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Applied Sciences
Special Issues
AI and IoT in Revolutionizing Civil Infrastructure
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AI and IoT in Revolutionizing Civil Infrastructure

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (20 November 2021) | Viewed by 6427

Special Issue Editors

Dr. Ethiopia Nigussie

E-Mail Website
Guest Editor
Department of Future Technologies, University of Turku, Turku, Finland
Interests: internet of things; cybersecurity; self-aware networked systems; embedded systems; ICT4D
Special Issues, Collections and Topics in MDPI journals
Dr. Woubishet Zewdu Taffese

E-Mail Website
Guest Editor
Departmenet of Civil Engineering, Aalto University, Espoo, Finland
Interests: AI; applied machine learning and deep learning; smart infrastructure; sustainable construction materials; concrete
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advancements in sensing and IoT technologies along with AI algorithms are revolutionizing civil infrastructure. The combination of the IoT and AI is paving the way towards smart and sustainable infrastructure by enhancing the efficiency of the construction sector throughout the entire value chain—from the production of construction materials to the design, construction, operation, and maintenance phases.

This Special Issue encourages authors to submit research results covering the role of IoT and AI in the lifecycle of various civil infrastructure (such as buildings, roads, bridges, dam, railway, and sewerage system). Contributions addressing relevant concepts, design, and implementation as well as state-of-the-art review work are welcome. Topics of this Special Issue include, but are not limited to, the following:

  • IoT systems for smart civil infrastructure;
  • Application of computer vision, machine learning, and deep learning;
  • AI and IoT for construction project management;
  • AI and IoT for construction material research and development;
  • Intelligent fault monitoring, detection, and diagnostics;
  • Multi-sensor data fusion for IoT;
  • Autonomous pavement condition assessment.

Dr. Ethiopia Nigussie
Dr. Woubishet Zewdu Taffese
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. Applied Sciences 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 2400 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 infrastructure
  • IoT
  • AI
  • machine learning
  • deep learning
  • multi-sensor fusion
  • structural health monitoring
  • nondestructive testing
  • sensing systems
  • automation in construction
  • damage detection
  • big data analytics
  • computer vision
  • smart city

Published Papers (2 papers)

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Research

25 pages, 9401 KiB  
Article
Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine Learning
by Reventheran Ganasan, Chee Ghuan Tan, Zainah Ibrahim, Fadzli Mohamed Nazri, Muhammad M. Sherif and Ahmed El-Shafie
Appl. Sci. 2021, 11(16), 7700; https://doi.org/10.3390/app11167700 - 21 Aug 2021
Cited by 4 | Viewed by 2664
Abstract
In recent years, researchers have investigated the development of artificial neural networks (ANN) and finite element models (FEM) for predicting crack propagation in reinforced concrete (RC) members. However, most of the developed prediction models have been limited to focus on individual isolated RC [...] Read more.
In recent years, researchers have investigated the development of artificial neural networks (ANN) and finite element models (FEM) for predicting crack propagation in reinforced concrete (RC) members. However, most of the developed prediction models have been limited to focus on individual isolated RC members without considering the interaction of members in a structure subjected to hazard loads, due to earthquake and wind. This research develops models to predict the evolution of the cracks in the RC beam-column joint (BCJ) region. The RC beam-column joint is subjected to lateral cyclic loading. Four machine learning models are developed using Rapidminer to predict the crack width experienced by seven RC beam-column joints. The design parameters associated with RC beam-column joints and lateral cyclic loadings in terms of drift ratio are used as inputs. Several prediction models are developed, and the highest performing neural networks are selected, refined, and optimized using the various split data ratios, number of inputs, and performance indices. The error in predicting the experimental crack width is used as a performance index. Full article
(This article belongs to the Special Issue AI and IoT in Revolutionizing Civil Infrastructure)
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15 pages, 1013 KiB  
Article
Artificial Intelligence for Prediction of Physical and Mechanical Properties of Stabilized Soil for Affordable Housing
by Woubishet Zewdu Taffese and Kassahun Admassu Abegaz
Appl. Sci. 2021, 11(16), 7503; https://doi.org/10.3390/app11167503 - 16 Aug 2021
Cited by 12 | Viewed by 2467
Abstract
Soil stabilization is the alteration of physicomechanical properties of soils to meet specific engineering requirements of problematic soils. Laboratory examination of soils is well recognized as appropriate for examining the engineering properties of stabilized soils; however, they are labor-intensive, time-consuming, and expensive. In [...] Read more.
Soil stabilization is the alteration of physicomechanical properties of soils to meet specific engineering requirements of problematic soils. Laboratory examination of soils is well recognized as appropriate for examining the engineering properties of stabilized soils; however, they are labor-intensive, time-consuming, and expensive. In this work, four artificial intelligence based models (OMC-EM, MDD-EM, UCS-EM+, and UCS-EM−) to predict the optimum moisture content (OMC), maximum dry density (MDD), and unconfined compressive strength (UCS) are developed. Experimental data covering a wide range of stabilized soils were collected from previously published works. The OMC-EM, MDD-EM, and UCS-EM− models employed seven features that describe the proportion and types of stabilized soils, Atterberg limits, and classification groups of soils. The UCS-EM+ model, besides the seven features, employs two more features describing the compaction properties (OMC and MDD). An optimizable ensemble method is used to fit the data. The model evaluation confirms that the developed three models (OMC-EM, MDD-EM, and UCS-EM+) perform reasonably well. The weak performance of UCS-EM− model validates that the features OMC and MDD have substantial significance in predicting the UCS. The performance comparison of all the developed ensemble models with the artificial neural network ones confirmed the prediction superiority of the ensemble models. Full article
(This article belongs to the Special Issue AI and IoT in Revolutionizing Civil Infrastructure)
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