Special Issue "Artificial Intelligence in Infrastructure Geotechnics"

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

Deadline for manuscript submissions: 30 September 2022 | Viewed by 2862

Special Issue Editors

Dr. Joaquim Tinoco
E-Mail Website
Guest Editor
Institute for Sustainability and Innovation in Structural Engineering (ISISE), University of Minho, Braga, Portugal
Interests: geotechnical engineering; soil improvement; soft soils; slopes stability; soft computing; data mining; Artificial Intelligence
Dr. Pijush Samui
E-Mail Website1 Website2
Guest Editor
Department of Civil Engineering, NIT Patna, Bihar, Patna 800005, India
Interests: machine learning, reliability; earthquake engineering; pile foundation; site characterization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Infrastructure geotechnics play a key role in countries’ development, being present on all basic infrastructures, including transportations, water supply, sewers, electrical grids, or telecommunications. Complex geotechnical problems are regularly faced when dealing with these infrastructure projects, for which traditional methods are unable to give the most adequate answer. Alternatively, artificial intelligence techniques are being largely applied in different infrastructure geotechnics problems due to its promising potential to solve high non-linear and complex problems. In fact, in recent decades, there has been a trend to promote interdisciplinary research, where one field poses interesting problems and data, and the other field provides the problem-solving tools (i.e., methods and algorithms).

In this Special Issue, we solicit high-quality original research articles focused on how complex infrastructure geotechnics problems are being solved with the contribution of advanced artificial intelligent algorithms, whether in design or construction, covering the different infrastructure types (e.g., roads, railways, bridges, tunnels, water supply, sewers, electrical grids, telecommunications). We welcome both theoretical and application papers of high technical standard across various disciplines, thus facilitating an awareness of techniques and methods in one area that may be applicable to other areas. We seek high-quality submissions of original research articles as well as review articles on all aspects related to infrastructure geotechnics that have the potential for practical application.

Topics of interest include but are not limited to:

  • Tunnels and deep excavations;
  • Asset management;
  • New construction materials and mixture design;
  • Intelligent constructions;
  • Materials behavior (rock, soil, cementitious mixtures);
  • Site characterization;
  • Soil improvement;
  • Monitoring, surveillance, and field measurement methods;
  • Virtual reality and augmented reality;
  • Advanced design techniques.

Dr. Joaquim Tinoco
Dr. Pijush Samui
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 1600 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

  • infrastructures geotechnics
  • transportations
  • water supply
  • sewers
  • electrical grids
  • telecommunications
  • artificial intelligence
  • machine learning
  • artificial neural networks
  • optimization

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning
Infrastructures 2021, 6(11), 157; https://doi.org/10.3390/infrastructures6110157 - 05 Nov 2021
Viewed by 837
Abstract
Decision support and optimization tools to be used in construction often require an accurate estimation of the cost variables to maximize their benefit. Heavy machinery is traditionally one of the greatest costs to consider mainly due to fuel consumption. These typically diesel-powered machines [...] Read more.
Decision support and optimization tools to be used in construction often require an accurate estimation of the cost variables to maximize their benefit. Heavy machinery is traditionally one of the greatest costs to consider mainly due to fuel consumption. These typically diesel-powered machines have a great variability of fuel consumption depending on the scenario of utilization. This paper describes the creation of a framework aiming to estimate the fuel consumption of construction trucks depending on the carried load, the slope, the distance, and the pavement type. Having a more accurate estimation will increase the benefit of these optimization tools. The fuel consumption estimation model was developed using Machine Learning (ML) algorithms supported by data, which were gathered through several sensors, in a specially designed datalogger with wireless communication and opportunistic synchronization, in a real context experiment. The results demonstrated the viability of the method, providing important insight into the advantages associated with the combination of sensorization and the machine learning models in a real-world construction setting. Ultimately, this study comprises a significant step towards the achievement of IoT implementation from a Construction 4.0 viewpoint, especially when considering its potential for real-time and digital twins applications. Full article
(This article belongs to the Special Issue Artificial Intelligence in Infrastructure Geotechnics)
Show Figures

Figure 1

Article
A Machine-Learning Approach for Extracting Modulus of Compacted Unbound Aggregate Base and Subgrade Materials Using Intelligent Compaction Technology
Infrastructures 2021, 6(10), 142; https://doi.org/10.3390/infrastructures6100142 - 08 Oct 2021
Cited by 3 | Viewed by 664
Abstract
This study presents a rigorous approach for the extraction of the modulus of soil and unbound aggregate base materials for quality management using intelligent compaction (IC) technology. The proposed approach makes use of machine-learning methods in tandem with IC technology and modulus-based spot [...] Read more.
This study presents a rigorous approach for the extraction of the modulus of soil and unbound aggregate base materials for quality management using intelligent compaction (IC) technology. The proposed approach makes use of machine-learning methods in tandem with IC technology and modulus-based spot testing as a local calibration process to estimate the mechanical properties of compacted geomaterials. A calibrated three-dimensional finite element (FE) model that simulates the proof-mapping process of compacted geomaterials was used to develop a comprehensive database of responses of a wide range of single and two-layered geosystems. The database was then used to develop different inverse solvers using artificial neural networks for the estimation of the modulus from the characteristics of the roller and information about the geomaterials. Several instrumented test sites were used for the evaluation and validation of the inverse solvers. The proposed approach was found promising for the extraction of the modulus of compacted geomaterials using IC. The accuracy of the inverse solvers is enhanced if a local calibration process is incorporated as part of a quality management program that includes the use of in situ measurements using modulus-based test devices and laboratory resilient modulus testing. Moreover, compaction uniformity plays a key role in the retrieval of the modulus of geomaterials with certainty. The proposed approach fuses artificial intelligence with mechanistic solutions to position IC as a technology that is well suited for the quality management of compacted materials. Full article
(This article belongs to the Special Issue Artificial Intelligence in Infrastructure Geotechnics)
Show Figures

Figure 1

Article
Rock Strain Prediction Using Deep Neural Network and Hybrid Models of ANFIS and Meta-Heuristic Optimization Algorithms
Infrastructures 2021, 6(9), 129; https://doi.org/10.3390/infrastructures6090129 - 07 Sep 2021
Cited by 4 | Viewed by 784
Abstract
The majority of natural ground vibrations are caused by the release of strain energy accumulated in the rock strata. The strain reacts to the formation of crack patterns and rock stratum failure. Rock strain prediction is one of the significant works for the [...] Read more.
The majority of natural ground vibrations are caused by the release of strain energy accumulated in the rock strata. The strain reacts to the formation of crack patterns and rock stratum failure. Rock strain prediction is one of the significant works for the assessment of the failure of rock material. The purpose of this paper is to investigate the development of a new strain prediction approach in rock samples utilizing deep neural network (DNN) and hybrid ANFIS (adaptive neuro-fuzzy inference system) models. Four optimization algorithms, namely particle swarm optimization (PSO), Fireflies algorithm (FF), genetic algorithm (GA), and grey wolf optimizer (GWO), were used to optimize the learning parameters of ANFIS and ANFIS-PSO, ANFIS-FF, ANFIS-GA, and ANFIS-GWO were constructed. For this purpose, the necessary datasets were obtained from an experimental setup of an unconfined compression test of rocks in lateral and longitudinal directions. Various statistical parameters were used to investigate the accuracy of the proposed prediction models. In addition, rank analysis was performed to select the most robust model for accurate rock sample prediction. Based on the experimental results, the constructed DNN is very potential to be a new alternative to assist engineers to estimate the rock strain in the design phase of many engineering projects. Full article
(This article belongs to the Special Issue Artificial Intelligence in Infrastructure Geotechnics)
Show Figures

Figure 1

Back to TopTop