2019 Vol.2 Oct. No.5

• Title: BIM-based artificial engineering integration method for building engineering database
• Name: Yogeshwara Subramanian
• Company: Vellore Institute of Technology
• Abstract:

  In order to improve the construction engineering database system and meet the requirements of data processing and integration, a BIM-based construction engineering database artificial intelligence system integration research method was designed. In-depth analysis of the role of artificial intelligence integration method in the construction engineering database system, based on this to further explore the effectiveness of BIM-based construction engineering database artificial intelligence integration method. The experimental verification shows that the BIM-based artificial engineering integration method of building engineering database is more conducive to further improve the efficiency of construction engineering, effectively reduce the design period of building engineering, realize the optimal design of building engineering, and its integration efficiency is about 30% higher than the traditional integration method.

• Keyword: BIM; architectural engineering; artificial intelligence; database; design optimization;
• DOI: 10.12250/jpciams2019050557
• Citation form: Yogeshwara Subramanian.BIM-based artificial engineering integration method for building engineering database[J]. Computer Informatization and Mechanical System, 2019, vol. 2, pp. 15-20.

[1] Zhang Z L, Luo X G, Yu Y, et al. Integration of an improved dynamic ensemble selection approach to enhance one-vs-one scheme[J]. Engineering Applications of Artificial Intelligence, 2018, 74:43-53.

[2] Vriesmann L M, Britto A S, Oliveira L S, et al. Combining overall and local class accuracies in an oracle-based method for dynamic ensemble selection[J]// International Joint Conference on Neural Networks. IEEE, 2015:1-7.

[3] Nabvipelesaraei A, Rafiee S, Mohtasebi S S, et al. Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production.[J]. Science of the Total Environment, 2018, s 631–632:1279-1294.

[4] Ahn B, Kim J, Choi B. Artificial Intelligence-based Machine Learning considering Flow and Temperature of the Pipeline for Leak Early Detection using Acoustic Emission[J]. Engineering Fracture Mechanics, 2018,24(57):267-301.

[5] Wang B, Zhang X, Information D O. Research progress of assessment methods based on artificial intelligence and clinical diagnosis in upper limb rehabilitation[J]. Beijing Biomedical Engineering, 2018,14(67):167-181.

[6] Srivastava P, Khan Y, Bhandari M, et al. Calibrated Simulation Analysis for Integration of Evaporative Cooling and Radiant Cooling System for Different Indian Climatic Zones[J]. Journal of Building Engineering, 2018,18(61):121-127.

[7] Sun Hong, Chen Shiping, Xu Liping. Research on Cloud Computing Modeling Based on Fusion Difference Method and Self-adaptive Threshold Segmentation[J]. International Journal of Pattern Recognition & Artificial Intelligence, 2018,10(21):712-715.

[8] Hamian M, Darvishan A, Hosseinzadeh M, et al. A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm[J]. Engineering Applications of Artificial Intelligence, 2018, 72:203-212.

[9] Jangir P, Jangir N. A new Non-Dominated Sorting Grey Wolf Optimizer (NS-GWO) algorithm: Development and application to solve engineering designs and economic constrained emission dispatch problem with integration of wind power[J]. Engineering Applications of Artificial Intelligence, 2018, 72:449-467.

[10] Li C, Zou W, Zhang N, et al. An evolving T–S fuzzy model identification approach based on a special membership function and its application on pump-turbine governing system[J]. Engineering Applications of Artificial Intelligence, 2018, 69:93-103.