopt appropriate strategies in a timely manner to improve the efficiency of safety management.

The maintenance of historic buildings requires a systematic approach to construct and reuse maintenance knowledge. Maintenance knowledge of historic buildings has extensive and specific knowledge, but a small number of experts provide most of experience. With the rapid development of building information modelling (BIM), it can facilitate maintenance management of historic buildings. Recently, research on connecting ontology and BIM was discussed to promote building knowledge management (BKM). However, BKM is less applied in historic buildings and connection strength is also less discussed. Connection strength of knowledge mapping can increase the performance of knowledge retrieval. Therefore, this study aims to build connection between maintenance ontology of historic buildings and BIM, and also proposes a cloud model-based knowledge mapping method to evaluate the connection strength. This study uses FMEA (failure mode and effects analysis) to connect ontology and BIM, and the grey relational analysis to evaluate the connection strength. Meanwhile, cloud model is integrated into FMEA to better deal with uncertain information to increase the reliability. This study adopts a real case to valid the practicability. The results show the proposed method can evaluate the connection strengths with uncertain information and obtain the maintenance knowledge more efficiently.

The construction industry has long been seen as a high-risk industry, and the risk evaluation method is the core of safety risk management. Complex construction environments can lead to risk evolution over time, leading to uncertainty in risk assessment. Therefore, it is necessary to establish a risk evaluation method for multi-period group decision, which can also deal with uncertain information reliably. This study defines the risk evaluation indicators for construction safety and adopts the cloud model to deal with the uncertain information of experts’ evaluations. A cloud-based aggregation algorithm is also employed for group decision. Then, a cloud-based Minkowski distance function is proposed to enhance the ability of TOPSIS to deal with the uncertain information. Finally, an optimization algorithm is used to calculate the multi-period comprehensive evaluation value to define the risk priority. A real case is used for demonstration and the results show that the proposed method can effectively deal with the risk evaluation problem of multi-project, multi-period and group decision with uncertain information.

Hybrid gray model is a combination of gray model and other mathematical models to obtain a high precision prediction. It has demonstrated good performance in many applications. However, there is little discussion on the optimal combination of weights among these models. For this purpose, this paper proposes a dynamic weighting hybrid gray model to provide a flexible combination to adapt to both stable and unstable time series. Short, medium and long term modeling numbers are used to verify the reliability of the proposed method. Two illustrative examples are shown to compare the prediction accuracy of the proposed method with that of the classical hybrid gray model and ANN model. Results show that the proposed model is better and is more adaptable to a time series with rapid changes.