Application of BIM technology in construction project cost refinement control and construction energy consumption control

Abstract

Abstract In order to solve the problem of accurate cost control management in the process of engineering construction, this paper applies BIM technology to the whole process of cost management, covering the whole life cycle of the project. Through efficient sharing of information on the BIM collaborative platform, it carries out unimpeded process work and achieves refined management. Aiming at the complexity of the information characteristics that affect the cost and uncertainty of information transmission, an algorithm fusion scheme based on NCM-FPN has been designed. The TOPSIS method is used to integrate the case feature attributes into the cost case information database. By combining the clustering algorithm with practical management experience, the knowledge base of engineering cost control strategies can be established, and the logical relationship of strategy matching can be determined. The inverse cloud algorithm is used to establish the feature cloud, determine the fuzzy Petri network structure based on generative rules and design the inference algorithm to establish an intelligent model for strategy matching so as to realize the accurate mapping and rapid response from the target case features to the control strategies. Through the analysis of construction energy consumption, it is concluded that the construction energy consumption of different structure buildings is not the same; the construction energy consumption per unit area of brick structure, steel structure residence and frame office building is 0.1659GJ/m2, 0.2706GJ/m2 and 0.3104GJ/m2 respectively, and the largest is the shear-wall structure residence is 0.3381GJ/m2 for the 2.04 times of the brick structure. Based on BIM technology, the cost of dynamic control engineering for construction projects was reduced by 5.4 million. Therefore, BIM is an important technical means to promote the whole process control of project cost, from empirical control to informationization, standardization, and intelligent control.