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Artificial Intelligence in Infrastructure Geotechnics

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

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 30077

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Special Issue Editors

Dr. Joaquim Tinoco

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Guest Editor

Department of Civil Engineering, Advanced Production and Intelligent Systems (ARISE), Institute for Sustainability and Innovation in Structural Engineering (ISISE), University of Minho, 4800-058 Guimarães, Portugal
Interests: geotechnical engineering; soft soils; soil improvement; slopes stability; artificial intelligence (AI); neural networks; evolutionary algorithms
Special Issues, Collections and Topics in MDPI journals

Dr. Pijush Samui

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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 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

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

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Published Papers (9 papers)

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17 pages, 4002 KiB

Open AccessArticle

Forecasting the Capacity of Open-Ended Pipe Piles Using Machine Learning

by Baturalp Ozturk, Antonio Kodsy and Magued Iskander

Infrastructures 2023, 8(1), 12; https://doi.org/10.3390/infrastructures8010012 - 9 Jan 2023

Cited by 9 | Viewed by 2711

Abstract

Pile design is an essential component of geotechnical engineering practice, and pipe piles, in particular, are increasingly being used for the support of a variety of infrastructure projects. These piles are being used with dimensions that exceed those used in the development of the most widely used design approaches. At the same time, the growth in pile dimensions calls for the evolution of the state-of-the-art at a similar pace. The objective of this study is to provide an improved prediction of pile capacity. A database of 112 load tests on pipe piles ranging in diameter from 10 to 100 in. (0.25–2.5 m) and in length from 10 to 320 ft. (3–98 m) was employed in this study. First, design capacities were computed using four popular design methods and compared to capacities interpreted from a load test. For the employed dataset, the Revised Lambda method was found to best predict capacities of pipe piles obtained from a load test, among the four examined methods, and was thus employed as a reference standard for assessing the performance of ML methods. Next, eight ML regression models were trained to compute the capacity of pipe piles. Several trained ML models predicted capacities for the testing data set on par with the Revised Lambda method, and three were selected for further investigation. A variety of pile dimensions and soil properties were examined as input properties for ML and the trained models performed surprisingly well with only the pile dimensions used as input. In addition, ML models exhibited satisfactory diameter and length effects, which have been areas of concern for some traditional design approaches. The work thus demonstrates the feasibility of employing machine learning (ML) for determining the capacity of pipe piles. A web application was also developed as a tool for forecasting the capacity of pipe piles using ML. Full article

(This article belongs to the Special Issue Artificial Intelligence in Infrastructure Geotechnics)

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25 pages, 6687 KiB

Open AccessArticle

ANN-Based Assessment of Soft Surface Soil Layers’ Impact on Fault Rupture Propagation and Kinematic Distress of Gas Pipelines

by Nikolaos Makrakis, Prodromos N. Psarropoulos and Yiannis Tsompanakis

Infrastructures 2023, 8(1), 6; https://doi.org/10.3390/infrastructures8010006 - 30 Dec 2022

Cited by 4 | Viewed by 2254

Abstract

Large-scale lifelines in seismic-prone regions very frequently cross areas that are characterized by active tectonic faulting, as complete avoidance might be techno-economically unfeasible. The resulting Permanent Ground Displacements (PGDs) constitute a major threat to such critical infrastructure. The current study numerically investigates the crucial impact of soil deposits, which usually cover the ruptured bedrock, on the ground displacement profile and the kinematic distress of natural gas pipelines. For this purpose, a decoupled numerical methodology, based on Finite Element Method (FEM), is adopted and a detailed parametric investigation is performed for various fault and soil properties. Moreover, the advanced capabilities of Artificial Neural Networks (ANNs) are utilized, aiming to facilitate the fast and reliable assessment of soil response and pipeline strains due to seismic faulting, replacing time-consuming FEM computations. An extensive sensitivity analysis is performed to select the optimal architecture and training algorithm of the employed ANNs for both the geotechnical and structural parts of the decoupled approach, with suitable input and target values related to bedrock offset, fault and soil properties, surface PGDs, and pipeline strains. The proposed ANN-based approach can be efficiently applied by practice engineers in seismic design and route optimization of natural gas pipelines. Full article

(This article belongs to the Special Issue Artificial Intelligence in Infrastructure Geotechnics)

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27 pages, 6326 KiB

Open AccessArticle

Design of an Energy Pile Based on CPT Data Using Soft Computing Techniques

by Pramod Kumar and Pijush Samui

Infrastructures 2022, 7(12), 169; https://doi.org/10.3390/infrastructures7120169 - 12 Dec 2022

Cited by 20 | Viewed by 2659

Abstract

The present study focused on the design of geothermal energy piles based on cone penetration test (CPT) data, which was obtained from the Perniö test site in Finland. The geothermal piles are heat-capacity systems that provide both a supply of energy and structural support to civil engineering structures. In geotechnical engineering, it is necessary to provide an efficient, reliable, and precise method for calculating the group capacity of the energy piles. In this research, the first aim is to determine the most significant variables required to calculate the energy pile capacity, i.e., the pile length (L), pile diameter (D), average cone resistance (

q_{c 0}

), minimum cone resistance (

q_{c 1}

), average of minimum cone resistance (

q_{c 2}

), cone resistance (

q_{c}

), Young’s modulus (

E

), coefficient of thermal expansion (

α_{c}

), and temperature change (

Δ T

). The values of q_c₀, q_c₁, q_c₂, q_c, and E are then employed as model inputs in soft computing algorithms, which includes random forest (RF), the support vector machine (SVM), the gradient boosting machine (GBM), and extreme gradient boosting (XGB) in order to predict the pile group capacity. The developed soft computing models were then evaluated by using several statistical criteria, and the lowest system error with the best performance was attained by the GBM technique. The performance parameters, such as the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), mean biased error (MBE), median absolute deviation (MAD), weighted mean absolute percentage error (WMAPE), expanded uncertainty (U₉₅), global performance indicator (GPI), Theil’s inequality index (TIC), and the index of agreement (IA) values of the testing data for the GBM models are 0.80, 0.10, 0.08, −0.01, 0.06, 0.21, 0.28, −0.00, 0.11, and 0.94, respectively, demonstrating the strength and capacity of this soft computing algorithm in evaluating the pile’s group capacity for the energy pile. Rank analysis, error matrix, Taylor’s diagram, and the reliability index have all been developed to compare the proposed model’s accuracy. The results of this research also show that the GBM model developed is better at estimating the group capacity of energy piles than the other soft computing models. Full article

(This article belongs to the Special Issue Artificial Intelligence in Infrastructure Geotechnics)

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18 pages, 4675 KiB

Open AccessArticle

Physical Model of Shallow Foundation under Dynamic Loads on Sands

by Diego F. Gil, Cristhian C. Mendoza, Luis R. Vásquez-Varela and Santiago Cano

Infrastructures 2022, 7(11), 147; https://doi.org/10.3390/infrastructures7110147 - 25 Oct 2022

Cited by 7 | Viewed by 2764

Abstract

Structures built on sands worldwide, with shallow foundations, have experienced damage and collapse during and after earthquakes. Two phenomena triggered the collapse: the liquefaction phenomenon and the P-Δ effects. However, current research and practice do not fully understand granular soil behavior during liquefaction and P-Δ effects, as proven by the sum of investigations on physical models, constitutive models, and laboratory testing proposals about these topics. A question appears at this point: what is the relationship between excitation frequency, displacement amplitude, and the triggering of overturning? To cope with this issue, the authors propose to create a physical 1-g model composed of a single-degree-of-freedom oscillator (SDOFO) capable of transmitting cyclic loadings to the soil in rocking vibration mode. The measurement methodology was based on computer vision using OpenCV by Python, which allowed the “free movement” of the SDOFO. The authors use computer vision as a suitable way to obtain displacements and times without sensors placed directly in the physical model. According to the results, it was possible to define an inversely non-linear relationship between frequency, displacement amplitude, and the total cycles required to reach overturning for different effective grain-size (D₁₀). Full article

(This article belongs to the Special Issue Artificial Intelligence in Infrastructure Geotechnics)

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24 pages, 3786 KiB

Open AccessArticle

Reliability Analysis of Gravity Retaining Wall Using Hybrid ANFIS

by Rashid Mustafa, Pijush Samui and Sunita Kumari

Infrastructures 2022, 7(9), 121; https://doi.org/10.3390/infrastructures7090121 - 15 Sep 2022

Cited by 18 | Viewed by 5020

Abstract

Gravity retaining walls are a vital structure in the area of geotechnical engineering, and academicians in earlier studies have conveyed substantial uncertainties involved in calculating the factor of safety against overturning, using a deterministic approach. Hence, to enhance the accuracy and eliminate the uncertainties involved, artificial intelligence (AI) was used in the present research. The main aim of this study is to propose a high-performance machine learning (ML) model to determine the factor of safety (FOS) of gravity retaining walls against overturning. The projected methodology included a novel hybrid machine learning model that merged with an adaptive neuro-fuzzy inference system (ANFIS) and meta-heuristic optimization techniques (particle swarm optimization (PSO), genetic algorithm (GA), firefly algorithm (FFA) and grey wolf optimization (GWO)). In this research, four hybrid models, namely ANFIS-PSO, ANFIS-FFA, ANFIS-GA and ANFIS-GWO, were created to estimate the factor of safety against overturning. The proposed hybrid models were evaluated on two distinct datasets (training 70% and testing 30%) with three input combinations, namely cohesion (c), unit weight of soil (Υ) and angle of shearing resistance (φ). To access the prediction power of different hybrid models, various statistical parameters such as R², AdjR², VAF, WI, LMI, a-20 index, PI, KGE, RMSE, SI, MAE, NMBE and MBE were computed for training (TR) and testing (TS) datasets. The overall performance of the models indicated that ANFIS-PSO provided better results among all four models. The reliability index was computed using the first-order second-moment (FOSM) method for all models, and the probability of failure was also computed. A Williams plot was drawn to check the applicability domain of the hybrid model and to check the influence of different input parameters on the prediction of the factor of safety, and the Gini index was also computed. Full article

(This article belongs to the Special Issue Artificial Intelligence in Infrastructure Geotechnics)

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16 pages, 5152 KiB

Open AccessArticle

Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning

by Gonçalo Pereira, Manuel Parente, João Moutinho and Manuel Sampaio

Infrastructures 2021, 6(11), 157; https://doi.org/10.3390/infrastructures6110157 - 5 Nov 2021

Cited by 13 | Viewed by 3984

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 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)

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24 pages, 5970 KiB

Open AccessArticle

A Machine-Learning Approach for Extracting Modulus of Compacted Unbound Aggregate Base and Subgrade Materials Using Intelligent Compaction Technology

by Aria Fathi, Cesar Tirado, Sergio Rocha, Mehran Mazari and Soheil Nazarian

Infrastructures 2021, 6(10), 142; https://doi.org/10.3390/infrastructures6100142 - 8 Oct 2021

Cited by 10 | Viewed by 2379

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 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)

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20 pages, 6400 KiB

Open AccessArticle

Rock Strain Prediction Using Deep Neural Network and Hybrid Models of ANFIS and Meta-Heuristic Optimization Algorithms

by T. Pradeep, Abidhan Bardhan, Avijit Burman and Pijush Samui

Infrastructures 2021, 6(9), 129; https://doi.org/10.3390/infrastructures6090129 - 7 Sep 2021

Cited by 26 | Viewed by 2779

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 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)

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Review

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28 pages, 9357 KiB

Open AccessReview

(AI) in Infrastructure Projects—Gap Study

by Mohamed Y. Abdel-Kader, Ahmed M. Ebid, Kennedy C. Onyelowe, Ibrahim M. Mahdi and Ibrahim Abdel-Rasheed

Infrastructures 2022, 7(10), 137; https://doi.org/10.3390/infrastructures7100137 - 17 Oct 2022

Cited by 5 | Viewed by 3912

Abstract

Infrastructure projects are usually complicated, expensive, long-term mega projects; accordingly, they are the type of projects that most need optimization in the design, construction and operation stages. A great deal of earlier research was carried out to optimize the performance of infrastructure projects using traditional management techniques. Recently, artificial intelligence (AI) techniques were implemented in infrastructure projects to improve their performance and efficiency due to their ability to deal with fuzzy, incomplete, inaccurate and distorted data. The aim of this research is to collect, classify, analyze and review all of the available previous research related to implementing AI techniques in infrastructure projects to figure out the gaps in the previous studies and the recent trends in this research area. A total of 159 studies were collected since the beginning of the 1990s until the end of 2021. This database was classified based on publishing date, infrastructure subject and the used AI technique. The results of this study show that implementing AI techniques in infrastructure projects is rapidly increasing. They also indicate that transportation is the first and the most AI-using project and that both artificial neural networks (ANN) and particle swarm optimization (PSO) are the most implemented techniques in infrastructure projects. Finally, the study presented some opportunities for farther research, especially in natural gas projects. Full article

(This article belongs to the Special Issue Artificial Intelligence in Infrastructure Geotechnics)

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