Special Issue "Digital Model and Data-Driven Bridge Engineering: Plan, Design, Manufacturing, Construction, Safety and Maintenance"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (30 June 2021).

Special Issue Editors

Prof. Dr. Chang-Su Shim
E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, Urban Design and Studies, Chung-Ang University, Seoul, Korea
Interests: 3D digital models for bridges; digital design of concrete structures; 3D printing; performance prediction; BIM-based BMS; prefabricated bridge structures
Prof. Dr. Nashwan Dawood
E-Mail Website
Guest Editor
Centre for Sustainable Engineering, Teesside Univeristy, Middlesbrough TS1 3BX, UK
Interests: BIM technologies and processes; sustainability; information technologies and systems; 5D; VR; integrated databases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digitalization of bridge engineering is a challenging task to combine domain knowledge and various digital technologies. In recent years, BIM (building information modelling) has been widely acknowledged as an essential and inevitable tool for the bridge industry. Information delivery and sharing in real-time is based on digital models. A collaborative work environment with other industries such as manufacturing and ICT requires new communication format. Data-driven engineering is a new way of practice to expand the scope of business in bridge engineering.

Digital design of bridges requires interoperability with design tools for aesthetic and analysis programs. Digital models enable creative design of bridge structures and link to digital manufacturing by 3D printers and robots. DfMA (design for manufacturing and assembly) is a new issue in civil engineering, especially for prefabricated structures. Prefabricated systems need much stricter tolerance control. Digitalization in particular is essential for preassembly and preconstruction of bridge structures. In the bridge construction industry, quality control and machine guidance using scanning appears as a potential alternative to improve productivity in construction.

Digital models with data enable us to deliver information through the whole life cycle of a bridge. LCDIV (life-cycle digital information value) provides an opportunity to innovate bridge engineering by adopting AI and IOT for the operation and maintenance. Accumulated data enable data-driven performance prediction of bridge members. New challenging developments of bridge maintenance system using digital models have been reported. Eventually, these efforts will create digital twin models for bridges, which can lead the bridge industry to the next level.       

The Special Issue, entitled “Digital Model and Data-driven Bridge Engineering: Plan, Design, Manufacturing, Construction, Safety, and Maintenance” offers an opportunity to connect new development outcomes, including theoretical, simulation, experimental studies, and case studies. 

Prof. Dr. Chang-Su Shim
Prof. Dr. Nashwan Dawood
Guest Editors

Manuscript Submission Information

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Keywords

  • Level-3 BIM for bridge engineering
  • Digitalized data-driven design of bridges
  • Interoperability of digital models with analysis
  • DfMA (design for manufacturing and assembly)
  • 3D printing and robot fabrication
  • Information delivery for prefabricated bridge structures
  • Digital-model-based machine guidance
  • Quality control using point cloud
  • Asset information models of bridges
  • Critical damage indicating sensors for bridge maintenance
  • Data-driven performance prediction of bridge structures
  • BMS using digital models
  • Digital twin models of bridges

Published Papers (5 papers)

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Research

Article
Automated Structural Damage Identification Using Data Normalization and 1-Dimensional Convolutional Neural Network
Appl. Sci. 2021, 11(6), 2610; https://doi.org/10.3390/app11062610 - 15 Mar 2021
Cited by 1 | Viewed by 682
Abstract
In the field of structural-health monitoring, vibration-based structural damage detection techniques have been practically implemented in recent decades for structural condition assessment. With the development of deep-learning networks that make automatic feature extraction and high classification accuracy possible, deep-learning-based structural damage detection has [...] Read more.
In the field of structural-health monitoring, vibration-based structural damage detection techniques have been practically implemented in recent decades for structural condition assessment. With the development of deep-learning networks that make automatic feature extraction and high classification accuracy possible, deep-learning-based structural damage detection has been gaining significant attention. The deep-learning neural networks come with fixed input and output size, and input data must be downsampled or cropped to the predetermined input size of the networks to obtain desired output of the network. However, the length of input data (i.e., sensing data) is associated with the excitation quality of a structure, adjusting the size of the input data while maintaining the excitation quality is critical to ensure high accuracy of the deep-learning-based structural damage detection. To address this issue, natural-excitation-technique-based data normalization and the use of 1-D convolutional neural networks for automated structural damage detection are presented. The presented approach converts input data to predetermined size using cross-correlation and uses convolutional network to extract damage-sensitive feature for automated structural damage identification. Numerical simulations were conducted on a simply supported beam model excited by random and traffic loadings, and the performance was validated under various scenarios. The proposed method successfully detected the location of damage on a beam under random and traffic loadings with accuracies of 99.90% and 99.20%, respectively. Full article
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Article
Data-Driven Modeling Algorithms for Cable-Stayed Bridges Considering Mechanical Behavior
Appl. Sci. 2021, 11(5), 2266; https://doi.org/10.3390/app11052266 - 04 Mar 2021
Viewed by 520
Abstract
Digital transformation of bridge engineering utilizes distinct modeling techniques to combine domain knowledge with digital information modeling. In particular, a long-span bridge is a key link in a transportation network, with more than 100 years of service life. BIM (building information modeling) is [...] Read more.
Digital transformation of bridge engineering utilizes distinct modeling techniques to combine domain knowledge with digital information modeling. In particular, a long-span bridge is a key link in a transportation network, with more than 100 years of service life. BIM (building information modeling) is an effort towards improving the current data delivery in the construction industry. However, it is limited by the rigidity that geometry affords; this is particularly problematic when the structure to be modelled is a deformable body. The quality and value of information for the bridges can be enhanced by establishing a data-driven digital information delivery through the entire life-cycle of the bridges. In this study, a data-driven modeling algorithm for cable-stayed bridges is proposed, considering the geometry change determining the mechanical behavior. Data delivery is accomplished by a combination of datasets and algorithms based on the different purposes. The master information model considers alignment of the bridge and essential constraints for the main members, such as stiffening girders, pylons, and cables, between the digital models. Geometry control of the stiffening girders and tension forces of cables are supported by the modeling algorithm of the interoperable target configuration under dead load analysis. The suggested modeling algorithm is verified by comparison with previous analytical studies on cable-stayed bridges. Full article
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Article
Flexural Behavior of Post-Tensioned Concrete Beams with Multiple Internal Corroded Strands
Appl. Sci. 2020, 10(22), 7994; https://doi.org/10.3390/app10227994 - 11 Nov 2020
Cited by 2 | Viewed by 587
Abstract
The corrosion of prestressing steel in prestressed concrete bridges is a critical safety issue. To evaluate the strength of a prestressed concrete beam with corroded strands, it is necessary to know the mechanical properties of the corroded strands in terms of their tensile [...] Read more.
The corrosion of prestressing steel in prestressed concrete bridges is a critical safety issue. To evaluate the strength of a prestressed concrete beam with corroded strands, it is necessary to know the mechanical properties of the corroded strands in terms of their tensile strength and ductility. In this study, material models were suggested using tensile tests of corroded strands which had been taken from existing bridges. Five prestressed concrete beams with multiple internal corroded strands of different corrosion levels and locations were fabricated and tested using the three-point bending test. The beams with corroded strands near the support did not show meaningful flexural behavior changes, while the beams with corrosion in the mid-span showed significant strength reduction. In order to suggest the appropriate evaluation of the flexural strength of a prestressed concrete beam with corroded strands, material models of the corroded strands were divided into two model categories: a bi-linear material model and a brittle material model. Strength evaluations of the corroded prestressed concrete beams according to fps approximation and strain-compatibility using OpenSEES were conducted. Results suggested the use of the strain compatibility method only when the section loss was greater than 5%. Full article
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Article
A Master Digital Model for Suspension Bridges
Appl. Sci. 2020, 10(21), 7666; https://doi.org/10.3390/app10217666 - 29 Oct 2020
Cited by 4 | Viewed by 1207
Abstract
Long-span suspension bridges require accumulated design and construction technologies owing to challenging environmental conditions and complex engineering practices. Building information modeling (BIM) is a technique used to federate essential data on engineering knowledge regarding cable-supported bridges. In this study, a BIM-based master digital [...] Read more.
Long-span suspension bridges require accumulated design and construction technologies owing to challenging environmental conditions and complex engineering practices. Building information modeling (BIM) is a technique used to federate essential data on engineering knowledge regarding cable-supported bridges. In this study, a BIM-based master digital model that uses a data-driven design for multiple purposes is proposed. Information requirements and common data environments are defined considering international BIM standards. A digital inventory for a suspension bridge is created using individual algorithm-based models, and an alignment-based algorithm is used to systematize them and generate the entire bridge system. After assembling the geometrical model, metadata and various BIM applications are linked to create the federated master model, from which the mechanical model is derived for further stages. During the construction stage, the advantage of this digital model lies in its capability to perform efficient revisions and updates with respect to varying situations during the erection process. Stability analyses of the bridge system can be performed continuously at each erection step while considering the geometric control simulation. Furthermore, finite element analysis models for any individual structural member can be extracted from the master digital model, which is aimed at estimating the actual behavior of bridge members. In addition, a pilot master digital model was generated and applied to an existing suspension bridge; this model exhibited significant potential in terms of bridge data generation and manipulation. Full article
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Article
Accelerated System-Level Seismic Risk Assessment of Bridge Transportation Networks through Artificial Neural Network-Based Surrogate Model
Appl. Sci. 2020, 10(18), 6476; https://doi.org/10.3390/app10186476 - 17 Sep 2020
Cited by 5 | Viewed by 913
Abstract
In this study, an artificial neural network (ANN)-based surrogate model is proposed to evaluate the system-level seismic risk of bridge transportation networks efficiently. To estimate the performance of a network, total system travel time (TSTT) was introduced as a performance index, and an [...] Read more.
In this study, an artificial neural network (ANN)-based surrogate model is proposed to evaluate the system-level seismic risk of bridge transportation networks efficiently. To estimate the performance of a network, total system travel time (TSTT) was introduced as a performance index, and an ANN-based surrogate model was incorporated to evaluate a high-dimensional network with probabilistic seismic hazard analysis (PSHA) efficiently. To generate training data, the damage states of bridge components were considered as the input training data, and TSTT was selected as output data. An actual bridge transportation network in South Korea was considered as the target network, and the entire network map was reconstructed based on geographic information system data to demonstrate the proposed method. For numerical analysis, the training data were generated based on epicenter location history. By using the surrogate model, the network performance was estimated for various earthquake magnitudes at the trained epicenter with significantly-reduced computational time cost. In addition, 20 historical epicenters were adopted to confirm the robustness of the epicenter. Therefore, it was concluded that the proposed ANN-based surrogate model could be used as an alternative for efficient system-level seismic risk assessment of high-dimensional bridge transportation networks. Full article
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