Next Issue
Volume 2, September
Previous Issue
Volume 2, March

Infrastructures, Volume 2, Issue 2 (June 2017) – 4 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
Order results
Result details
Select all
Export citation of selected articles as:
Article
An Enhanced Algorithm for Concurrent Recognition of Rail Tracks and Power Cables from Terrestrial and Airborne LiDAR Point Clouds
Infrastructures 2017, 2(2), 8; https://doi.org/10.3390/infrastructures2020008 - 02 Jun 2017
Cited by 14 | Viewed by 5242
Abstract
This study proposes an enhanced algorithm that outperforms the methods developed by the author’s earlier contributions for the recognition of railroad assets from LiDAR point clouds. The algorithm is improved by: (1) making it applicable to railroads with any slope; (2) employing Eigen [...] Read more.
This study proposes an enhanced algorithm that outperforms the methods developed by the author’s earlier contributions for the recognition of railroad assets from LiDAR point clouds. The algorithm is improved by: (1) making it applicable to railroads with any slope; (2) employing Eigen decomposition for the rail seed point selection that makes it independent of the rails’ dimensions; and (3) developing a computationally efficient fully data-driven method (simultaneous identification of rail tracks and contact cables) that is able to process poorly sampled datasets with complicated configurations. The upgraded algorithm is applied to two datasets with quite different point sampling and complexity. First dataset is scanned by a terrestrial system and contains three million points covering 630 m of an inter-city railroad corridor. It presents a simple configuration with nonintersecting straight rail tracks and cables. Second dataset includes 80 m of a complex urban railroad environment comprising curved and merging rail tracks and intersecting cables. It is scanned from an airborne platform and contains 165,000 points. The results indicate that all objects of interest are identified and the average recognition precision and accuracy of both datasets at the point cloud level are greater than 95%. Full article
(This article belongs to the Special Issue Building Information Modelling for Civil Infrastructures)
Show Figures

Graphical abstract

Article
Highway Bridge Infrastructure in the Province of British Columbia (BC), Canada
Infrastructures 2017, 2(2), 7; https://doi.org/10.3390/infrastructures2020007 - 11 May 2017
Cited by 6 | Viewed by 5415
Abstract
Some recent catastrophic impacts on highway bridges around the world have raised concerns for assessing the vulnerability of existing highway bridges in Canada. Rapid aging of bridge infrastructure coupled with increased traffic volume has made it crucial to establish an advanced Bridge Management [...] Read more.
Some recent catastrophic impacts on highway bridges around the world have raised concerns for assessing the vulnerability of existing highway bridges in Canada. Rapid aging of bridge infrastructure coupled with increased traffic volume has made it crucial to establish an advanced Bridge Management System (BMS) for highway bridges. This paper aims at developing a highway bridge inventory for the province of British Columbia (BC) which is critical for efficient assessment of the existing structural health condition of the bridges, predicting their future deterioration, and prioritizing their maintenance and retrofitting works. This inventory is an extensive assemblage of data on highway bridges in BC under the responsibility of the BC Ministry of Transportation and Infrastructure (BC MoT) that includes more than 2500 highway bridges. It includes identification of the most common bridge types along with their location, structural and geometric parameters such as construction materials, bridge length, number of spans, deck width, skew angle, bridge pier, and foundation type, structural health condition rating and construction period. This information is of paramount importance for effective infrastructure management, proper rehabilitation solutions, and efficient design of a Structural Health Monitoring (SHM) and Control System for enhancing structural resilience of highway bridges in BC. Several statistical analyses have been carried out for efficient utilization of the information available in the inventory for further research and analyses, as well as for developing a proper BMS for the province’s bridges. Full article
(This article belongs to the Special Issue Concrete Structures: Present and Future Trends)
Show Figures

Figure 1

Article
Machine Learning and Optimality in Multi Storey Reinforced Concrete Frames
Infrastructures 2017, 2(2), 6; https://doi.org/10.3390/infrastructures2020006 - 03 May 2017
Cited by 3 | Viewed by 7299
Abstract
The present study investigates the potential of the implementation of machine learning techniques in optimized multi storey reinforced concrete frames. The variables that are taken into account in the objective function of the optimization problem are the following: the frame type (frame bay [...] Read more.
The present study investigates the potential of the implementation of machine learning techniques in optimized multi storey reinforced concrete frames. The variables that are taken into account in the objective function of the optimization problem are the following: the frame type (frame bay length optimality) and dimensioning of the cross sections. The objective function has the goal of attaining a minimum cost design based on market data, after a structural analysis of the frames. A number of optimized examples with widely encountered cases of total lengths of frames and with various loadings are presented. Modeling is based on Eurocode 2. Optimization takes place with the use of evolutionary algorithms. The optimized results are subjected to predictive modeling based on neural networks. The objective of the study is to create predictive models with the aim of minimizing the usage of scarce resources. Full article
(This article belongs to the Special Issue Concrete Structures: Present and Future Trends)
Show Figures

Figure 1

Reply
Reply to Giannakos, K. Comment on: Toughness of Railroad Concrete Crossties with Holes and Web Openings. Infrastructures 2017, 2, 3
Infrastructures 2017, 2(2), 5; https://doi.org/10.3390/infrastructures2020005 - 13 Apr 2017
Cited by 7 | Viewed by 4460
Abstract
This paper responds to the discussion [1] by Dr. K. Giannakos over our technical note [2].[...] Full article
Show Figures

Figure 1

Previous Issue
Next Issue
Back to TopTop