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Infrastructures
Special Issues
Intelligent Infrastructures
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Intelligent Infrastructures

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

Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 25117

Special Issue Editors

Dr. Kasthurirangan Gopalakrishnan

E-Mail Website
Guest Editor
Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA 50011, USA
Interests: smart transportation infrastructure; sustainable highways and airport pavements; green technologies; data science; deep learning applications to civil engineering
Dr. Halil Ceylan

E-Mail Website
Guest Editor
Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA 50011, USA
Interests: non-destructive evaluation of infrastructure; intelligent infrastructure; smart health monitoring; transportation infrastructure asset management; mechanistic-based modeling of pavement systems; engineering applications of artificial intelligence
Dr. Sunghwan Kim

E-Mail Website
Guest Editor
Institute for Transportation, Iowa State University, Ames, IA 50011, USA
Interests: roadway infrastructure assessment and modeling; sustainable infrastructure; highway and airport construction; forensic investigation of transportation infrastructure

Special Issue Information

Dear Colleagues,

Safe and efficient civil infrastructure forms the basis of our communities and economies. We are in the historical moment when developing countries create and build new infrastructures and the developed countries are trying to deal with, and gradually replace, the old ones. We seek sustainable, smart and accessible solutions to provide reliability of services.

Agencies in charge of maintaining and managing civil infrastructure across the globe are always faced with budgetary and resource constraints, such that their investments are unable to keep up with the maintenance and management needs of deteriorating infrastructure systems. Thus, there is a need for designing and constructing long-lasting infrastructure as well as cost-effective and reliable (structural and functional) health monitoring systems to ensure that these infrastructure systems do perform optimally the functions they are intended to perform during their service lives. Rapid and significant advancements are being made in the development and implementation of smart technologies and Internet of Things (IoT) in the context of Smart Cities initiatives, including data acquisition, sensing, energy harvesting, wireless communications, big data, machine learning, etc. These technologies have the potential to enable smart infrastructure systems that are not only optimized for multi-functional capabilities, but also help improve the durability and sustainability of infrastructure systems.

We welcome papers that address a broad range of topics focusing on intelligent and multi-functional civil infrastructure, including, but not limited to:

  • Smart technologies for enabling intelligent civil infrastructure
  • Machine learning in the analysis, design, and management of intelligent infrastructure
  • Nano-engineered paved surfaces
  • Achieving ice-free transport infrastructure using electro-conductive concrete
  • Challenges in the field implementation of intelligent transport infrastructure
  • Self-sensing concrete
  • Applications of Intelligent infrastructure smart sensing technologies: MEMS sensors, wireless sensor networks, LiDAR, Unmanned Aerial Systems (UAS), RFID tags, etc.
  • Mechanics of composite multifunctional infrastructures
  • Green technologies in the construction and maintenance of intelligent infrastructure
  • Environmental life-cycle analysis (LCA) and life-cycle cost analysis (LCCA) of intelligent infrastructure

Dr. Kasthurirangan Gopalakrishnan
Dr. Halil Ceylan
Dr. Sunghwan Kim
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

  • sustainable infrastructure
  • intelligent transportation infrastructure
  • smart sensing
  • artificial intelligence applications
  • structural health monitoring
  • advanced nondestructive evaluation

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

24 pages, 3753 KiB  
Article
Deep Reinforcement Learning Algorithms in Intelligent Infrastructure
by Will Serrano
Infrastructures 2019, 4(3), 52; https://doi.org/10.3390/infrastructures4030052 - 16 Aug 2019
Cited by 17 | Viewed by 7916
Abstract
Intelligent infrastructure, including smart cities and intelligent buildings, must learn and adapt to the variable needs and requirements of users, owners and operators in order to be future proof and to provide a return on investment based on Operational Expenditure (OPEX) and Capital [...] Read more.
Intelligent infrastructure, including smart cities and intelligent buildings, must learn and adapt to the variable needs and requirements of users, owners and operators in order to be future proof and to provide a return on investment based on Operational Expenditure (OPEX) and Capital Expenditure (CAPEX). To address this challenge, this article presents a biological algorithm based on neural networks and deep reinforcement learning that enables infrastructure to be intelligent by making predictions about its different variables. In addition, the proposed method makes decisions based on real time data. Intelligent infrastructure must be able to proactively monitor, protect and repair itself: this includes independent components and assets working the same way any autonomous biological organisms would. Neurons of artificial neural networks are associated with a prediction or decision layer based on a deep reinforcement learning algorithm that takes into consideration all of its previous learning. The proposed method was validated against an intelligent infrastructure dataset with outstanding results: the intelligent infrastructure was able to learn, predict and adapt to its variables, and components could make relevant decisions autonomously, emulating a living biological organism in which data flow exhaustively. Full article
(This article belongs to the Special Issue Intelligent Infrastructures)
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16 pages, 4947 KiB  
Article
Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures
by Sattar Dorafshan, Robert J. Thomas and Marc Maguire
Infrastructures 2019, 4(2), 19; https://doi.org/10.3390/infrastructures4020019 - 30 Apr 2019
Cited by 43 | Viewed by 7965
Abstract
This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, [...] Read more.
This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare different edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS. Full article
(This article belongs to the Special Issue Intelligent Infrastructures)
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16 pages, 3551 KiB  
Article
Design and Evaluation of IoT-Enabled Instrumentation for a Soil-Bentonite Slurry Trench Cutoff Wall
by Alan Marchiori, Yadong Li and Jeffrey Evans
Infrastructures 2019, 4(1), 5; https://doi.org/10.3390/infrastructures4010005 - 11 Jan 2019
Cited by 7 | Viewed by 8175
Abstract
In this work, we describe our approach and experiences bringing an instrumented soil-bentonite slurry trench cutoff wall into a modern IoT data collection and visualization pipeline. Soil-bentonite slurry trench cutoff walls have long been used to control ground water flow and contaminant transport. [...] Read more.
In this work, we describe our approach and experiences bringing an instrumented soil-bentonite slurry trench cutoff wall into a modern IoT data collection and visualization pipeline. Soil-bentonite slurry trench cutoff walls have long been used to control ground water flow and contaminant transport. A Raspberry Pi computer on site periodically downloads the sensor data over a serial interface from an industrial datalogger and transmits the data wirelessly to a gateway computer located 1.3 km away using a reliable transmission protocol. The resulting time-series data is stored in a MongoDB database and data is visualized in real-time by a custom web application. The system has been in operation for over two years achieving 99.42% reliability and no data loss from the collection, transport, or storage of data. This project demonstrates the successful bridging of legacy scientific instrumentation with modern IoT technologies and approaches to gain timely web-based data visualization facilitating rapid data analysis without negatively impacting data integrity or reliability. The instrumentation system has proven extremely useful in understanding the changes in the stress state over time and could be deployed elsewhere as a means of on-demand slurry trench cutoff wall structural health monitoring for real-time stress detection linked to hydraulic conductivity or adapted for other infrastructure monitoring applications. Full article
(This article belongs to the Special Issue Intelligent Infrastructures)
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