Estimation of the undrained shear strength of sensitive clays using optimized inference intelligence system

Tran, Quoc Anh; Ho, Lanh Si; Le, Hiep Van; Prakash, Indra; Pham, Binh Thai

doi:10.1007/s00521-022-06891-5

Cited by 11 publications

(7 citation statements)

References 51 publications

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“…Based on the above results and analysis, accurate ML prediction of the undrained shear strength of the alluvial sensitive soft clay has an important and positive impact on the field of geotechnical engineering. The current ML model achieved more accuracy compared with other previous studies [6,19]. Precise determination of USS for the sensitive clay is a challenging task, being time-consuming, requiring great effort, and incurring high financial costs.…”

Section: Resultsmentioning

confidence: 67%

“…The precision of the laboratory tests, such as direct shear, unconfined compression, or undrained triaxial compression, significantly depend on the quality of the collected sensitive, undisturbed clay samples [2,4], and additionally rely on the thickness and the friction of the sharp edges of the circular samplers with soil. On the other hand, field test results (field vane test and piezocone cone penetration test-CPTU) are also influenced by selection techniques [5,6]. In 2021, Ayadat [4] stated that the field vane shear test is more accurate than the Swedish cone shear test.…”

Section: Introductionmentioning

confidence: 99%

“…ML models are rarely used to estimate undrained shear strength of soft soil based on different geotechnical properties [12,19,20]. In 2022, Tran et al [6] only utilized ML models in the prediction of the undrained shear strength for marine-sensitive soft soil, with a coefficient of correlation of 0.715.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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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.

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Section: Resultsmentioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

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

2022

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“…Recent applications of AI in geotechnical engineering include geotextile [23,24], tunnelling [25], geothermal energy [26], unsaturated flow [27], geo-structural health monitoring [28,29], liquefaction [30], nanotechnology [31], carbon sequestration [32], and soil properties and behaviour prediction [33][34][35]. The ML techniques applied in these past investigations include artificial neural network (ANN), support vector machine (SVM), genetic algorithms (GA), fuzzy logic, image analysis, and adaptive neurofuzzy inference systems (ANFIS).…”

Section: Introductionmentioning

confidence: 99%

Shrink–swell index prediction through deep learning

Teodosio

Wasantha

Yaghoubi

et al. 2022

Neural Comput & Applic

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Growing application of artificial intelligence in geotechnical engineering has been observed; however, its ability to predict the properties and nonlinear behaviour of reactive soil is currently not well considered. Although previous studies provided linear correlations between shrink–swell index and Atterberg limits, obtained model accuracy values were found unsatisfactory results. Artificial intelligence, specifically deep learning, has the potential to give improved accuracy. This research employed deep learning to predict more accurate values of shrink–swell indices, which explored two scenarios; Scenario 1 used the features liquid limit, plastic limit, plasticity index, and linear shrinkage, whilst Scenario 2 added the input feature, fines percentage passing through a 0.075-mm sieve (%fines). Findings indicated that the implementation of deep learning neural networks resulted in increased model measurement accuracy in Scenarios 1 and 2. The values of accuracy measured in this study were suggestively higher and have wider variance than most previous studies. Global sensitivity analyses were also conducted to investigate the influence of each input feature. These sensitivity analyses resulted in a range of predicted values within the variance of data in Scenario 2, with the %fines having the highest contribution to the variance of the shrink–swell index and a relevant interaction between linear shrinkage and %fines. The proposed model Scenario 2 was around 10–65% more accurate than the preceding models considered in this study, which can then be used to expeditiously estimate more accurate values of shrink–swell indices.

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“…Similar results have been reported bySharma and Bora (2003) andYasun (2018) Kayabaşı and Gökçeoğlu 2018;Demir and Şahin 2022),. shallow and deep foundation(Moayedi and Hayati 2019;Liu et al 2020;Mbarak et al 2020;Moayedi et al 2020;Zhang et al 2021;Armaghani et al 2022), soil and pavement(Işık 2009;Kalkan et al 2009;İkizler et al 2010;Akan and Keskin 2019;Dehghanbanadaki et al 2019;Taleb Bahmed et al 2019;Abu-Farsakh and Mojumder 2020;Kayabaşı 2020;Zhai et al 2020;Akbay Arama et al 2021;Tabarsa et al 2021;Kim et al 2022;Lin et al 2022;Tran et al 2022;),…”

mentioning

confidence: 99%

Prediction of the Undrained Shear Strength of the Remolded Soil with Non-Linear Regression, Fuzzy Logic and Artificial Neural Network

Yünkül

KARAÇOR

Gürbüz

et al. 2023

Preprint

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The aim of this study is to predict the undrained shear strength (Cu) of the remolded soil samples and for this purpose, non-linear regression (NLR) analyses, fuzzy logic (FL) and artificial neural network (ANN) modelling were used to assess. Total 1306 undrained shear strength results of soil types of CH, CL, MH and ML from 230 different remolded soil test settings on 21 publications were collected while six different measurement devices were used by researchers. Although water content, plastic limit and liquid limit were used as input parameters for FL and ANN modelling, liquidity index or water content ratio were considered as input parameter for NLR analyses. In NLR analyses, 12 different regression equations were derived for prediction of Cu. Feed-Forward backpropagation and TANSIG transfer function were used for ANN modelling while Mamdani inference system was preferred with trapezoidal and triangular membership function for FL modelling. The experimental results of 914 tests for training of the ANN models, 196 for validation and 196 for testing were used. It was observed that the accuracy of the ANN and FL modellings were higher than NRL analyses. Furthermore, the simple and reliable regression equation was proposed for assessments of Cu values having higher coefficient of determination values (R2).

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Estimation of the undrained shear strength of sensitive clays using optimized inference intelligence system

Cited by 11 publications

References 51 publications

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

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

Shrink–swell index prediction through deep learning

Prediction of the Undrained Shear Strength of the Remolded Soil with Non-Linear Regression, Fuzzy Logic and Artificial Neural Network

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