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Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering

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A special issue of Geosciences (ISSN 2076-3263).

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 44591

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Prof. Dr. Mark Jaksa

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Guest Editor

School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, Australia
Interests: spatial variability of soils; probabilistic analysis and design in geotechnical engineering; geostatistics; artificial neural networks; optimisation of site investigations; expansive and unsaturated soils; residential footing design; environmental geotechnics and landfills; in situ testing of soils (CPT, DMT); tensile capacity of temporary; ground anchors; computer applications in geotechnical engineering; computer applications in teaching and learning

Dr. Zhongqiang Liu

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Guest Editor

Norwegian Geotechnical Institute, Ullevaal Stadion, P.O. Box 3930, 0806 Oslo, Norway
Interests: offshore foundations; offshore wind turbines; machine learning; reliability-based design
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Special Issue Information

Dear Colleagues,

As geotechnical engineering deals with natural materials—i.e., soil and rock—data associated with these often exhibit significant variability. In recent years, artificial intelligence methods, such as artificial neural networks, genetic programming, and support vector machines, have become more mature and more readily available and, as a result, have seen increased application to a wide range of geotechnical engineering problems. Such applications of have demonstrated that artificial intelligence techniques frequently outperform traditional, deterministic-based solutions.

This Special Issue seeks to incorporate the latest developments in artificial intelligence with respect to geotechnical engineering. Authors are encouraged to submit their latest research in the broad field of “Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering”. Authors are further encouraged to consider how their models can be disseminated, for example, digitally or by means of an equation, so that readers and practitioners can make use of them in their work.

Prof. Dr. Mark Jaksa
Dr. Zhongqiang Liu
Guest Editors

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Keywords

artificial intelligence
machine learning
artificial neural networks
genetic programming
support vector machines

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Published Papers (9 papers)

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Editorial

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2 pages, 153 KiB

Open AccessEditorial

Editorial for Special Issue “Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering”

by Mark Jaksa and Zhongqiang Liu

Geosciences 2021, 11(10), 399; https://doi.org/10.3390/geosciences11100399 - 22 Sep 2021

Cited by 12 | Viewed by 3112

Abstract

Since its inception in the mid-1950s [...] Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering)

Research

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20 pages, 5060 KiB

Open AccessEditor’s ChoiceArticle

CPT Data Interpretation Employing Different Machine Learning Techniques

by Stefan Rauter and Franz Tschuchnigg

Geosciences 2021, 11(7), 265; https://doi.org/10.3390/geosciences11070265 - 22 Jun 2021

Cited by 31 | Viewed by 7297

Abstract

The classification of soils into categories with a similar range of properties is a fundamental geotechnical engineering procedure. At present, this classification is based on various types of cost- and time-intensive laboratory and/or in situ tests. These soil investigations are essential for each individual construction site and have to be performed prior to the design of a project. Since Machine Learning could play a key role in reducing the costs and time needed for a suitable site investigation program, the basic ability of Machine Learning models to classify soils from Cone Penetration Tests (CPT) is evaluated. To find an appropriate classification model, 24 different Machine Learning models, based on three different algorithms, are built and trained on a dataset consisting of 1339 CPT. The applied algorithms are a Support Vector Machine, an Artificial Neural Network and a Random Forest. As input features, different combinations of direct cone penetration test data (tip resistance q_c, sleeve friction f_s, friction ratio R_f, depth d), combined with “defined”, thus, not directly measured data (total vertical stresses σ_v, effective vertical stresses σ’_v and hydrostatic pore pressure u₀), are used. Standard soil classes based on grain size distributions and soil classes based on soil behavior types according to Robertson are applied as targets. The different models are compared with respect to their prediction performance and the required learning time. The best results for all targets were obtained with models using a Random Forest classifier. For the soil classes based on grain size distribution, an accuracy of about 75%, and for soil classes according to Robertson, an accuracy of about 97–99%, was reached. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering)

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28 pages, 14543 KiB

Open AccessArticle

Stiffness and Strength of Stabilized Organic Soils—Part I/II: Experimental Database and Statistical Description for Machine Learning Modelling

by Francisco G. Hernandez-Martinez, Abir Al-Tabbaa, Zenon Medina-Cetina and Negin Yousefpour

Geosciences 2021, 11(6), 243; https://doi.org/10.3390/geosciences11060243 - 4 Jun 2021

Cited by 8 | Viewed by 3074

Abstract

This paper presents the experimental database and corresponding statistical analysis (Part I), which serves as a basis to perform the corresponding parametric analysis and machine learning modelling (Part II) of a comprehensive study on organic soil strength and stiffness, stabilized via the wet soil mixing method. The experimental database includes unconfined compression tests performed under laboratory-controlled conditions to investigate the impact of soil type, the soil’s organic content, the soil’s initial natural water content, binder type, binder quantity, grout to soil ratio, water to binder ratio, curing time, temperature, curing relative humidity and carbon dioxide content on the stabilized organic specimens’ stiffness and strength. A descriptive statistical analysis complements the description of the experimental database, along with a qualitative study on the stabilization hydration process via scanning electron microscopy images. Results confirmed findings on the use of Portland cement alone and a mix of Portland cement with ground granulated blast furnace slag as suitable binders for soil stabilization. Findings on mixes including lime and magnesium oxide cements demonstrated minimal stabilization. Specimen size affected stiffness, but not the strength for mixes of peat and Portland cement. The experimental database, along with all produced data analyses, are available at the Texas Data Repository as indicated in the Data Availability Statement below, to allow for data reproducibility and promote the use of artificial intelligence and machine learning competing modelling techniques as the ones presented in Part II of this paper. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering)

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22 pages, 4020 KiB

Open AccessArticle

Stiffness and Strength of Stabilized Organic Soils—Part II/II: Parametric Analysis and Modeling with Machine Learning

by Negin Yousefpour, Zenon Medina-Cetina, Francisco G. Hernandez-Martinez and Abir Al-Tabbaa

Geosciences 2021, 11(5), 218; https://doi.org/10.3390/geosciences11050218 - 17 May 2021

Cited by 7 | Viewed by 3181

Abstract

Predicting the range of achievable strength and stiffness from stabilized soil mixtures is critical for engineering design and construction, especially for organic soils, which are often considered “unsuitable” due to their high compressibility and the lack of knowledge about their mechanical behavior after stabilization. This study investigates the mechanical behavior of stabilized organic soils using machine learning (ML) methods. ML algorithms were developed and trained using a database from a comprehensive experimental study (see Part I), including more than one thousand unconfined compression tests on organic clay samples stabilized by wet soil mixing (WSM) technique. Three different ML methods were adopted and compared, including two artificial neural networks (ANN) and a linear regression method. ANN models proved reliable in the prediction of the stiffness and strength of stabilized organic soils, significantly outperforming linear regression models. Binder type, mixing ratio, soil organic and water content, sample size, aging, temperature, relative humidity, and carbonation were the control variables (input parameters) incorporated into the ML models. The impacts of these factors were evaluated through rigorous ANN-based parametric analyses. Additionally, the nonlinear relations of stiffness and strength with these parameters were developed, and their optimum ranges were identified through the ANN models. Overall, the robust ML approach presented in this paper can significantly improve the mixture design for organic soil stabilization and minimize the experimental cost for implementing WSM in engineering projects. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering)

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17 pages, 5495 KiB

Open AccessArticle

Leak-Off Pressure Using Weakly Correlated Geospatial Information and Machine Learning Algorithms

by Jung Chan Choi, Zhongqiang Liu, Suzanne Lacasse and Elin Skurtveit

Geosciences 2021, 11(4), 181; https://doi.org/10.3390/geosciences11040181 - 19 Apr 2021

Cited by 4 | Viewed by 3243

Abstract

Leak-off pressure (LOP) is a key parameter to determine the allowable weight of drilling mud in a well and the in situ horizontal stress. The LOP test is run in situ and is frequently used by the petroleum industry. If the well pressure exceeds the LOP, wellbore instability may occur, with hydraulic fracturing and large mud losses in the formation. A reliable prediction of LOP is required to ensure safe and economical drilling operations. The prediction of LOP is challenging because it is affected by the usually complex earlier geological loading history, and the values of LOP and their measurements can vary significantly geospatially. This paper investigates the ability of machine learning algorithms to predict leak-off pressure on the basis of geospatial information of LOP measurements. About 3000 LOP test data were collected from 1800 exploration wells offshore Norway. Three machine learning algorithms (the deep neural network (DNN), random forest (RF), and support vector machine (SVM) algorithms) optimized by three hyperparameter search methods (the grid search, randomized search and Bayesian search) were compared with multivariate regression analysis. The Bayesian search algorithm needed fewer iterations than the grid search algorithms to find an optimal combination of hyperparameters. The three machine learning algorithms showed better performance than the multivariate linear regression when the features of the geospatial inputs were properly scaled. The RF algorithm gave the most promising results regardless of data scaling. If the data were not scaled, the DNN and SVM algorithms, even with optimized parameters, did not provide significantly improved test scores compared to the multivariate regression analysis. The analyses also showed that when the number of data points in a geographical setting is much smaller than that of other geographical areas, the prediction accuracy reduces significantly. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering)

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16 pages, 3843 KiB

Open AccessArticle

Application of Machine Learning for the Automation of the Quality Control of Noise Filtering Processes in Seismic Data Imaging

by Mohamed Mejri and Maiza Bekara

Geosciences 2020, 10(12), 475; https://doi.org/10.3390/geosciences10120475 - 24 Nov 2020

Cited by 4 | Viewed by 3022

Abstract

Seismic imaging is the main technology used for subsurface hydrocarbon prospection. It provides an image of the subsurface using the same principles as ultrasound medical imaging. As for any data acquired through hydrophones (pressure sensors) and/or geophones (velocity/acceleration sensors), the raw seismic data are heavily contaminated with noise and unwanted reflections that need to be removed before further processing. Therefore, the noise attenuation is done at an early stage and often while acquiring the data. Quality control (QC) is mandatory to give confidence in the denoising process and to ensure that a costly data re-acquisition is not needed. QC is done manually by humans and comprises a major portion of the cost of a typical seismic processing project. It is therefore advantageous to automate this process to improve cost and efficiency. Here, we propose a supervised learning approach to build an automatic QC system. The QC system is an attribute-based classifier that is trained to classify three types of filtering (mild = under filtering, noise remaining in the data; optimal = good filtering; harsh = over filtering, the signal is distorted). The attributes are computed from the data and represent geophysical and statistical measures of the quality of the filtering. The system is tested on a full-scale survey (9000 km

^{2}

) to QC the results of the swell noise attenuation process in marine seismic data. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering)

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31 pages, 18003 KiB

Open AccessArticle

Soil Liquefaction Assessment Using Soft Computing Approaches Based on Capacity Energy Concept

by Zhixiong Chen, Hongrui Li, Anthony Teck Chee Goh, Chongzhi Wu and Wengang Zhang

Geosciences 2020, 10(9), 330; https://doi.org/10.3390/geosciences10090330 - 21 Aug 2020

Cited by 30 | Viewed by 4163

Abstract

Soil liquefaction is one of the most complicated phenomena to assess in geotechnical earthquake engineering. The conventional procedures developed to determine the liquefaction potential of sandy soil deposits can be categorized into three main groups: Stress-based, strain-based, and energy-based procedures. The main advantage of the energy-based approach over the remaining two methods is the fact that it considers the effects of strain and stress concurrently unlike the stress or strain-based methods. Several liquefaction evaluation procedures and approaches have been developed relating the capacity energy to the initial soil parameters, such as the relative density, initial effective confining pressure, fine contents, and soil textural properties. In this study, based on the capacity energy database by Baziar et al. (2011), analyses have been carried out on a total of 405 previously published tests using soft computing approaches, including Ridge, Lasso & LassoCV, Random Forest, eXtreme Gradient Boost (XGBoost), and Multivariate Adaptive Regression Splines (MARS) approaches, to assess the capacity energy required to trigger liquefaction in sand and silty sands. The results clearly prove the capability of the proposed models and the capacity energy concept to assess liquefaction resistance of soils. It is also proposed that these approaches should be used as cross-validation against each other. The result shows that the capacity energy is most sensitive to the relative density. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering)

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18 pages, 3318 KiB

Open AccessArticle

Optimal Testing Locations in Geotechnical Site Investigations through the Application of a Genetic Algorithm

by Michael P. Crisp, Mark Jaksa and Yien Kuo

Geosciences 2020, 10(7), 265; https://doi.org/10.3390/geosciences10070265 - 10 Jul 2020

Cited by 13 | Viewed by 4224

Abstract

Geotechnical site investigations are an essential prerequisite for reliable foundation designs. However, there is relatively little quantitative guidance for planning optimal investigations, including the choice of testing location. This study uses a genetic algorithm to find the ideal testing locations of various numbers of boreholes with respect to pile foundation performance. The optimization has been done separately for single-layer and multi-layer soils, which infer what is best for obtaining soil material properties and delineating layer boundaries, respectively. A sensitivity analysis was conducted to find the genetic algorithm parameters that result in high quality solutions within a reasonable timeframe. While boreholes arranged in a regular grid pattern provide good performance in many cases, there are instances where optimized locations provide a cost saving of A$2 million, or 4.2% of the construction cost. A set of recommended testing guidelines is provided. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering)

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Review

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24 pages, 1547 KiB

Open AccessReview

Systematic Literature Review on Data-Driven Models for Predictive Maintenance of Railway Track: Implications in Geotechnical Engineering

by Jiawei Xie, Jinsong Huang, Cheng Zeng, Shui-Hua Jiang and Nathan Podlich

Geosciences 2020, 10(11), 425; https://doi.org/10.3390/geosciences10110425 - 26 Oct 2020

Cited by 49 | Viewed by 7734

Abstract

Conventional planning of maintenance and renewal work for railway track is based on heuristics and simple scheduling. The railway industry is now collecting a large amount of data with the fast-paced development of sensor technologies. These data sets carry information about the conditions of various components in railway track. Since just before the beginning of the 21st century, data-driven models have been used in the predictive maintenance of railway track. This study presents a systematic literature review of data-driven models applied in the predictive maintenance of railway track. A taxonomy to classify the existing literature based on types of models and types of applications is provided. It is found that applying the deep learning methods, unsupervised methods, and ensemble methods are the new trends for predictive maintenance of railway track. Rail geometry irregularity, rail head defect, and missing rail components detection were the top three most commonly considered issues within the application of data-driven models. Prediction of rail breaks has received increasing attention in the last four years. Among these data-driven model applications, the collected data types are the most critical factors which affect selecting suitable models. Finally, this study discusses upcoming challenges in the predictive maintenance of railway track. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering)

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