2021
DOI: 10.3390/sym13101927
|View full text |Cite
|
Sign up to set email alerts
|

Intelligent Safety Assessment of Prestressed Steel Structures Based on Digital Twins

Abstract: In the development process of intelligent construction, the safety assessment of prestressed steel structures as an important research direction has become more and more attractive in academia. Digital twins (DTs) is the key technology to realize intelligent construction. The virtual and real interaction of the DTs can provide an efficient management and control mechanism for the construction process. This research proposes an intelligent safety assessment method of prestressed steel structures based on DTs. I… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
6
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
10

Relationship

1
9

Authors

Journals

citations
Cited by 23 publications
(12 citation statements)
references
References 29 publications
0
6
0
Order By: Relevance
“…Unsafe events were fed to a bow-tie model for maintenance implementations that were later imported back to the FEM for final analysis. Liu et al (2021b) then extended the application for predictive maintenance. ANN trained by Backpropagation was used in the prediction stage of the DT, permitting the signal of early warnings.…”
Section: Physics-based Modeling Approachesmentioning
confidence: 99%
“…Unsafe events were fed to a bow-tie model for maintenance implementations that were later imported back to the FEM for final analysis. Liu et al (2021b) then extended the application for predictive maintenance. ANN trained by Backpropagation was used in the prediction stage of the DT, permitting the signal of early warnings.…”
Section: Physics-based Modeling Approachesmentioning
confidence: 99%
“…For example, Wang et al [13] proposed a digital twin-driven structural safety control method for a cable network considering spatio-temporal variations, which was able to compare and analyze the geometric information of the construction site with the real-time finite element simulation results to ensure the structural safety during construction. Liu et al [14] carried out an intelligent safety assessment of the steel structure based on digital twins, and achieved the analysis of the safety performance of the structure by constructing a digital twin framework for multidimensional spatial and temporal information fusion. In view of the complex charac-teristics of the construction of super-large underground space structures, the concept of digital twins is introduced, combined with Internet of Things technology, and a twin system architecture that can meet the actual needs of the project is proposed to improve the level of intelligent structural construction safety monitoring and ensure that the underground space structure is in a safe state during construction.…”
Section: Establishment Of Digital Twin System Frameworkmentioning
confidence: 99%
“…The availability of robotics on site preparation (e.g., material mapping and localization) [121,122] P03.06 Utilization of GIS for site selection (e.g., land development, selecting soil investigation for proper location) [7,123] P04 Design P04.01 Using BIM modeling for the spacing layout [4] P04.02 Implementation of 5D for detailed engineering optimization [124] P04.03 Using big data and analytics for design optimization [125,126] P04.04 Using drones for site localization [127] P04.05 Utilizing digital twins on project design [19,103,128] P04.06 Implementation of AI to capture and assess during preconstruction (e.g., decision-making, price, experience past project performance for contractor) [5,82] P04.07 Integration of virtual reality with design (e.g., enhance efficiency for planning and project design) [8] P04.08 Utilization of laser scanning during design stage (e.g., as-built drawing, clash checks for MEP, electrical work, and improve quality control) [129][130][131] Table A2. Construction management successful factors.…”
Section: Groups and Factors Referencesmentioning
confidence: 99%