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9 pages, 206 KiB

Open AccessEditorial

The Application of Machine Learning in Geotechnical Engineering

by Wei Gao

Appl. Sci. 2024, 14(11), 4712; https://doi.org/10.3390/app14114712 - 30 May 2024

Cited by 1 | Viewed by 1672

Abstract

Geotechnical engineering is civil engineering constructed in rock and soil and includes three main types: underground, foundation, and slope engineering [...] Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

Research

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23 pages, 3343 KiB

Open AccessArticle

Prediction of Utility Tunnel Performance in a Soft Foundation during an Operation Period Based on Deep Learning

by Wei Gao, Shuangshuang Ge, Yangqinchu Gao and Shuo Yuan

Appl. Sci. 2024, 14(6), 2334; https://doi.org/10.3390/app14062334 - 10 Mar 2024

Viewed by 1028

Abstract

The underground utility tunnel in a soft foundation is generally affected by the serious disturbance of the vehicle load during the operation period. Therefore, in this study, for the typical utility tunnel engineering in Suqian City of Jiangsu Province, China, field tests were [...] Read more.

The underground utility tunnel in a soft foundation is generally affected by the serious disturbance of the vehicle load during the operation period. Therefore, in this study, for the typical utility tunnel engineering in Suqian City of Jiangsu Province, China, field tests were conducted to monitor the performance of the utility tunnel structure in a soft foundation affected by the ground traffic loads during the operation period. Based on the test results, the datasets whose number is 15,376, composed of the five main disturbance factors (four vehicle operating load parameters and one operating time parameter), and the corresponding two main structure responses (displacement and stress) have been constructed. Based on the obtained datasets, using the proposed new deep learning model called WO-DBN, in which the seven hyperparameters of a deep belief network (DBN) are determined by the whale optimization algorithm (WOA), the safety responses of the utility tunnel structure have been predicted. The results show that for the prediction results, the average absolute error for the displacement is 0.1604, and for the stress, it is 12.3726, which are not significant and can meet the requirement of the real engineering. Therefore, the deep learning model can accurately predict the performance of the utility tunnel structure under a vehicle load and other disturbances, and the model has good applicability. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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18 pages, 4881 KiB

Open AccessArticle

Predicting Model of Dual-Mode Shield Tunneling Parameters in Complex Ground Using Recurrent Neural Networks and Multiple Optimization Algorithms

by Taihua Yang, Tian Wen, Xing Huang, Bin Liu, Hongbing Shi, Shaoran Liu, Xiaoxiang Peng and Guangzu Sheng

Appl. Sci. 2024, 14(2), 581; https://doi.org/10.3390/app14020581 - 9 Jan 2024

Cited by 1 | Viewed by 1192

Abstract

Based on the left tunnel of the Liuxiandong Station to Baimang Station section of Shenzhen Metro Line 13 (China), a prediction model for the advanced rate of dual-mode shield tunneling in complex strata was established to explore intelligent tunneling technology in complex ground. [...] Read more.

Based on the left tunnel of the Liuxiandong Station to Baimang Station section of Shenzhen Metro Line 13 (China), a prediction model for the advanced rate of dual-mode shield tunneling in complex strata was established to explore intelligent tunneling technology in complex ground. Firstly, geological parameters of the complex strata and on-site monitoring parameters of EPB/TBM dual-mode shield tunneling were collected, with tunneling parameters, shield tunneling mode, and strata parameters selected as input features. Subsequently, the Isolation Forest algorithm was employed to remove outliers from the original advance parameters, and an improved mean filtering algorithm was applied to eliminate data noise, resulting in the steady-state phase parameters of the shield tunneling process. The base model was chosen as the Long-Short Term Memory (LSTM) recurrent neural network. During the model training process, particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and Bayesian optimization (BO) algorithms were, respectively, combined to optimize the model’s hyperparameters. Via rank analysis based on evaluation metrics, the BO-LSTM model was found to have the shortest runtime and highest accuracy. Finally, the dropout algorithm and five-fold time series cross-validation were incorporated into the BO-LSTM model, creating a multi-algorithm-optimized recurrent neural network model for predicting tunneling speed. The results indicate that (1) the Isolation Forest algorithm can conveniently identify outliers while considering the relationship between tunneling speed and other parameters; (2) the improved mean filtering algorithm exhibits better denoising effects on cutterhead speed and tunneling speed; and (3) the multi-algorithm optimized LSTM model exhibits high prediction accuracy and operational efficiency under various geological parameters and different excavation modes. The minimum Mean Absolute Percentage Error (MAPE) prediction result is 8.3%, with an average MAPE prediction result below 15%. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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21 pages, 2362 KiB

Open AccessArticle

Prediction of Acceleration Amplification Ratio of Rocking Foundations Using Machine Learning and Deep Learning Models

by Sivapalan Gajan

Appl. Sci. 2023, 13(23), 12791; https://doi.org/10.3390/app132312791 - 29 Nov 2023

Cited by 1 | Viewed by 1179

Abstract

Experimental results reveal that rocking shallow foundations reduce earthquake-induced force and flexural displacement demands transmitted to structures and can be used as an effective geotechnical seismic isolation mechanism. This paper presents data-driven predictive models for maximum acceleration transmitted to structures founded on rocking [...] Read more.

Experimental results reveal that rocking shallow foundations reduce earthquake-induced force and flexural displacement demands transmitted to structures and can be used as an effective geotechnical seismic isolation mechanism. This paper presents data-driven predictive models for maximum acceleration transmitted to structures founded on rocking shallow foundations during earthquake loading. Results from base-shaking experiments on rocking foundations have been utilized for the development of artificial neural network regression (ANN), k-nearest neighbors regression, support vector regression, random forest regression, adaptive boosting regression, and gradient boosting regression models. Acceleration amplification ratio, defined as the maximum acceleration at the center of gravity of a structure divided by the peak ground acceleration of the earthquake, is considered as the prediction parameter. For five out of six models developed in this study, the overall mean absolute percentage error in predictions in repeated k-fold cross validation tests vary between 0.128 and 0.145, with the ANN model being the most accurate and most consistent. The cross validation mean absolute error in predictions of all six models vary between 0.08 and 0.1, indicating that the maximum acceleration of structures supported by rocking foundations can be predicted within an average error limit of 8% to 10% of the peak ground acceleration of the earthquake. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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21 pages, 5938 KiB

Open AccessArticle

Utilizing Multivariate Adaptive Regression Splines (MARS) for Precise Estimation of Soil Compaction Parameters

by Musaab Sabah Abed, Firas Jawad Kadhim, Jwad K. Almusawi, Hamza Imran, Luís Filipe Almeida Bernardo and Sadiq N. Henedy

Appl. Sci. 2023, 13(21), 11634; https://doi.org/10.3390/app132111634 - 24 Oct 2023

Cited by 4 | Viewed by 1726

Abstract

Traditional laboratory methods for estimating soil compaction parameters, such as the Proctor test, have been recognized as time-consuming and labor-intensive. Given the increasing need for the rapid and accurate estimation of soil compaction parameters for a range of geotechnical applications, the application of [...] Read more.

Traditional laboratory methods for estimating soil compaction parameters, such as the Proctor test, have been recognized as time-consuming and labor-intensive. Given the increasing need for the rapid and accurate estimation of soil compaction parameters for a range of geotechnical applications, the application of machine learning models offers a promising alternative. This study focuses on employing the multivariate adaptive regression splines (MARS) model algorithm, a machine learning method that presents a significant advantage over other models through generating human-understandable piecewise linear equations. The MARS model was trained and tested on a comprehensive dataset to predict essential soil compaction parameters, including optimum water content (w_opt) and maximum dry density (ρ_dmax). The performance of the model was evaluated using coefficient of determination (R²) and root mean square error (RMSE) values. Remarkably, the MARS models showed excellent predictive ability with high R² and low RMSE, MAE, and relative error values, indicating its robustness and reliability in predicting soil compaction parameters. Through rigorous five-fold cross-validation, the model’s predictions for w_opt returned an RMSE of 1.948%, an R² of 0.893, and an MAE of 1.498%. For ρ_dmax, the results showcased an RMSE of 0.064 Mg/m³, an R² of 0.899, and an MAE of 0.050 Mg/m³. When evaluated on unseen data, the model’s performance for w_opt prediction was marked with an MAE of 1.276%, RMSE of 1.577%, and R² of 0.948. Similarly, for ρ_dmax, the predictions were characterized by an MAE of 0.047 Mg/m³, RMSE of 0.062 Mg/m³, and R² of 0.919. The results also indicated that the MARS model outperformed previously developed machine learning models, suggesting its potential to replace conventional testing methods. The successful application of the MARS model could revolutionize the geotechnical field through providing quick and reliable predictions of soil compaction parameters, improving efficiency for construction projects. Lastly, a variable importance analysis was performed on the model to assess how input variables affect its outcomes. It was found that fine content (C_f) and plastic limit (PL) have the greatest impact on compaction parameters. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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15 pages, 4170 KiB

Open AccessArticle

Effects of Dry Density and Moisture Content on the Kaolin–Brass Interfacial Shear Adhesion

by Rayed Almasoudi, Hossam Abuel-Naga and Firas Daghistani

Appl. Sci. 2023, 13(20), 11191; https://doi.org/10.3390/app132011191 - 11 Oct 2023

Viewed by 1428

Abstract

Kaolin clay, with its consistent properties, fine particle size, high surface area, and extensive historical use, stands out as a reliable choice for laboratory research. This study aims to assess the interface shear adhesion behaviour between compacted clay and a metallic surface. For [...] Read more.

Kaolin clay, with its consistent properties, fine particle size, high surface area, and extensive historical use, stands out as a reliable choice for laboratory research. This study aims to assess the interface shear adhesion behaviour between compacted clay and a metallic surface. For this purpose, a new testing approach was developed. The proposed method is simple, requires neither advanced equipment nor specialised test procedures, and, thus, represents an improvement over existing practices in this field. The experimental program focuses on determining the interface shear adhesion strength between reconstituted kaolin clay and a metallic surface. The kaolin clay testing specimens were dynamically compacted at various energy levels and moisture contents. The results indicate that the optimum moisture content is 30%, which provides the highest density to the sample and divides the compaction curve into dry and wet sides. Furthermore, the results demonstrate that the interface shear adhesion strength increases with the clay’s dry density. Conversely, there is a significant decrease in strength as the moisture content specifically rises on the wet side of the compaction curve. The adhesion behaviour was also attributed to matric suction, where high suction enhanced interfacial adhesion, while low suction weakened bonding and diminished adhesion. Additionally, this study presents a unique three-dimensional contour graph illustrating the combined effects of dry density and moisture content on the interfacial adhesion. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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24 pages, 9860 KiB

Open AccessArticle

Research on Multi-Objective Optimization Model of Foundation Pit Dewatering Based on NSGA-II Algorithm

by Zhiheng Ma, Jinguo Wang, Yanrong Zhao, Bolin Li and Yufeng Wei

Appl. Sci. 2023, 13(19), 10865; https://doi.org/10.3390/app131910865 - 29 Sep 2023

Cited by 1 | Viewed by 1481

Abstract

This study focuses on optimizing the foundation pit dewatering scheme using the foundation pit dewatering theory and the principles of multi-objective optimization. It explores the development of a multi-objective optimization model and efficient solution technology for foundation pit dewatering. This research focuses on [...] Read more.

This study focuses on optimizing the foundation pit dewatering scheme using the foundation pit dewatering theory and the principles of multi-objective optimization. It explores the development of a multi-objective optimization model and efficient solution technology for foundation pit dewatering. This research focuses on the foundation pit dewatering project at the inverted siphon section of Xixiayuan canal head, specifically from pile number XZ0+326 to XZ0+500. It establishes an optimized mathematical model for foundation pit dewatering that incorporates three objectives. Additionally, a dewatering optimization program is developed by utilizing the MATLAB optimization toolbox and the multi-objective optimization algorithm program based on the NSGA-II algorithm (Gamultiobj). The multi-objective optimization mathematical model is solved, and a Pareto-optimal solution set with uniform distribution is obtained. The multi-objective optimization evaluation system based on AHP is constructed from the three aspects of dewatering cost, the impact of settlement on the environment, and the safety and stability of the foundation pit. The optimization scheme of the Pareto-optimal solution set is selected as the decision result to provide multiple feasible schemes for the dewatering construction of foundation pits. The optimization scheme is verified by using the GMS software. The simulation results demonstrate that the optimization scheme fulfills the requirements for water level and settlement control. Moreover, the developed optimization program efficiently solves the multi-objective optimization problem associated with foundation pit dewatering. Lastly, an evaluation system incorporating the NSGA-II algorithm and AHP is developed and utilized in the context of dewatering engineering in order to offer multiple viable optimal dewatering schemes. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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20 pages, 3356 KiB

Open AccessArticle

Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data

by Ayele Tesema Chala and Richard P. Ray

Appl. Sci. 2023, 13(14), 8286; https://doi.org/10.3390/app13148286 - 18 Jul 2023

Cited by 7 | Viewed by 2331

Abstract

Seismic response assessment requires reliable information about subsurface conditions, including soil shear wave velocity

(V_{s})

. To properly assess seismic response, engineers need accurate information about

V_{s}

, an essential parameter for evaluating the propagation of seismic waves. However, [...] Read more.

Seismic response assessment requires reliable information about subsurface conditions, including soil shear wave velocity

(V_{s})

. To properly assess seismic response, engineers need accurate information about

V_{s}

, an essential parameter for evaluating the propagation of seismic waves. However, measuring

V_{s}

is generally challenging due to the complex and time-consuming nature of field and laboratory tests. This study aims to predict

V_{s}

using machine learning (ML) algorithms from cone penetration test (CPT) data. The study utilized four ML algorithms, namely Random Forests (RFs), Support Vector Machine (SVM), Decision Trees (DT), and eXtreme Gradient Boosting (XGBoost), to predict

V_{s}

. These ML models were trained on 70% of the datasets, while their efficiency and generalization ability were assessed on the remaining 30%. The hyperparameters for each ML model were fine-tuned through Bayesian optimization with k-fold cross-validation techniques. The performance of each ML model was evaluated using eight different metrics, including root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (

R^{2}

), performance index (

P I

), scatter index (

S I

),

A 10 - I

, and

U_{95}

. The results demonstrated that the RF model consistently performed well across all metrics. It achieved high accuracy and the lowest level of errors, indicating superior accuracy and precision in predicting

V_{s}

. The SVM and XGBoost models also exhibited strong performance, with slightly higher error metrics compared with the RF model. However, the DT model performed poorly, with higher error rates and uncertainty in predicting

V_{s}

. Based on these results, we can conclude that the RF model is highly effective at accurately predicting

V_{s}

using CPT data with minimal input features. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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22 pages, 4410 KiB

Open AccessArticle

Evaluating the Influence of Sand Particle Morphology on Shear Strength: A Comparison of Experimental and Machine Learning Approaches

by Firas Daghistani and Hossam Abuel-Naga

Appl. Sci. 2023, 13(14), 8160; https://doi.org/10.3390/app13148160 - 13 Jul 2023

Cited by 12 | Viewed by 2638

Abstract

Particulate materials, such as sandy soil, are everywhere in nature and form the basis for many engineering applications. The aim of this research is to investigate the particle shape, size, and gradation of sandy soil and how they relate to shear strength, which [...] Read more.

Particulate materials, such as sandy soil, are everywhere in nature and form the basis for many engineering applications. The aim of this research is to investigate the particle shape, size, and gradation of sandy soil and how they relate to shear strength, which is an essential characteristic that impacts soil stability and mechanical behaviour. This will be achieved by employing a combination of experimental methodology, which includes the use of a microscope direct shear apparatus, and machine learning techniques, namely multiple linear regression and random forest regression. The experimental findings reveal that angular-shaped sand particles enhance the shear strength characteristics compared to spherical, rounded ones. Similarly, coarser sand particles improve these characteristics compared to finer sand particles, as do well-graded particles when compared to poorly graded ones. The machine learning findings show the validity of both models in predicting shear strength when compared to the experimental results, showing high accuracy. The models are designed to predict shear strength of sand considering six input features: mean particle size, uniformity coefficient, curvature coefficient, dry density, normal stress, and particle regularity. The most important features from both models were identified. In addition, an empirical equation for calculating shear strength was developed through multiple linear regression analysis using the six features. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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18 pages, 4481 KiB

Open AccessArticle

Prediction of Undrained Bearing Capacity of Skirted Foundation in Spatially Variable Soils Based on Convolutional Neural Network

by Haifeng Cheng, Houle Zhang, Zihan Liu and Yongxin Wu

Appl. Sci. 2023, 13(11), 6624; https://doi.org/10.3390/app13116624 - 30 May 2023

Cited by 6 | Viewed by 1647

Abstract

Skirted foundations are widely used in offshore and subsea engineering. Previous studies have shown that soil undrained shear strength variability has a notable impact on probabilistic analyses of skirted foundation bearing capacity. This study proposes an efficient machine-learning method to predict the uniaxial [...] Read more.

Skirted foundations are widely used in offshore and subsea engineering. Previous studies have shown that soil undrained shear strength variability has a notable impact on probabilistic analyses of skirted foundation bearing capacity. This study proposes an efficient machine-learning method to predict the uniaxial bearing capacity factors of skirted foundations under pure horizontal and moment loads, without relying on traditional time-consuming random finite element methods. A two-dimensional convolutional neural network is adopted to capture the potential correlation between soil random fields and bearing capacity factors. The proposed CNN-based model exhibits satisfactory prediction performance with regard to coefficients of variation and scale of fluctuations in two directions. Specifically, coefficient of determination (R2) values exceed 0.97, while root mean square error (RMSE) values remain below 0.13 for the surrogate model. In addition, more than 96% of the predictions are associated with a relative error of 5% or less, providing evidence of the proposed 2D-CNN model’s satisfactory prediction performance. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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15 pages, 3795 KiB

Open AccessArticle

Application of an Artificial Neural Network (ANN) Model to Determine the Value of the Damping Ratio (D) of Clay Soils

by Marzena Lendo-Siwicka, Karina Zabłocka, Emil Soból, Anna Markiewicz and Grzegorz Wrzesiński

Appl. Sci. 2023, 13(10), 6224; https://doi.org/10.3390/app13106224 - 19 May 2023

Cited by 2 | Viewed by 1948

Abstract

The properties and behavior of soils depend on many factors. The interaction of individual factors is difficult to determine by traditional statistical methods due to their interdependence. The paper presents a procedure of creating an artificial neural network (ANN) model to determine the [...] Read more.

The properties and behavior of soils depend on many factors. The interaction of individual factors is difficult to determine by traditional statistical methods due to their interdependence. The paper presents a procedure of creating an artificial neural network (ANN) model to determine the value of the damping ratio (D) of clay soils. The main purpose of this paper is to compare the appropriateness of ANN model application with empirical formulas described in the literature. The ANN model was developed using a series of laboratory tests of the damping ratio performed in the Resonance Column. Predicted values of the damping ratio of clay soils obtained from the ANN model are characterized by high convergence (coefficient of determination R² = 0.976). In comparison with other published empirical formulas, the ANN model showed an improvement in the prediction accuracy. What is more, ANN models proved to be more flexible compared to formulas and relationships with a predetermined structure, and they were well suited to modeling the complex behavior of most geotechnical engineering materials, which, by their very nature, exhibit extreme variability. In conclusion, ANNs have the potential to predict the damping ratio (D) of clay soils and can do much better than traditional statistical techniques. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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18 pages, 3047 KiB

Open AccessArticle

Assessing the Performance of Machine Learning Algorithms for Soil Classification Using Cone Penetration Test Data

by Ayele Tesema Chala and Richard Ray

Appl. Sci. 2023, 13(9), 5758; https://doi.org/10.3390/app13095758 - 6 May 2023

Cited by 10 | Viewed by 3662

Abstract

Conventional soil classification methods are expensive and demand extensive field and laboratory work. This research evaluates the efficiency of various machine learning (ML) algorithms in classifying soils based on Robertson’s soil behavioral types. This study employs 4 ML algorithms, including artificial neural network [...] Read more.

Conventional soil classification methods are expensive and demand extensive field and laboratory work. This research evaluates the efficiency of various machine learning (ML) algorithms in classifying soils based on Robertson’s soil behavioral types. This study employs 4 ML algorithms, including artificial neural network (ANN), random forest (RF), support vector machine (SVM), and decision trees (DT), to classify soils from 232 cone penetration test (CPT) datasets. The datasets were randomly split into training and testing datasets to train and test the ML models. Metrics such as overall accuracy, sensitivity, precision, F1_score, and confusion matrices provided quantitative evaluations of each model. Our analysis showed that all the ML models accurately classified most soils. The SVM model achieved the highest accuracy of 99.84%, while the ANN model achieved an overall accuracy of 98.82%. The RF and DT models achieved overall accuracy scores of 99.23% and 95.67%, respectively. Additionally, most of the evaluation metrics indicated high scores, demonstrating that the ML models performed well. The SVM and RF models exhibited outstanding performance on both majority and minority soil classes, while the ANN model achieved lower sensitivity and F1_score for minority soil class. Based on these results, we conclude that the SVM and RF algorithms can be integrated into software programs for rapid and accurate soil classification. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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13 pages, 1656 KiB

Open AccessArticle

Prediction Modeling of Ground Subsidence Risk Based on Machine Learning Using the Attribute Information of Underground Utilities in Urban Areas in Korea

by Sungyeol Lee, Jaemo Kang and Jinyoung Kim

Appl. Sci. 2023, 13(9), 5566; https://doi.org/10.3390/app13095566 - 30 Apr 2023

Cited by 3 | Viewed by 1515

Abstract

As ground subsidence accidents in urban areas that occur due to damage to underground utilities can cause great damage, it is necessary to predict and prepare for such accidents in order to minimize such damage. It has been reported that the main cause [...] Read more.

As ground subsidence accidents in urban areas that occur due to damage to underground utilities can cause great damage, it is necessary to predict and prepare for such accidents in order to minimize such damage. It has been reported that the main cause of ground subsidence in urban areas is cavities in the ground formed by damage to underground utilities. Thus, in this study, attribute information and historical ground subsidence information of six types of underground utility lines (water supply, sewage, power, gas, heating, and communication) were collected to develop a ground subsidence risk prediction model based on machine learning. To predict the risk of ground subsidence in the target area, it was divided into a grid with a square size of 500 m × 500 m, and attribute information of underground utility lines and historical information of ground subsidence included in the grid were extracted. Six types of underground utility lines were merged into single-type attribute information, and the risk of ground subsidence was categorized into three levels using the number of ground subsidence occurrences to develop a dataset. In addition, 12 datasets, which were developed based on the conditions of certain divided ranges of attribute information and risk levels, and 12 additional datasets, which were developed using the Synthetic Minority Oversampling Technique to resolve the imbalance of data, were built. Then, factors that represented significant correlations between input and output data were singled out and were then applied to the RandomForest, XGBoost, and LightGBM algorithms to select a model that produced the best performance. By classifying the ground subsidence risk levels through the selected model, it was found that density was the most important influencing factor used in the model. A risk map of ground subsidence in the target area was made through the model; the map showed the trend of well-predicted risk levels in the area where ground subsidence was concentrated. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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22 pages, 8655 KiB

Open AccessArticle

CatBoost–Bayesian Hybrid Model Adaptively Coupled with Modified Theoretical Equations for Estimating the Undrained Shear Strength of Clay

by Huajian Yang, Zhikui Liu, Yuantao Li, Haixia Wei and Nengsheng Huang

Appl. Sci. 2023, 13(9), 5418; https://doi.org/10.3390/app13095418 - 26 Apr 2023

Cited by 3 | Viewed by 2428

Abstract

The undrained shear strength of clay is an important index for the calculation of the bearing capacity of the foundation soil, the calculation of the soil pressure of the foundation pit, and the analysis of the slope stability. Therefore, the purpose of this [...] Read more.

The undrained shear strength of clay is an important index for the calculation of the bearing capacity of the foundation soil, the calculation of the soil pressure of the foundation pit, and the analysis of the slope stability. Therefore, the purpose of this paper is to conduct a comprehensive study of the combined use of machine learning with clay theoretical equations to estimate it. Under the Bayesian framework, the CatBoost algorithm (CatBoost–Bayesian) based on Bayesian optimization algorithm was developed to obtain the feature importance level of soil parameters affecting the undrained shear strength of clay, so as to adaptively couple the theoretical equation of undrained shear strength of

K_{0}

consolidated clay, which was derived from the modified Cambridge model. Then, the theoretical equation of undrained shear strength of the isotropically consolidated clay was established from the critical state of the clay parameters. Finally, it was illustrated and verified using the experimental samples of Finnish clay. The results indicate that the theoretical equation established by the overconsolidation ratio and effective overburden pressure parameters can well estimate the undrained shear strength of isotropically consolidated clays, and the parameter uncertainty can be considered explicitly and rigorously. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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18 pages, 5846 KiB

Open AccessArticle

Assessment of the Rock Elasticity Modulus Using Four Hybrid RF Models: A Combination of Data-Driven and Soft Techniques

by Chuanqi Li and Daniel Dias

Appl. Sci. 2023, 13(4), 2373; https://doi.org/10.3390/app13042373 - 12 Feb 2023

Cited by 10 | Viewed by 4110

Abstract

The determination of the rock elasticity modulus (EM) is an indispensable key step for the design of rock engineering problems. Traditional experimental analysis can accurately measure the rock EM, but it requires manpower and material resources, and it is time consuming. The EM [...] Read more.

The determination of the rock elasticity modulus (EM) is an indispensable key step for the design of rock engineering problems. Traditional experimental analysis can accurately measure the rock EM, but it requires manpower and material resources, and it is time consuming. The EM estimation of new rocks using former published empirical formulas is also a possibility but can be attached of high uncertainties. In this paper, four types of metaheuristic optimization algorithms (MOA), named the backtracking search optimization algorithm (BSA), multi-verse optimizer (MVO), golden eagle optimizer (GEO) and poor and rich optimization algorithm (PRO), were utilized to optimize the random forest (RF) model for predicting the rock EM. A data-driven technology was used to generate an integrated database consisting of 120 rock samples from the literature. To verify the predictive performance of the proposed models, five common machine-learning models and one empirical formula were also developed to predict the rock EM. Four popular performance indices, including the root-mean-square error (RMSE), mean absolute error (MAE), the coefficient of determination (R²) and Willmott’s index (WI), were adopted to evaluate all models. The results showed that the PRO-RF model has obtained the most satisfactory prediction accuracy. The porosity (Pn) is the most important variable for predicting the rock EM based on the sensitive analysis. This paper compares the performance of the RF models optimized by using four MOA for the rock EM prediction. It provides a good example for the subsequent application of soft techniques on the EM and other important rock parameter estimations. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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17 pages, 6309 KiB

Open AccessArticle

Intelligent Feedback Analysis of Fluid–Solid Coupling of Surrounding Rock of Tunnel in Water-Rich Areas

by Tao Zhan, Xinping Guo, Tengfei Jiang and Annan Jiang

Appl. Sci. 2023, 13(3), 1479; https://doi.org/10.3390/app13031479 - 22 Jan 2023

Cited by 1 | Viewed by 1475

Abstract

To realize parameter feedback optimization of tunnel construction in water-rich areas, a feedback analysis method for tunnel parameters under fluid–solid coupling conditions was established based on an intelligent optimization algorithm. Firstly, the numerical calculation model was established and solved using the fluid–solid coupling [...] Read more.

To realize parameter feedback optimization of tunnel construction in water-rich areas, a feedback analysis method for tunnel parameters under fluid–solid coupling conditions was established based on an intelligent optimization algorithm. Firstly, the numerical calculation model was established and solved using the fluid–solid coupling model. In orthogonal design analysis, the displacement of surrounding rock and pore water pressure distribution in different rock mass parameter combinations were obtained, and the learning samples needed for machine learning were established. The input group was surrounding rock displacement and pore water pressure, and the output was rock mass parameters. Then, the Gaussian process algorithm was used to obtain the nonlinear mapping relationship contained in the learning samples. A differential evolution algorithm was used to optimize the critical parameters involved in this process. Furthermore, according to the established regression model and the measured displacement and pore water pressure in the research area, differential evolution was used again to optimize the rock mass parameters and obtain the parameter feedback analysis results. Finally, the inversion values were compared with the actual measured values, and the reliability of the surrounding rock parameters obtained from the feedback analysis was verified, providing an effective method for obtaining surrounding rock parameters for similar projects. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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9 pages, 3032 KiB

Open AccessCommunication

SAR and Optical Image Registration Based on Uniform Feature Points Extraction and Consistency Gradient Calculation

by Wannan Zhang and Yuqian Zhao

Appl. Sci. 2023, 13(3), 1238; https://doi.org/10.3390/app13031238 - 17 Jan 2023

Cited by 3 | Viewed by 1704

Abstract

Synthetic aperture radar (SAR) satellites have an active sensor on board, which emits electromagnetic signals and measures the strength and time delay of the returned signal backscattered from ground objects. Optical images have rich spectral information, but it is easily affected by atmospheric [...] Read more.

Synthetic aperture radar (SAR) satellites have an active sensor on board, which emits electromagnetic signals and measures the strength and time delay of the returned signal backscattered from ground objects. Optical images have rich spectral information, but it is easily affected by atmospheric attenuation and weather conditions. Thus, the study of the registration between these two images is of great significance. We present a novel method for SAR and optical image registration. In the stage of feature points extraction, the method combines phase consistency intensity screening and scale space grid division to obtain stable and uniform feature points from the image. During the stage of feature description, the method employs the extended phase consistency method to calculate the gradient amplitude and direction of the image, and improves the correctness of the main direction calculation and descriptor construction. Experimental results demonstrate its superior matching performance with respect to the state-of-the-art methods. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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19 pages, 6539 KiB

Open AccessArticle

Back Analysis of Geotechnical Engineering Based on Data-Driven Model and Grey Wolf Optimization

by Lihong Zhao, Xinyi Liu, Xiaoyu Zang and Hongbo Zhao

Appl. Sci. 2022, 12(24), 12595; https://doi.org/10.3390/app122412595 - 8 Dec 2022

Cited by 1 | Viewed by 2057

Abstract

Geomaterial mechanical parameters are critical to implementing construction design and evaluating stability through feedback analysis in geotechnical engineering. The back analysis is widely utilized to identify and calibrate the geomaterial mechanical properties in geotechnical engineering. This study developed a novel back-analysis framework by [...] Read more.

Geomaterial mechanical parameters are critical to implementing construction design and evaluating stability through feedback analysis in geotechnical engineering. The back analysis is widely utilized to identify and calibrate the geomaterial mechanical properties in geotechnical engineering. This study developed a novel back-analysis framework by combining a reduced-order model (ROM), grey wolf optimization (GWO), and numerical technology. The ROM was adopted to evaluate the response of the geotechnical structure based on a numerical model. GWO was used to search and identify the geomaterials properties based on the ROM. The developed back analysis framework was applied to a circular tunnel and a practical tunnel for determining the mechanical property of the surrounding rock mass. The results showed that the ROM could be an excellent surrogated model and replaced it with the numerical model. The obtained geomaterial properties were in excellent agreement with the actual properties. The deformation behavior captured by the developed framework was consistent with the theoretical solution in a circular rock tunnel. The developed framework provides a practical, accurate, and convenient approach for calibrating the geomaterial properties based on field monitoring data in practical geotechnical engineering applications. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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21 pages, 8237 KiB

Open AccessArticle

Back Analysis of Surrounding Rock Parameters of Large-Span Arch Cover Station Based on GP-DE Algorithm

by Fu Zheng, Annan Jiang, Xinping Guo, Qinghua Min and Qingfeng Yin

Appl. Sci. 2022, 12(24), 12590; https://doi.org/10.3390/app122412590 - 8 Dec 2022

Cited by 4 | Viewed by 1267

Abstract

Due to the characteristics of soil–rock composites and large-span arches, the surrounding rock parameters of stations are difficult to obtain accurately under soft upper and hard lower geological conditions when the arch cover method is used to carry out the construction of a [...] Read more.

Due to the characteristics of soil–rock composites and large-span arches, the surrounding rock parameters of stations are difficult to obtain accurately under soft upper and hard lower geological conditions when the arch cover method is used to carry out the construction of a large-span underground excavation station. To optimize the design of stations and guide the next step of construction, an intelligent inverse analysis method, the Gaussian process differential evolution co-optimization algorithm (GP-DE algorithm), is proposed for the arch cover method for station construction. Taking the Shikui Road station of the Dalian Metro Line Five as the engineering background, the finite element model of FLAC3D is established. By combining the measured data of the sensor and the monitoring data obtained using the orthogonal scheme, this algorithm is used for the joint back analysis of displacement stress and the accuracy of the inversion parameters is verified by forwarding the calculation for FLAC3D. By using the obtained surrounding rock parameters, the demolition length of the center diaphragm to the Shikui Road station is optimized. Under different numbers of training samples, the inversion effect of the GP-DE algorithm and the other three common back-analysis algorithms is compared and analyzed. Finally, based on the iteration rate and convergence effect, the value range of the differential evolution algorithm parameters F and CR is given. The results show that the forward calculation results of the parameters obtained from the back analysis are in good agreement with the actual values, and the accuracy of the back-analysis results is high. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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11 pages, 2692 KiB

Open AccessArticle

Modeling Undrained Shear Strength of Sensitive Alluvial Soft Clay Using Machine Learning Approach

by Mohamed B. D. Elsawy, Mohammed F. Alsharekh and Mahmoud Shaban

Appl. Sci. 2022, 12(19), 10177; https://doi.org/10.3390/app121910177 - 10 Oct 2022

Cited by 7 | Viewed by 5549

Abstract

Soft soils are commonly located in many regions near seas, oceans, and rivers all over the world. These regions are vital and attractive for population and governments development. Soft soil is classified as problematic soil owing to sustaining low shear strength and high [...] Read more.

Soft soils are commonly located in many regions near seas, oceans, and rivers all over the world. These regions are vital and attractive for population and governments development. Soft soil is classified as problematic soil owing to sustaining low shear strength and high settlement under structures. Constructing structures and/or infrastructures on soft soil is a considerable risk that needs great attention from structural engineers. The bearing capacity of structure foundations on soft soil depends mainly on their undrained shear strength. This soil feature strongly influences the selection of appropriate soil improvement methods. However, determining undrained shear strength is very difficult, costly, and time-consuming, especially for sensitive clay. Consequently, extracting undisturbed samples of sensitive clay faces several difficulties on construction sites. In this research, accurate field-tested data were fed to advanced machine learning models to predict the undrained shear strength of the sensitive clay to save hard effort, time, repeated laboratory testing, and costs. In this context, a dataset of 111 geotechnical testing points were collected based on laboratory and field examinations of the soil’s key features. These features included the water content, liquid limit, dry unit weight, plasticity index, consistency index, void ratio, specific gravity, and pocket penetration shear. Several machine learning algorithms were adopted to provide the soft clay modeling, including the linear, Gaussian process regression, ensemble and regression trees, and the support vector regression. The coefficient of determination was mainly used to assess the performance of each predictive model. The achieved results revealed that the support vector regression model attained the most accurate prediction for soil undrained shear strength. These outcomes lay the groundwork for evaluating soil shear strength characteristics in a practical, fast, and low-cost way. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)

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