Special Issue "Application of Machine Learning and Artificial Intelligence in NDE and Structural Health Monitoring of Civil Infrastructures"

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 2137

Special Issue Editor

Dr. Kien Dinh
E-Mail Website
Guest Editor
NDT Concrete LLC, Deltona, FL, USA
Interests: non-destructive testing; machine learning; artificial intelligence

Special Issue Information

Dear Colleagues,

Nondestructive evaluation (NDE) and structural health monitoring (SHM) of civil infrastructures usually deal with an extensive amount of data obtained from the sensors employed/deployed. For example, ground-penetrating radar (GPR) technology utilizes antennas to collect a large number of A-scans for concrete bridge deck or high-definition cameras may be used to measure the physical parameters of structures such as the displacement, strain/stress, rotation, vibration, crack, and spalling. While most of such data have conventionally been analyzed by experts in each technology, many studies are being conducted to automate the data analysis using machine learning/artificial intelligence algorithms. In an effort to assemble those studies, MDPI’s Infrastructures journal has proposed and organized this Special Issue. To be specific, this Special Issue will publish study results and research papers that present innovative uses of machine learning/artificial intelligence for processing NDE/SHM data. Additionally, it also encourages papers that provide comprehensive reviews of the literature on this topic.

Dr. Kien Dinh
Guest Editor

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 1600 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

  • machine learning
  • artificial intelligence
  • non-destructive testing
  • non-destructive evaluation
  • structural health monitoring

Published Papers (2 papers)

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Research

Article
Deep-Learning-Based Drive-by Damage Detection System for Railway Bridges
Infrastructures 2022, 7(6), 84; https://doi.org/10.3390/infrastructures7060084 - 14 Jun 2022
Viewed by 743
Abstract
With the ever-increasing number of well-aged bridges carrying traffic loads beyond their intended design capacity, there is an urgency to find reliable and efficient means of monitoring structural safety and integrity. Among different attempts, vibration-based indirect damage identification systems have shown great promise [...] Read more.
With the ever-increasing number of well-aged bridges carrying traffic loads beyond their intended design capacity, there is an urgency to find reliable and efficient means of monitoring structural safety and integrity. Among different attempts, vibration-based indirect damage identification systems have shown great promise in providing real-time information on the state of bridge damage. The fundamental principle in an indirect vibration-based damage identification system is to extract bridge damage signatures from on-board measurements, which also embody vibration signatures from the vehicle and road/rail profile and can be contaminated due to varying environmental and operational conditions. This study presents a numerical feasibility study of a novel data-driven damage detection system using train-borne signals while passing over a bridge with the speed of traffic. For this purpose, a deep Convolutional Neural Network is optimised, trained and tested to detect damage using a simulated acceleration response on a nominal RC4 power car passing over a 15 m simply supported reinforced concrete railway bridge. A 2D train–track interaction model is used to simulate train-borne acceleration signals. Bayesian Optimisation is used to optimise the architecture of the deep learning algorithm. The damage detection algorithm was tested on 18 damage scenarios (different severity levels and locations) and has shown great accuracy in detecting damage under varying speeds, rail irregularities and noise, hence provides promise in transforming the future of railway bridge damage identification systems. Full article
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Article
Predicting the Effects of Climate Change on Water Temperatures of Roode Elsberg Dam Using Nonparametric Machine Learning Models
Infrastructures 2021, 6(2), 14; https://doi.org/10.3390/infrastructures6020014 - 20 Jan 2021
Viewed by 963
Abstract
A nonparametric machine learning model was used to study the behaviour of the variables of a concrete arch dam: Roode Elsberg dam. The variables used were ambient temperature, water temperatures, and water level. Water temperature was measured using twelve thermometers; six thermometers were [...] Read more.
A nonparametric machine learning model was used to study the behaviour of the variables of a concrete arch dam: Roode Elsberg dam. The variables used were ambient temperature, water temperatures, and water level. Water temperature was measured using twelve thermometers; six thermometers were on each flank of the dam. The thermometers were placed in pairs on different levels: avg6 (avg6-R and avg6-L) and avg5 (avg5-R and avg5-L) were on level 47.43 m, avg4 (avg4-R and avg4-L) and avg3 (avg3-R and avg3-L) were on level 43.62 m, and avg2 (avg2-R and avg2-L) and avg1 (avg1-R and avg1-L) were on level 26.23 m. Four neural networks and four random forests were cross-validated to determine their best-performing hyperparameters with the water temperature data. Quantile random forest was the best performer at mtry 7 (Number of variables randomly sampled as candidates at each split) and RMSE (Root mean square error) of 0.0015, therefore it was used for making predictions. The predictions were made using two cases of water level: recorded water level and full dam steady-state at Representative Concentration Pathway (RCP) 4.5 (hot and cold model) and RCP 8.5 (hot and cold model). Ambient temperature increased on average by 1.6 °C for the period 2012–2053 when using recorded water level; this led to increases in water temperature of 0.9 °C, 0.8 °C, and 0.4 °C for avg6-R, avg3-R, and avg1-R, respectively, for the period 2012–2053. The same average temperature increase led to average increases of 0.7 °C for avg6-R, 0.6 °C for avg3-R, and 0.3 °C for avg1-R for a full dam steady-state for the period 2012–2053. Full article
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