Enhance the Simulation of Architecture and Engineering Design Process: A Data-Driven Based Approach

被引:0
|
作者
Hou, Yu [1 ]
Soibelman, Lucio [1 ]
Jin, Yan [2 ]
机构
[1] Univ Southern Calif, Sonny Astani Dept Civil & Environm Engn, Los Angeles, CA 90089 USA
[2] Univ Southern Calif, Dept Aerosp & Mech Engn, Los Angeles, CA 90089 USA
来源
COMPUTING IN CIVIL ENGINEERING 2019: VISUALIZATION, INFORMATION MODELING, AND SIMULATION | 2019年
关键词
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Virtual design team ( VDT) is one of the architecture and engineering ( A&E) design planning and control methods. Current VDT software is not data-driven like most available methods, and its simulation is based on questionnaires and on designers' experiences. Managers may end up making decisions using limited information because there is no simple process that allows them to become familiar with all team members' performances. There is a need to investigate the benefits of a data-driven approach that supports design process simulations by using A&E designers' performance parameters. This study explored the performance parameters of a designers' performance statistics, being how early they started tasks. Designers' outputs for the same tasks can be different when they are working for different clients and managers. Designers' performances described and extracted from data acquired from past projects allow for the customization of design simulations by changing their inputs and parameters according to specific project characteristics. The comparison between original simulations and simulations with features extracted from an existing A&E database revealed that a process simulation that is data-driven can improve the accuracy of the simulation to help better plan and control the design process.
引用
收藏
页码:626 / 634
页数:9
相关论文
共 50 条
  • [31] DATA-DRIVEN ENGINEERING DESIGN RESEARCH: OPPORTUNITIES USING OPEN DATA
    Parraguez, Pedro
    Maier, Anja
    DS87-7 PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE ON ENGINEERING DESIGN (ICED 17), VOL 7: DESIGN THEORY AND RESEARCH METHODOLOGY, 2017, : 41 - 50
  • [32] Towards a Software Engineering Process for Developing Data-Driven Applications
    Hesenius, Marc
    Schwenzfeier, Nils
    Meyer, Ole
    Koop, Wilhelm
    Gruhn, Volker
    2019 IEEE/ACM 7TH INTERNATIONAL WORKSHOP ON REALIZING ARTIFICIAL INTELLIGENCE SYNERGIES IN SOFTWARE ENGINEERING (RAISE 2019), 2019, : 35 - 41
  • [33] MACHINE INSTRUCTIONS OF A DATA-DRIVEN ARCHITECTURE - DESIGN CONSIDERATIONS.
    Azaria, Helnye
    Microprocessing and Microprogramming, 1986, 20 (1-3): : 99 - 105
  • [34] Data-driven architecture based on pipelined thread processing
    Saitoh, Tohru
    Asada, Katsuhiko
    Systems and Computers in Japan, 1997, 28 (13) : 27 - 35
  • [35] The PAG Crowd: A Graph Based Approach for Efficient Data-Driven Crowd Simulation
    Charalambous, P.
    Chrysanthou, Y.
    COMPUTER GRAPHICS FORUM, 2014, 33 (08) : 95 - 108
  • [36] Social Simulation: The Need of Data-Driven Agent-Based Modelling Approach
    Sajjad, Mazhar
    Singh, Karandeep
    Paik, Euihyun
    Ahn, Chang-Won
    2016 18TH INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATIONS TECHNOLOGY (ICACT) - INFORMATION AND COMMUNICATIONS FOR SAFE AND SECURE LIFE, 2016, : 818 - 821
  • [37] A Data-Driven Process Monitoring Approach with Disturbance Decoupling
    Luo, Hao
    Li, Kuan
    Huo, Mingyi
    Yin, Shen
    Kaynak, Okyay
    PROCEEDINGS OF 2018 IEEE 7TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS), 2018, : 569 - 574
  • [38] A Radar-Nearest-Neighbor based data-driven approach for crowd simulation
    Zhao, Xuedan
    Zhang, Jun
    Song, Weiguo
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2021, 129
  • [39] Unveiling precision: a data-driven approach to enhance photoacoustic imaging with sparse data
    Huang, Mengyuan
    Liu, Wu
    Sun, Guocheng
    Shi, Chaojing
    Liu, Xi
    Han, Kaitai
    Liu, Shitou
    Wang, Zijun
    Xie, Zhennian
    Guo, Qianjin
    BIOMEDICAL OPTICS EXPRESS, 2024, 15 (01) : 28 - 43
  • [40] Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design
    Gyory, Joshua T.
    Soria Zurita, Nicolas F.
    Martin, Jay
    Balon, Corey
    McComb, Christopher
    Kotovsky, Kenneth
    Cagan, Jonathan
    JOURNAL OF MECHANICAL DESIGN, 2022, 144 (02)