Emerging Technologies for Effective and Intelligent Transport Infrastructure Monitoring

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 2861

Special Issue Editors


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Guest Editor
DICEA, Department of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, Italy
Interests: design and construction of road, railways and airport infrastructure; active and passive road safety; road pavements; sustainable mobility; context sensitive design
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E-Mail Website
Guest Editor
Department of Civil, Constructional and Environmental Engineering, University of Rome La Sapienza, Via Eudossiana 18, 00184 Rome, Italy
Interests: road alignment; big data analysis and management; civil and environmental engineering; road infrastructure design; probe vehicles; road safety

Special Issue Information

Dear Colleagues,

Infrastructure monitoring is an essential factor in order to ensure the safety and efficiency of road and railway networks. It is necessary to know the features and control the performances of all the components, especially with regard to crucial ones like bridges, viaducts, tunnels, pavements, rails, walls, roadsides, drainage systems, and so on. Inspection activities are needed to determine the state of health of the asset and to guide decision-making processes, aligning them with increasingly binding regulatory developments. Nowadays, in order to contribute to surveys and make them more effective, various kinds of smart infrastructure monitoring systems are available; these are generally based on innovative sensors, systems and integrated technologies, and allow us to gather and collect all the data useful for monitoring and analyzing the real state of each infrastructure component. The use of these systems is quickly developing among managers, technicians, researchers, and road authorities, especially with the aim of continuously controlling the decay of infrastructure elements and identifying the most suitable time to activate preventive or curative maintenance actions. In addition, new technologies may also make it possible to significantly reduce traffic disruptions and risks to the public, optimizing the use of resources, minimizing operating costs and enabling the life cycle of assets to be extended.

The topics of interest include, but are not limited to:

  • Online and real-time traffic and infrastructure state data collection;
  • Emerging technologies for monitoring civil structures and infrastructures;
  • Structure inspection (bridges, viaducts, and tunnels);
  • Pavement monitoring sensors (for loads and thermal state monitoring);
  • Railway track analysis;
  • Roadside and road restraint systems inspection;
  • Non-intrusive, high-performance, low-power survey systems;
  • Geo Information Systems (GIS—ordered inventory and monitoring data collection);
  • Methodologies for data processing and analysis;
  • Use of Artificial Intelligence, neural networks, and machine learning;
  • Innovative technologies for sustainable transport infrastructure maintenance;
  • Automated pavement condition evaluations;
  • Bridge management system (BMS);
  • Building information management (BIM).

Dr. Giuseppe Cantisani
Dr. Giulia Del Serrone
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

  • traffic and infrastructure data collection
  • emerging technologies
  • monitoring civil structures and infrastructures
  • inspection (bridges, viaducts, and tunnels)
  • road pavements
  • railway track
  • roadside and road restraint systems
  • survey systems
  • geo information systems (GIS)
  • bridge management system (BMS)
  • building information management (BIM)

Published Papers (3 papers)

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Research

22 pages, 7922 KiB  
Article
Flexible Permeable-Pavement System Sustainability: A Methodology for Stormwater Management Based on PM Granulometry
by Vittorio Ranieri, Stefano Coropulis, Veronica Fedele, Paolo Intini and John Joseph Sansalone
Infrastructures 2024, 9(6), 95; https://doi.org/10.3390/infrastructures9060095 - 11 Jun 2024
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Abstract
Permeable-pavement design methodologies can improve the hydrologic and therefore the environmental benefits of rural and urban roadway systems. By contrast, conventional impervious pavements perturb the hydrologic cycle, altering the relationship between the rainfall loading and runoff response. Impervious pavements create a hydraulically conductive [...] Read more.
Permeable-pavement design methodologies can improve the hydrologic and therefore the environmental benefits of rural and urban roadway systems. By contrast, conventional impervious pavements perturb the hydrologic cycle, altering the relationship between the rainfall loading and runoff response. Impervious pavements create a hydraulically conductive interface for the transport of traffic-generated chemicals and particulate matter (PM), deleteriously impacting their proximate environments. Permeable-pavement systems are countermeasures to mitigate hydrologic, chemical, and PM impacts. However, permeable pavements are not always equally implementable due to costs, PM loadings, and design constraints. A potential solution to facilitate environmental benefits while meeting the traffic load capacity is the combination of two filtration systems placed at the pavement shoulders and/or pedestrian sidewalks: a bituminous-pavement open-graded friction course (BPFC) and an aggregate-filled infiltration trench. This solution is presented in this manuscript together with the methodological framework and the first results of the investigations into designing and validating such a combined system. The research was conducted at the laboratories of the Polytechnic University of Bari and the University of Florida, while an operational and full-scale physical model was constructed in Bari, Italy. The first results presented characterize the PM deposition on public roads based on granulometry (particle size distributions (PSDs) and particle number densities (PNDs)). Samples (n = 16) were collected and analyzed at eight different sites with different land uses, traffic, and pavements from different cities (Bari and Taranto, Italy). The PM analysis showed similar distributions (PSDs and PNDs), except for two samples. The gravimetric-based PSDs of the PM had granulometric distributions in the sand-size range. In contrast, the PNDs, modeled by a Power Law Model (PLM) (R2 ≥ 0.92), illustrated an exponentially increasing number of particles in the fine silt and clay-size range, representing less than 10% of the PSD mass. Moreover, the results indicate that PM sourced from permeable-pavement systems has differing impacts on the pavement service life. Full article
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21 pages, 7785 KiB  
Article
Experiences Using MEMS Accelerometers on Railway Bearers at Switches and Crossings to Obtain Displacement—Awkward Situations
by Jou-Yi Shih, Paul Weston, Mani Entezami, Clive Roberts and Mark O’Callaghan
Infrastructures 2024, 9(6), 91; https://doi.org/10.3390/infrastructures9060091 - 28 May 2024
Viewed by 486
Abstract
A sleeper, or more generally a “bearer”, moves vertically under a passing train load. The extent of this motion depends on the static and dynamic load of the train, the train speed, and the support conditions at the bearer and its neighbours. Excessive [...] Read more.
A sleeper, or more generally a “bearer”, moves vertically under a passing train load. The extent of this motion depends on the static and dynamic load of the train, the train speed, and the support conditions at the bearer and its neighbours. Excessive motion, typically from voiding see-sawing, low support stiffness or possibly excessive stiffness, or even too little stiffness, are all of interest to maintainers. Typically, problems arise around transition zones, switches and crossings, but plain track with poor support can also be a problem. Within the last decade, low-cost micro-electro-mechanical system (MEMS) accelerometers have been used to capture the time history of vertical motion for use in condition monitoring. Existing condition monitoring systems often overlook or sometimes even ignore the possibility of problematic data, which seem to be common in monitored locations. It is essential to understand whether such “bad” data require further attention. Three problematic sites are presented, focussing on examples where the acceleration was higher than expected or the computed displacement was not as expected. Potential causes include wheel defects, hammering of the ballast by a hanging bearer, or high acceleration at some structural resonant frequency. The present paper aims to show the challenges of using MEMS accelerometers to collect data for condition monitoring and offers insights into the sort of problematic data that may be collected from real sites. Full article
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21 pages, 14599 KiB  
Article
Transport Infrastructure Management Based on LiDAR Synthetic Data: A Deep Learning Approach with a ROADSENSE Simulator
by Lino Comesaña-Cebral, Joaquín Martínez-Sánchez, Antón Nuñez Seoane and Pedro Arias
Infrastructures 2024, 9(3), 58; https://doi.org/10.3390/infrastructures9030058 - 13 Mar 2024
Viewed by 1527
Abstract
In the realm of transportation system management, various remote sensing techniques have proven instrumental in enhancing safety, mobility, and overall resilience. Among these techniques, Light Detection and Ranging (LiDAR) has emerged as a prevalent method for object detection, facilitating the comprehensive monitoring of [...] Read more.
In the realm of transportation system management, various remote sensing techniques have proven instrumental in enhancing safety, mobility, and overall resilience. Among these techniques, Light Detection and Ranging (LiDAR) has emerged as a prevalent method for object detection, facilitating the comprehensive monitoring of environmental and infrastructure assets in transportation environments. Currently, the application of Artificial Intelligence (AI)-based methods, particularly in the domain of semantic segmentation of 3D LiDAR point clouds by Deep Learning (DL) models, is a powerful method for supporting the management of both infrastructure and vegetation in road environments. In this context, there is a lack of open labeled datasets that are suitable for training Deep Neural Networks (DNNs) in transportation scenarios, so, to fill this gap, we introduce ROADSENSE (Road and Scenic Environment Simulation), an open-access 3D scene simulator that generates synthetic datasets with labeled point clouds. We assess its functionality by adapting and training a state-of-the-art DL-based semantic classifier, PointNet++, with synthetic data generated by both ROADSENSE and the well-known HELIOS++ (HEildelberg LiDAR Operations Simulator). To evaluate the resulting trained models, we apply both DNNs on real point clouds and demonstrate their effectiveness in both roadway and forest environments. While the differences are minor, the best mean intersection over union (MIoU) values for highway and national roads are over 77%, which are obtained with the DNN trained on HELIOS++ point clouds, and the best classification performance in forested areas is over 92%, which is obtained with the model trained on ROADSENSE point clouds. This work contributes information on a valuable tool for advancing DL applications in transportation scenarios, offering insights and solutions for improved road and roadside management. Full article
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