Normalised Modulus Reduction and Material Damping Ratio Curves for Bay of Campeche Clay

Taboada, VM; Dantal, V; Roque, Diego Cruz; Lopez, FA Flores; Monroy, RE Vazquez; Nabor, Procoro Barrera

doi:10.3723/osig17.457

Cited by 2 publications

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“…Figure4shows the effect of relative density on the dynamic properties of granular soils. The results show that with an increase in relative density, G/Gmax increases, and D decreases, aligning with prior research findings[60][61][62]. Further elaboration and comprehensive findings stemming from these tests have been disseminated through detailed publications by Bayat and Ghalandarzadeh[58,63].…”

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confidence: 85%

Prediction of Normalized Shear Modulus and Damping Ratio for Granular Soils Over a Wide Strain Range Using Deep Neural Network Modelling

Bayat,

Mousavi,

et al. 2023

Preprint

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Dynamic properties (i.e., shear modulus and damping ratio) of geomaterials play a vital role in civil engineering applications and are essential for reliable dynamic response analysis. This paper presents a novel approach for predicting the normalized shear modulus (G/Gmax) and damping ratio (D) of granular soils across a wide strain range using a Deep Neural Network (DNN) modeling strategy. Traditional methods for predicting these properties often rely on empirically derived relationships that may not capture the full complexity of granular soil behavior under varying strain conditions. A comprehensive dataset of shear modulus and damping ratio measurements from laboratory cyclic triaxial (CT) and resonant column (RC) tests conducted under various conditions is utilized. The dataset covers a wide range of strain levels, allowing for a more robust and versatile modeling approach. For predicting the G/Gmax and D of granular soils, a Deep Feed-Forward Neural Network (DFFNN) model was developed to learn the features from input data. The proposed model considers the influence of grading characteristics (Gravel Content, GC, median particle size, D50, Uniformity Coefficient, Cu, and Coefficient of Curvature, Cc), shear strain (\(\gamma\)), void ratio (e), mean effective confining pressure (\({\sigma ^{\prime}_m}\)), consolidation stress ratio (KC) and specimens’ preparation method (S-P) as input data. The empirical models (EMs) and three other intelligent techniques, namely Shallow Neural Network (SNN), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR) were used for comparison. The testing accuracy of the proposed DFFNN for predicting the G/Gmax and D was 0.9830 and 0.9396, respectively. The results demonstrate that the proposed DFFNN modeling strategy provides a highly accurate means of predicting G/Gmax and D for granular soils across a broad shear strain range. This method offers advantages over EMs by incorporating a data-driven approach that can adapt to the specific behavior of different granular soil types and loading conditions.

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confidence: 85%

Prediction of Normalized Shear Modulus and Damping Ratio for Granular Soils Over a Wide Strain Range Using Deep Neural Network Modelling

Bayat,

Mousavi,

et al. 2023

Preprint

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Normalized Modulus Reduction and Damping Ratio Curves for Bay of Campeche Calcareous Clay to Carbonate Mud

Taboada

Cao

Lopez

et al. 2021

Day 1 Mon, August 16, 2021

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Equations to calculate the modulus reduction curve (G/Gmax-γ) and material damping ratio curve (D-γ) of calcareous clay and clayey carbonate mud of the Bay of Campeche and Tabasco Coastline are developed. This was achieved using a database of 156 resonant column tests and 468 strain-controlled cyclic direct simple shear tests performed in clays with 10 % ≤ CaCO3 ≤90 %. The effects of carbonate content (CaCO3), mean effective confining pressure (σ′m), plasticity index (PI), and overconsolidation ratio (OCR) on the shape of the modulus reduction and material damping ratio curves are shown based on the available laboratory data and the equations developed to calculate these curves. It is shown that as CaCO3 increases, the normalized shear modulus (G/Gmax) curve tends to shift downward and the damping ratio (D) curve tends to shift upward; as σ′m and PI increase, the G/Gmax curve tends to shift upward and the damping ratio curve tends to shift downward; and the value of OCR has practically no effect on the position of the curves. The validation of the calculated values of G/Gmax and D shows the best predictions are found at low shear strains for G/Gmax and at large shear strains for D, falling within ± 25 % of the measured values, and shows that due to limitations in the model at large strains (γ > 1 %) for G/Gmax and at low strains (γ < 0.05 %) for D, the calculated values fall within ± 50 % of the measured values. The equations developed to calculate the curves of G/Gmax-γ and D-γ of calcareous clay and clayey carbonate mud are recommended for preliminary or perhaps even final seismic site response evaluations. However, considering the scatter of the data points around the curves, the equations should be used with caution, and parametric and sensitivity studies are strongly recommended to assess the importance of this scatter. In large critical projects, direct experimental determinations of G/Gmax and D for the soils of interest are suggested to be more appropriate.

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Normalised Modulus Reduction and Material Damping Ratio Curves for Bay of Campeche Clay

Cited by 2 publications

References 0 publications

Prediction of Normalized Shear Modulus and Damping Ratio for Granular Soils Over a Wide Strain Range Using Deep Neural Network Modelling

Prediction of Normalized Shear Modulus and Damping Ratio for Granular Soils Over a Wide Strain Range Using Deep Neural Network Modelling

Normalized Modulus Reduction and Damping Ratio Curves for Bay of Campeche Calcareous Clay to Carbonate Mud

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