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Special Issue "Advances in Sensing Systems for Assessment and Health Monitoring of Transport Infrastructures and Construction Materials"

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

Deadline for manuscript submissions: 15 October 2020.

Special Issue Editors

Prof. Dr. Andrea Benedetto
Website
Guest Editor
Department of Engineering, Roma Tre University, Rome, Italy
Interests: ground-penetrating radar; signal processing; remote sensing; deflection-based assessment methods; non-destructive testing; modeling and simulation; road safety and highway engineering; driving simulation; civil engineering
Special Issues and Collections in MDPI journals
Prof. Dr. Imad Al-Qadi
Website
Guest Editor
Department of Civil and Environmental Engineering, Illinois Center for Transportation, University of Illinois at Urbana-Champaign, Urbana, USA
Interests: ground-penetrating radar; signal processing; modeling and simulation; non-destructive testing; airfield and highway pavement engineering; construction materials; civil engineering
Prof. Dr. Amir M. Alani
Website
Guest Editor
School of Computing and Engineering, University of West London (UWL), London, United Kingdom
Interests: ground-penetrating radar; signal processing; remote sensing; civil engineering; soil engineering; forestry engineering
Special Issues and Collections in MDPI journals
Prof. Dr. Andreas Loizos
Website
Guest Editor
Laboratory of Pavement Engineering, National Technical University of Athens (NTUA), Athens, Greece
Interests: ground-penetrating radar; deflection-based assessment methods; fiber-optic sensors; pavement and material engineering; roadway and airfield pavement evaluation; non-destructive testing; civil engineering
Prof. Dr. Fabio Tosti
Website
Guest Editor
School of Computing and Engineering, University of West London (UWL), London, United Kingdom
Interests: ground-penetrating radar; signal processing; remote sensing; deflection-based methods; numerical simulations; forestry engineering; civil engineering
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a comprehensive overview of state-of-the-art applications, numerical and theoretical developments of sensing techniques within the context of assessment, and health monitoring of transport infrastructures and construction materials. Sensing systems of interest are related to active and passive sensors, analogue and digital sensors, and ground-based, embedded, and remote sensing systems. Sensors based on acoustic, electrical, electromagnetic, chemical, optical, and radioactive principles are considered, amongst others, in both stand-alone and integrated multi-source operating modes. Hence, papers with a focus on areas including, but not limited to, highways, railways, and airfields, as well as construction materials, are encouraged. Review papers in the above outlined research areas will also be considered.

Prof. Dr. Andrea Benedetto
Prof. Dr. Imad Al-Qadi
Prof. Dr. Amir M. Alani
Prof. Dr. Andreas Loizos
Prof. Dr. Fabio Tosti
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 2000 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

  • sensing systems
  • non-destructive testing
  • assessment and health monitoring
  • transport infrastructures
  • highways
  • railways
  • airfields
  • construction materials
  • stand-alone sensors
  • integrated multi-source sensors

Published Papers (4 papers)

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Research

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Open AccessArticle
Fracture Behavior of Permeable Asphalt Mixtures with Steel Slag under Low Temperature Based on Acoustic Emission Technique
Sensors 2020, 20(18), 5090; https://doi.org/10.3390/s20185090 - 07 Sep 2020
Abstract
Acoustic emission (AE), as a nondestructive testing (NDT) and real-time monitoring technique, could characterize the damage evolution and fracture behavior of materials. The primary objective of this paper was to investigate the improvement mechanism of steel slag on the low-temperature fracture behavior of [...] Read more.
Acoustic emission (AE), as a nondestructive testing (NDT) and real-time monitoring technique, could characterize the damage evolution and fracture behavior of materials. The primary objective of this paper was to investigate the improvement mechanism of steel slag on the low-temperature fracture behavior of permeable asphalt mixtures (PAM). Firstly, steel slag coarse aggregates were used to replace basalt coarse aggregates with equal volume at different levels (0%, 25%, 50%, 75%, and 100%). Then, the low-temperature splitting test with slow loading was used to obtain steady crack growth, and the crack initiation and propagation of specimens were monitored by AE technique in real time. From the low-temperature splitting test results, SS-100 (permeable asphalt mixtures with 100% steel slag) has the optimal low-temperature cracking resistance. Therefore, the difference of fracture behavior between the control group (permeable asphalt mixtures without steel slag) and SS-100 was mainly discussed. From the AE test results, a slight bottom-up trend of sentinel function was founded in the 0.6–0.9 displacement level for SS-100, which is different from the control group. Furthermore, the fracture stages of the control group and SS-100 could be divided based on cumulative RA and cumulative AF curves. The incorporation of 100% steel slag reduced the shear events and restrained the growth of shear cracking of the specimen in the macro-crack stage. Finally, the considerable drops of three kinds of b-values in the final phase were found in the control group, but significant repeated fluctuations in SS-100. In short, the fracture behavior of PAM under low temperature was significantly improved after adding 100% steel slag. Full article
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Open AccessArticle
Estimation for Runway Friction Coefficient Based on Multi-Sensor Information Fusion and Model Correlation
Sensors 2020, 20(14), 3886; https://doi.org/10.3390/s20143886 - 13 Jul 2020
Abstract
Friction is a crucial factor affecting air accident occurrence on landing or taking off. Tire–runway friction directly contributes to aircraft stability on land. Therefore, an accurate friction estimation is a rising issue for all stakeholders. This paper summarizes the existing measurement methods, and [...] Read more.
Friction is a crucial factor affecting air accident occurrence on landing or taking off. Tire–runway friction directly contributes to aircraft stability on land. Therefore, an accurate friction estimation is a rising issue for all stakeholders. This paper summarizes the existing measurement methods, and a multi-sensor information fusion scheme is proposed to estimate the friction coefficient between the tire and the runway. Acoustic sensors, optical sensors, tread sensors, and other physical sensors form a sensor system that is used to measure friction-related parameters and fuse them through a neural network. So far, many attempts have been made to link the ground friction coefficient with the aircraft braking friction coefficient. The models that have been developed include the International Runway Friction Index (IRFI), Canada Runway Friction Index (CRFI), and other fitting models. Additionally, this paper attempts to correlate the output of the neural network (estimated friction coefficient) with the correlation model to predict the friction coefficient between the tire and the runway when the aircraft brakes. The sensor system proposed in this paper can be regarded as a mobile weather–runway–tire system, which can estimate the friction coefficient by integrating the runway surface conditions and the tire conditions, and fully consider their common effects. The role of the correlation model is to convert the ground friction coefficient to the grade of the aircraft braking friction coefficient and the information is finally reported to the pilots so that they can make better decisions. Full article
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Open AccessArticle
Experimental Research on Shear Failure Monitoring of Composite Rocks Using Piezoelectric Active Sensing Approach
Sensors 2020, 20(5), 1376; https://doi.org/10.3390/s20051376 - 03 Mar 2020
Cited by 1
Abstract
Underground space engineering structures are generally subject to extensive damages and significant deformation. Given that composite rocks are prone to shear failure, which cannot be accurately monitored, the piezoelectric active sensing method and wavelet packet analysis method were employed to conduct a shear [...] Read more.
Underground space engineering structures are generally subject to extensive damages and significant deformation. Given that composite rocks are prone to shear failure, which cannot be accurately monitored, the piezoelectric active sensing method and wavelet packet analysis method were employed to conduct a shear failure monitoring test on composite rocks in this study. For the experiment, specimens were prepared for the simulation of the composite rocks using cement. Two pairs of piezoelectric smart aggregates (SAs) were embedded in the composite specimens. When the specimens were tested using the direct shear apparatus, an active sensing-based monitoring test was conducted using the embedded SAs. Moreover, a wavelet packet analysis was conducted to compute the energy of the monitoring signal; thus allowing for the determination of the shear damage index of the composite specimens and the quantitative characterization of the shear failure process. The results indicated that upon the shear failure of the composite specimens, the amplitudes and peak values of the monitoring signals decreased significantly, and the shear failure and damage indices of the composite specimens increased abruptly and approached a value of 1. The feasibility and reliability of the piezoelectric active sensing method, with respect to the monitoring of the shear failure of composite rocks, was therefore experimentally demonstrated in this study. Full article
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Review

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Open AccessReview
Vehicle-Assisted Techniques for Health Monitoring of Bridges
Sensors 2020, 20(12), 3460; https://doi.org/10.3390/s20123460 - 19 Jun 2020
Cited by 5
Abstract
Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic [...] Read more.
Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic parameters such as weight of vehicles, axle load, configuration of axles, position of vehicles on the bridge, number of vehicles, direction, and vehicle’s speed. The estimation of traffic loadings on bridges are generally notional and, consequently, can be excessively conservative. Hence, accurate prediction of the in-service performance of a bridge structure is very desirable and great savings can be achieved through the accurate assessment of the applied traffic load in existing bridges. In this paper, a review is conducted on conventional vehicle-based health monitoring methods used for bridges. Vision-based, weigh in motion (WIM), bridge weigh in motion (BWIM), drive-by and vehicle bridge interaction (VBI)-based models are the methods that are generally used in the structural health monitoring (SHM) of bridges. The performance of vehicle-assisted methods is studied and suggestions for future work in this area are addressed, including alleviating the downsides of each approach to disentangle the complexities, and adopting intelligent and autonomous vehicle-assisted methods for health monitoring of bridges. Full article
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