Dynamic soil properties for seismic ground response studies in Northeastern India

Dammala, Pradeep Kumar; Krishna, A. Murali; Bhattacharya, Subhamoy; Nikitas, Georgios; Rouholamin, Mehdi

doi:10.1016/j.soildyn.2017.06.003

Cited by 46 publications

(4 citation statements)

References 28 publications

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“…The dynamic shear modulus (G) of present silt-rich fly ash was found to be in the range of 3000-5100 kPa which is in the range of G value of pond ash (3250-6600 kPa) reported by Reddy et al [30]. In addition, the dynamic properties (G and D) of present fly ash were found to be in the range of dynamic properties of sand reported by Dammala et al [109] and Li et al [110]. Kalinski and Wallace [92]investigated the dynamic properties of fly ash using a resonant column test.…”

Section: Comparison With the Past Literature Worksupporting

confidence: 58%

Experimental investigation on dynamic behavior of silt-rich fly ash using cyclic triaxial and bender element tests

Ram

Mohanty

2021

Innov. Infrastruct. Solut.

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The present study mainly focuses on the evaluation of dynamic properties of silt-rich fly ash from Renukoot, India, under large and small strain conditions using cyclic triaxial and bender element test, respectively. The influence of confining pressure, loading frequency, relative compaction, and shear strain amplitude on dynamic properties of fly ash under small strain and large strain condition has also been studied. The dynamic shear modulus (G) found experimentally from cyclic triaxial test shows the decay in a nonlinear manner with the increment of loading frequency and cyclic shear strain. The increment in dynamic shear modulus of fly ash was noticed with the increment of relative compaction and confining pressure. Similarly, the increase in small strain shear modulus (G max ) was observed with the increment of confining pressure, relative compaction, and frequency of the waveform. The decrease in damping ratio was observed with an increase in shear strain, especially for higher strain side (i.e., γ > 0.5%) but vice versa is not true for lower strain side (γ < 0.5%). Therefore, irrespective of the soil type, there exists a threshold strain beyond which damping ratio decreases with an increase in shear strain. All the parameters except loading frequency have a prominent impact on G and G max is sensitive to the entire considered factors. Finally, empirical correlations were developed between normalized shear modulus ratio (G/G max ) and damping ratio (D) of silt-rich fly ash with cyclic shear strain (γ) at different confining pressure, relative compaction, and loading frequency. The developed correlations can be helpful in calculating the dynamic properties of Renukoot fly ash at different strain levels. The developed correlations will also assist in the safe design of structures with fly ash as geo-material in the seismic-prone regions.

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Section: Comparison With the Past Literature Worksupporting

confidence: 58%

Experimental investigation on dynamic behavior of silt-rich fly ash using cyclic triaxial and bender element tests

Ram

Mohanty

2021

Innov. Infrastruct. Solut.

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“…Numerous EMs have been presented for the prediction of dynamic properties of soils [37][38][39]. Despite their inherent limitations in terms of generalizability, these EMs offer the advantage of simplicity.…”

Section: Empirical Modelsmentioning

confidence: 99%

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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“…) 2016) is preferred to account for soil softening during liquefaction (Bhattacharya et al 2021 andAmani et al 2022). Any structural program (e.g., SAP2000) can be used to perform nonlinear dynamic analysis using multi-support excitation (Dammala et al 2017b and 2019) approach using the displacement time histories obtained from nonlinear GRA.…”

Section: Foundation Design For Owtmentioning

confidence: 99%

Offshore Wind Farms as Additional Coolant Power Sources to Enhance Seismic Resilience of Nuclear Power Plants– a Case Study

et al. 2022

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Dynamic soil properties for seismic ground response studies in Northeastern India

Cited by 46 publications

References 28 publications

Experimental investigation on dynamic behavior of silt-rich fly ash using cyclic triaxial and bender element tests

Experimental investigation on dynamic behavior of silt-rich fly ash using cyclic triaxial and bender element tests

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

Offshore Wind Farms as Additional Coolant Power Sources to Enhance Seismic Resilience of Nuclear Power Plants– a Case Study

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