Structural Performance Monitoring Using a Dynamic Data-Driven BIM Environment

被引:73
作者
Delgado, Juan Manuel Davila [1 ]
Butler, Liam J. [2 ]
Brilakis, Ioannis [3 ]
Elshafie, Mohammed Z. E. B. [3 ,4 ]
Middleton, Campbell R. [3 ]
机构
[1] Univ West England, Bristol BS16 1QY, Avon, England
[2] Univ Cambridge, Dept Engn, Trumpington St, Cambridge CB2 1PZ, Cambs, England
[3] Univ Cambridge, Dept Engn, Construct Engn & Technol, Trumpington St, Cambridge CB2 1PZ, Cambs, England
[4] Qatar Univ, POB 2713, Doha, Qatar
基金
英国工程与自然科学研究理事会;
关键词
ARTIFICIAL NEURAL-NETWORKS; CONSTRUCTION-INDUSTRY; DESIGN; MANAGEMENT; EXPRESS; IFCOWL;
D O I
10.1061/(ASCE)CP.1943-5487.0000749
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Structural health monitoring data has not been fully leveraged to support asset management due to a lack of effective integration with other data sets. A building information modeling (BIM) approach is presented to leverage structural monitoring data in a dynamic manner. The approach allows for the automatic generation of parametric BIM models of structural monitoring systems that include time-series sensor data, and it enables data-driven and dynamic visualization in an interactive 3D environment. The approach supports dynamic visualization of key structural performance parameters, allows for the seamless updating and long-term management of data, and facilitates data exchange by generating models compliant with industry foundation classes (IFC). A newly constructed bridge near Stafford, United Kingdom, with an integrated fiber-optic sensor-based monitoring system was used to test the capabilities of the developed approach. The case study demonstrated how the developed approach facilitates more intuitive data interpretation; provides a user-friendly interface to communicate with various stakeholders; allows for the identification of malfunctioning sensors, thus contributing to the assessment of monitoring system durability; and forms the basis for a powerful data-driven asset management tool. In addition, this project highlights the potential benefits of investing in the development of data-driven and dynamic BIM environments. (C) 2018 American Society of Civil Engineers.
引用
收藏
页数:17
相关论文
共 73 条
[71]  
SMARSLY K, 2015, P 20 INT C APPL COMP, P200
[72]   An integrated approach for optimum design of bridge decks using genetic algorithms and artificial neural networks [J].
Srinivas, V. ;
Ramanjaneyulu, K. .
ADVANCES IN ENGINEERING SOFTWARE, 2007, 38 (07) :475-487
[73]  
Wang X., 2011, P 28 INT S AUT ROB C, P631