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BIM technology is an important technology for digitizing construction project management in order to improve management efficiency, and it has a supportive role in predicting the relevant indicators of project management. The article constructs a project management prediction model based on BIM technology that integrates GSVM and EVS. The earned value method is used to calculate the project schedule performance index and cost performance index, and the gray support vector machine is adopted to map the nonlinear data into the high-dimensional space and carry out calculations in order to simplify the complexity of calculations and improve the prediction efficiency and accuracy. The case study shows that the project duration was shortened by 21 days and 242,000 dollars reduced the cost by applying the model of this paper. Structural equations confirm the positive effect of BIM technology and the GSVM-EVM model on the performance of construction project management and the positive effect of BIM technology on the prediction effect of the GSVM-EVM model.
BIM technology is an important technology for digitizing construction project management in order to improve management efficiency, and it has a supportive role in predicting the relevant indicators of project management. The article constructs a project management prediction model based on BIM technology that integrates GSVM and EVS. The earned value method is used to calculate the project schedule performance index and cost performance index, and the gray support vector machine is adopted to map the nonlinear data into the high-dimensional space and carry out calculations in order to simplify the complexity of calculations and improve the prediction efficiency and accuracy. The case study shows that the project duration was shortened by 21 days and 242,000 dollars reduced the cost by applying the model of this paper. Structural equations confirm the positive effect of BIM technology and the GSVM-EVM model on the performance of construction project management and the positive effect of BIM technology on the prediction effect of the GSVM-EVM model.
The construction of green buildings is an important direction for the transformation and development of the construction industry, but it is beset with problems such as a lack of construction experience, immature new technologies, and unstable material properties; these issues bring risks to the construction stage of green buildings, and the coupling of uncertain risk factors in the construction process of green buildings may lead to unfavorable results. The purpose of this study is to explore the coupling degree of green building construction safety risk factors and the changing trend in their coupling combinations at the system risk level. First, the risk factor index system was defined by reading the literature and gathering expert opinions, and the coupling degree between risk factors was measured using an improved coupling degree model. Then, a system dynamics model was established to simulate and analyze the coupling effects among the risk factors and determine the combinations with the greatest influence. The results show that the risk probability is proportional to the risk coupling value, the human–environment coupling value is the largest, and the material equipment–management coupling value is the smallest. The human–environment system simulation shows that reducing the coupling value of system factors will promote a decrease in the total level of system risk. According to the research conclusions, measures to prevent risk coupling are proposed, which offer theoretical references for green building practitioners carrying out risk management; these measures hold a certain guiding significance for the risk control and future development of green buildings.
Material planning is important in construction, for it affects procurement, cost, and schedule. Proper planning of material supply and logistics helps streamline the performance of all tasks through the avoidance of excessive or insufficient material supply. Material planning relies on quantity takeoff (QTO) and project schedules. Conventionally, quantity takeoff was a manual process based on 2D drawings and human interpretation and was error-prone. Presently, with the popularity of Building Information Modelling (BIM), in BIM-based projects, using inbuilt quantity takeoff functions, quantities of work can be generated automatically from BIM models to aid the quantity takeoff. However, if those inbuilt QTO solutions are object-based, then the quantities of works extracted may not meet the requirements of the users in selected cases, e.g., in zone-based construction projects. Also, for estimating daily material requirements, the accuracy of the quantities of work becomes more important, not only for the purpose of efficient planning but also for reducing construction waste. Since works using the same type of material can go overlapping, in addition to estimating the amount of material for each work, the total amount of material for a day must also be calculated. Thus, this research aims to develop a framework for automatic extraction of zone-based concrete volumes and formwork positions for cast-in-place concrete structures using the data in BIM models, followed by linking them with project schedules for estimating daily concrete and formwork requirements. This framework extends the body of knowledge by introducing an innovative algorithm for automatically calculating overlapped areas between concrete members and a rule for naming tasks in the schedule, followed by evaluating the formwork requirements without drawing formwork in a 3D model. A software tool will be developed to achieve the aim, and a case study will be used to validate the proposed framework.
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