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Infrastructures
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Application of AI for Non-invasive Assessment Methods to Monitor Surface...
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Application of AI for Non-invasive Assessment Methods to Monitor Surface Transportation Infrastructure

A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 9376

Special Issue Editors

Dr. Sattar Dorafshan

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Guest Editor
Department of Civil Engineering, University of North Dakota, Grand Forks, ND, USA
Interests: non-destructive testing; construction; crack detection in concrete
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. S. Sonny Kim

E-Mail Website
Guest Editor
School of Environmental, Civil, Agricultural, and Mechanical Engineering, University of Georgia, Athens, GA 30602, USA
Interests: tidal marsh soils; transportation geotechnics; nondestructive remote sensing and machine learning application in geomaterials
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vistasp M. Karbhari

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Texas at Arlington, 440 Nedderman Hall, 416 Yates St, Box 19308, Arlington, TX 7019, USA
Interests: composite materials; processing science; durability; damage tolerance; mechanics; high temperature
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development and application of artificial intelligence (AI) for interpretation and classification of non-invasive data plays a significant role in robust and unbiased condition assessment of surface transportation infrastructures (STI) such as roads and bridges. The big data obtained from the STI are complex by nature in spectral and spatial variation. Therefore, the analysis of these data are greatly enhanced through the deployment of recent advancements in machine learning (ML) and deep learning (DL). This special issue is dedicated to of the use of AI models to aid infrastructure stakeholders evaluate their assets avoiding traditional methods of testing. Authors are encouraged to submit original technical papers, review papers, and data papers to this special issue. 

Dr. Sattar Dorafshan
Dr. S. Sonny Kim
Prof. Dr. Vistasp Karbhari
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Infrastructures is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • roads
  • bridges
  • machine learning
  • big data
  • nondestructive evaluation
  • nondestructive testing
  • surface transportation infrastructure
  • deep learning
  • defect detection
  • condition assessment
  • health monitoring
  • remote sensing

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Published Papers (3 papers)

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20 pages, 11811 KiB  
Article
Prediction of Strain in Embedded Rebars for RC Member, Application of Hybrid Learning Approach
by Ali Mirzazade, Cosmin Popescu and Björn Täljsten
Infrastructures 2023, 8(4), 71; https://doi.org/10.3390/infrastructures8040071 - 4 Apr 2023
Viewed by 1733
Abstract
The aim of this study was to find strains in embedded reinforcement by monitoring surface deformations. Compared with analytical methods, application of the machine learning regression technique imparts a noteworthy reduction in modeling complexity caused by the tension stiffening effect. The present research [...] Read more.
The aim of this study was to find strains in embedded reinforcement by monitoring surface deformations. Compared with analytical methods, application of the machine learning regression technique imparts a noteworthy reduction in modeling complexity caused by the tension stiffening effect. The present research aimed to achieve a hybrid learning approach for non-contact prediction of embedded strains based on surface deformations monitored by digital image correlation (DIC). However, due to the small training dataset collected by the installed strain gauges, the input dataset was enriched by a semi-empirical equation proposed in a previous study. Therefore, the present study discussed (i) instrumentation by strain gauge and DIC as well as data acquisition and post-processing of the data, accounting for strain gradients on the concrete surface and embedded reinforcement; (ii) input dataset generation for training machine learning regression models approaching hybrid learning; (iii) data regression to predict strains in embedded reinforcement based on monitored surface deformations; and (iv) the results, validation, and post-processing responses to make the method more robust. Finally, the developed model was evaluated through numerous statistical performance measures. The results showed that the proposed method can reasonably predict strain in embedded reinforcement, providing an innovative type of sensing application with highly improved performance. Full article
(This article belongs to the Special Issue Application of AI for Non-invasive Assessment Methods to Monitor Surface Transportation Infrastructure)
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20 pages, 22254 KiB  
Article
Image-Based Corrosion Detection in Ancillary Structures
by Amrita Das, Eberechi Ichi and Sattar Dorafshan
Infrastructures 2023, 8(4), 66; https://doi.org/10.3390/infrastructures8040066 - 28 Mar 2023
Cited by 7 | Viewed by 3297
Abstract
Ancillary structures are essential for highways’ safe operationality but are mainly prone to environmental corrosion. The traditional way of inspecting ancillary structures is manned inspection, which is laborious, time-consuming, and unsafe for inspectors. In this paper, a novel image processing technique was developed [...] Read more.
Ancillary structures are essential for highways’ safe operationality but are mainly prone to environmental corrosion. The traditional way of inspecting ancillary structures is manned inspection, which is laborious, time-consuming, and unsafe for inspectors. In this paper, a novel image processing technique was developed for autonomous corrosion detection of in-service ancillary structures. The authors successfully leveraged corrosion features in the YCbCr color space as an alternative to the conventional red–green–blue (RGB) color space. The proposed method included a preprocessing operation including contrast adjustment, histogram equalization, adaptive histogram equalization, and optimum value determination of brightness. The effect of preprocessing was evaluated against a semantically segmented ground truth as a set of pixel-level annotated images. The false detection rate was higher in Otsu than in the global threshold method; therefore, the preprocessed images were converted to binary using the global threshold value. Finally, an average accuracy and true positive rate of 90% and 70%, respectively, were achieved for corrosion prediction in the YCbCr color space. Full article
(This article belongs to the Special Issue Application of AI for Non-invasive Assessment Methods to Monitor Surface Transportation Infrastructure)
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Other

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23 pages, 8510 KiB  
Data Descriptor
SDNET2021: Annotated NDE Dataset for Subsurface Structural Defects Detection in Concrete Bridge Decks
by Eberechi Ichi, Faezeh Jafari and Sattar Dorafshan
Infrastructures 2022, 7(9), 107; https://doi.org/10.3390/infrastructures7090107 - 23 Aug 2022
Cited by 10 | Viewed by 3632
Abstract
Annotated datasets play a significant role in developing advanced Artificial Intelligence (AI) models that can detect bridge structure defects autonomously. Most defect datasets contain visual images of surface defects; however, subsurface defect data such as delamination which are critical for effective bridge deck [...] Read more.
Annotated datasets play a significant role in developing advanced Artificial Intelligence (AI) models that can detect bridge structure defects autonomously. Most defect datasets contain visual images of surface defects; however, subsurface defect data such as delamination which are critical for effective bridge deck evaluations are typically rare or limited to laboratory specimens. Three Non-Destructive Evaluation (NDE) methods (Infrared Thermography (IRT), Impact Echo (IE), and Ground Penetrating Radar (GPR)) were used for concrete delamination detection and reinforcement corrosion detection. The authors have developed a unique NDE dataset, Structural Defect Network 2021 (SDNET2021), which consists of IRT, IE, and GPR data collected from five in-service reinforced concrete bridge decks. A delamination survey map locating the areas, extent and classes of delamination served as the ground truth for annotating IRT, IE and GPR field tests’ data in this study. The IRT were processed to create an ortho-mosaic maps for each deck and were aligned with the ground truth maps using image registration, affine transformation, image binarization, morphological operations, connected components and region props techniques to execute a semi-automatic pixel–wise annotation. Conventional methods such as Fast Fourier transform (FFT)/peak frequency and B-Scan were used for preliminary analysis for the IE and GPR signal data respectively. The quality of NDE data was verified using conventional Image Quality Assessment (IQA) techniques. SDNET2021 dataset consists of 557 delaminated and 1379 sound IE signals, 214,943 delaminated and 448,159 sound GPR signals, and about 1,718,083 delaminated and 2,862,597 sound IRT pixels. SDNET2021 addresses one of the major gaps in benchmarking, developing, training, and testing advanced deep learning models for concrete bridge evaluation by providing a publicly available annotated and validated NDE dataset. Full article
(This article belongs to the Special Issue Application of AI for Non-invasive Assessment Methods to Monitor Surface Transportation Infrastructure)
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