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Special Issue "Smart City and Smart Infrastructure"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 November 2019.

Special Issue Editors

Prof. Dr. Jong-Jae Lee
E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Sejong University, Seoul, Korea
Tel. +82-2-3408-3290
Interests: smart structures; smart sensors; signal/image processing; pattern recognition; machine learning
Assoc. Prof. Dr. Sung-Han Sim
E-Mail Website
Guest Editor
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
Interests: structural dynamics; structural health monitoring; computer vision and deep learning for infrastructure monitoring; system identification

Special Issue Information

Dear Colleagues,

The rapid development of sensor technologies accelerates the construction of smart cities and smart infrastructures. Sensors can transform cities and their infrastructure into truly smart systems by providing essential information for their intelligent functioning and decision making. Over the past decade, tremendous research efforts have been devoted to developing a wide variety of smart sensors and associated data processing strategies, showing great potential in realizing the concept of smart cities and smart infrastructure. For example, smart sensors not only provide measurements of structural and environmental responses, but also assess structural health to assist with infrastructure maintenance. Furthermore, sensor systems are the key components of smart cities, such as management systems for water, energy, waste, air quality, and transportation. In this regard, the Special Issue, entitled “Smart City and Smart Infrastructure” aims to provide relevant information on recent research, development, and application activities on advanced technologies applicable for smart cities and smart infrastructures.

The theme of this Special Issue includes but is not limited to sensor development, information processing, pattern recognition, artificial intelligence, augmented/virtual reality, sensor-based automation, robotics, etc. Sensor development and the advanced processing of sensor data, which are the fundamental enablers of smart cities and smart infrastructures, are of most interest for this Special Issue.

Prof. Dr. Jong-Jae Lee
Assoc. Prof. Dr. Sung-Han Sim
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 papers will be 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. Sensors is an international peer-reviewed open access semimonthly 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

  • smart sensors
  • signal, image, information processing
  • pattern recognition
  • city and infrastructure monitoring
  • artificial intelligence
  • computer vision applications for defect identification and monitoring
  • sensor applications for infrastructure safety monitoring
  • sensor-based management system for smart cities
  • energy harvesting for long-term monitoring using smart sensors
  • structural and geotechnical sensors
  • risk analysis of smart city and infrastructure
  • structural damage prognosis
  • big data-driven sensor technologies for smart cities

Published Papers (5 papers)

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Research

Open AccessArticle
Bayesian Prediction of Pre-Stressed Concrete Bridge Deflection Using Finite Element Analysis
Sensors 2019, 19(22), 4956; https://doi.org/10.3390/s19224956 - 14 Nov 2019
Abstract
Vertical deflection has been emphasized as an important safety indicator in the management of railway bridges. Therefore, various standards and studies have suggested physics-based models for predicting the time-dependent deflection of railway bridges. However, these approaches may be limited by model errors caused [...] Read more.
Vertical deflection has been emphasized as an important safety indicator in the management of railway bridges. Therefore, various standards and studies have suggested physics-based models for predicting the time-dependent deflection of railway bridges. However, these approaches may be limited by model errors caused by uncertainties in various factors, such as material properties, creep coefficient, and temperature. This study proposes a new Bayesian method that employs both a finite element model and actual measurement data. To overcome the limitations of an imperfect finite element model and a shortage of data, Gaussian process regression is introduced and modified to consider both, the finite element analysis results and actual measurement data. In addition, the probabilistic prediction model can be updated whenever additional measurement data is available. In this manner, a probabilistic prediction model, that is customized to a target bridge, can be obtained. The proposed method is applied to a pre-stressed concrete railway bridge in the construction stage in the Republic of Korea, as an example of a bridge for which accurate time-dependent deflection is difficult to predict, and measurement data are insufficient. Probabilistic prediction models are successfully derived by applying the proposed method, and the corresponding prediction results agree with the actual measurements, even though the bridge experienced large downward deflections during the construction stage. In addition, the practical uses of the prediction models are discussed. Full article
(This article belongs to the Special Issue Smart City and Smart Infrastructure)
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Open AccessArticle
Design and Optimization of an MFL Coil Sensor Apparatus Based on Numerical Survey
Sensors 2019, 19(22), 4869; https://doi.org/10.3390/s19224869 - 08 Nov 2019
Abstract
In this study, we aimed to design a coil sensor prototype capable of detecting metallic area loss based on numerical simulations using the magnetic flux leakage (MFL) method. Unlike previous numerical simulation-based studies, which are only conducted to obtain the MFL itself, the [...] Read more.
In this study, we aimed to design a coil sensor prototype capable of detecting metallic area loss based on numerical simulations using the magnetic flux leakage (MFL) method. Unlike previous numerical simulation-based studies, which are only conducted to obtain the MFL itself, the main objectives of this study were (1) to acquire the induced current in the coil sensor and (2) to optimize the apparatus based on a time-dependent numerical analysis. As a result, the optimum values of parameters in magnetizing and sensing units were obtained numerically. A magnetic sensor prototype was then fabricated using the optimum parameters obtained by numerical parametric study. Finally, experimental validation tests were conducted on a solid steel rod specimen with a stepwise cross-sectional reduction flaw. It was observed that numerical simulation had approximately 91% precision compared to the experimental test. The results reveal that application of a realistic numerical simulation of an MFL coil sensor can probably provide essential information for MFL-sensor fabrication and allows for preventive measures to be taken before manufacturing failure or defect misdetection. Full article
(This article belongs to the Special Issue Smart City and Smart Infrastructure)
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Open AccessArticle
Intelligent Image-Based Railway Inspection System Using Deep Learning-Based Object Detection and Weber Contrast-Based Image Comparison
Sensors 2019, 19(21), 4738; https://doi.org/10.3390/s19214738 - 31 Oct 2019
Abstract
For sustainable operation and maintenance of urban railway infrastructure, intelligent visual inspection of the railway infrastructure attracts increasing attention to avoid unreliable, manual observation by humans at night, while trains do not operate. Although various automatic approaches were proposed using image processing and [...] Read more.
For sustainable operation and maintenance of urban railway infrastructure, intelligent visual inspection of the railway infrastructure attracts increasing attention to avoid unreliable, manual observation by humans at night, while trains do not operate. Although various automatic approaches were proposed using image processing and computer vision techniques, most of them are focused only on railway tracks. In this paper, we present a novel railway inspection system using facility detection based on deep convolutional neural network and computer vision-based image comparison approach. The proposed system aims to automatically detect wears and cracks by comparing a pair of corresponding image sets acquired at different times. We installed line scan camera on the roof of the train. Unlike an area-based camera, the line scan camera quickly acquires images with a wide field of view. The proposed system consists of three main modules: (i) image reconstruction for registration of facility positions, (ii) facility detection using an improved single shot detector, and (iii) deformed region detection using image processing and computer vision techniques. In experiments, we demonstrate that the proposed system accurately finds facilities and detects their potential defects. For that reason, the proposed system can provide various advantages such as cost reduction for maintenance and accident prevention. Full article
(This article belongs to the Special Issue Smart City and Smart Infrastructure)
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Open AccessArticle
Realtime Tracking of Passengers on the London Underground Transport by Matching Smartphone Accelerometer Footprints
Sensors 2019, 19(19), 4184; https://doi.org/10.3390/s19194184 - 26 Sep 2019
Abstract
Passengers travelling on the London underground tubes currently have no means of knowing their whereabouts between stations. The challenge for providing such service is that the London underground tunnels have no GPS, Wi-Fi, Bluetooth, or any kind of terrestrial signals to leverage. This [...] Read more.
Passengers travelling on the London underground tubes currently have no means of knowing their whereabouts between stations. The challenge for providing such service is that the London underground tunnels have no GPS, Wi-Fi, Bluetooth, or any kind of terrestrial signals to leverage. This paper presents a novel yet practical idea to track passengers in realtime using the smartphone accelerometer and a training database of the entire London underground network. Our rationales are that London tubes are self-driving transports with predictable accelerations, decelerations, and travelling time and that they always travel on the same fixed rail lines between stations with distinctive bumps and vibrations, which permit us to generate an accelerometer map of the tubes’ movements on each line. Given the passenger’s accelerometer data, we identify in realtime what line they are travelling on and what station they depart from, using a pattern-matching algorithm, with an accuracy of up to about 90% when the sampling length is equivalent to at least 3 station stops. We incorporate Principal Component Analysis to perform inertial tracking of passengers’ positions along the line when trains break away from scheduled movements during rush hours. Our proposal was painstakingly assessed on the entire London underground, covering approximately 940 km of travelling distance, spanning across 381 stations on 11 different lines. Full article
(This article belongs to the Special Issue Smart City and Smart Infrastructure)
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Open AccessArticle
In-Situ Data-Driven Buffeting Response Analysis of a Cable-Stayed Bridge
Sensors 2019, 19(14), 3048; https://doi.org/10.3390/s19143048 - 10 Jul 2019
Abstract
To analytically evaluate buffeting responses, the analysis of wind characteristics such as turbulence intensity, turbulence length, gust, and roughness coefficient must be a priority. The analytical buffeting response is affected by the static aerodynamic force coefficient, flutter coefficient, structural damping ratio, aerodynamic damping [...] Read more.
To analytically evaluate buffeting responses, the analysis of wind characteristics such as turbulence intensity, turbulence length, gust, and roughness coefficient must be a priority. The analytical buffeting response is affected by the static aerodynamic force coefficient, flutter coefficient, structural damping ratio, aerodynamic damping ratio, and natural frequencies of the bridge. The cable-stayed bridge of interest in this study has been used for 32 years. In that time, the terrain conditions around the bridge have markedly changed from the conditions when the bridge was built. Further, the wind environments have varied considerably due to climate change. For these reasons, the turbulence intensity, length, spectrum coefficient, and roughness coefficient of the bridge site must be evaluated from full-scale measurements using a structural health monitoring system. Although the bridge is located on a coastal area, the evaluation results indicated that the wind characteristics of bridge site were analogous to those of open terrain. The buffeting response of the bridge was analyzed using the damping ratios, static aerodynamic force coefficients, and natural frequencies obtained from measured data. The analysis was performed for four cases. Two case analyses were performed by applying the variables obtained from measured data, while two other case analyses were performed based on the Korean Society of Civil Engineers (KSCE) Design Guidelines for Steel Cable Supported Bridges. The calculated responses of each analysis case were compared with the buffeting response measured at wind speeds of less than 25 m/s. The responses obtained by numerical analysis using estimated variables based on full-scale measurements agreed well with the measured buffeting responses measured at wind speeds of less than 25 m/s. Moreover, an extreme wind speed of 44 m/s, corresponding to a recurrence interval of 200 years, was derived from the Gumbel distribution. Therefore, the buffeting responses at wind speeds of 45 m/s were also determined by applying the estimated variables. From these results, management criteria based on measurement data for in-service bridge are determined and each level of management is proposed. Full article
(This article belongs to the Special Issue Smart City and Smart Infrastructure)
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