Elastic Properties of Rocks

Karagianni, Angeliki; Karoutzos, G.; Ktena, S.; Vagenas, N; Vlachopoulos, I.; Sabatakakis, N.; Koukis, G.

doi:10.12681/bgsg.11291

Cited by 13 publications

(7 citation statements)

References 3 publications

(5 reference statements)

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“…Explosive eruptions in subaerial environments are therefore often intense and far-reaching. In contrast, the bulk modulus of water is 2340 MPa, four orders of magnitude less compressible or deformable than air ( Table 2; but >10× times smaller than the Young's modulus of rock, 20000-70000 MPa; Karagianni et al, 2010). The intensity of explosive eruptions will therefore be greatly suppressed in submarine settings relative to their subaerial counterparts, and explosive shock waves produced during the eruption will be greatly attenuated close to source (Resnyansky and Delaney, 2006).…”

Section: Bulk Modulus Of Water: An Overlooked Constraint On Explosivimentioning

confidence: 99%

Why Deep-Water Eruptions Are So Different From Subaerial Eruptions

Raymond

Simmons

2018

Front. Earth Sci.

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Magmas erupted in deep-water environments (>500 m) are subject to physical constraints very different to those for subaerial eruptions, including hydrostatic pressure, bulk modulus, thermal conductivity, heat capacity and the density of water mass, which are generally orders of magnitude greater than for air. Generally, the exsolved volatile content of the erupting magma will be lower because magmas decompress to hydrostatic pressures orders of magnitude greater than atmospheric pressure. At water depths and pressures greater than those equivalent to the critical points of H 2 O and CO 2 , exsolved volatiles are supercritical fluids, not gas, and so have limited ability to expand, let alone explosively. Gas overpressures are lower in deep submarine magmas relative to subaerial counterparts, limiting explosive expansion of gas bubbles to shallower waters. Explosive intensity is further minimized by the higher bulk modulus of water, relative to air. Higher retention of volatiles makes subaqueously erupted magmas less viscous, and more prone to fire fountaining eruption style compared with compositionally equivalent subaerial counterparts. The high heat capacity and thermal conductivity of (ambient) water makes effusively (and/or explosively) erupted magmas more prone to rapid cooling and quench fragmentation, producing non-explosive hyaloclastite breccia. Gaseous subaqueous eruption columns and hot water plumes form above both explosive and non-explosive eruptions, and these can entrain pyroclasts and pumice autoclasts upward. The height of such plumes is limited by the water depth and will show different buoyancy, dynamics, and height and dispersal capacity compared with subaerial eruption columns. Water ingress and condensation erosion of gas bubbles will be major factors in controlling column dynamics. Autoclasts and pyroclasts with an initial bulk density less than water can rise buoyantly, irrespective of plume buoyancy, which they cannot do in the atmosphere. Dispersal and sedimentation of clasts in water is affected by the rate at which buoyant clasts become waterlogged and sink, and by wind, waves, and oceanic currents, which can produce very circuitous dispersal patterns in floating pumice rafts. Floating pumice can abrade by frictional interaction with neighbors in a floating raft, and generate in transit, post-eruptive ash fallout unrelated to explosive activity or quench fragmentation.

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Section: Bulk Modulus Of Water: An Overlooked Constraint On Explosivimentioning

confidence: 99%

Why Deep-Water Eruptions Are So Different From Subaerial Eruptions

Raymond

Simmons

2018

Front. Earth Sci.

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“…In every case, stairs were unimportant from a structural point of view, and were thus not included in any numerical model. According to [ 28 ], the elastic modulus of granite varies from 2.59 GPa to 88.79 GPa, and in the case of sandstone from 3.4 GPa to 71.7 GPa. In another paper [ 33 ], 48.8 GPa is the identified value of the elastic modulus of stone masonry.…”

Section: Discussionmentioning

confidence: 99%

“…The elastic moduli of sandstone and granite were selected based on Ref. [ 28 ]. It was suspected that the granite stairs were rigid and played the major role in lighthouse dynamic behavior, so the initial value was high.…”

Section: Methodsmentioning

confidence: 99%

Material Parameters Identification of Historic Lighthouse Based on Operational Modal Analysis

2020

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In the present paper, the identification of the material parameters of a masonry lighthouse is discussed. A fully non-invasive method was selected, in which the material properties were determined via numerical model validation applied to the first pair of natural frequencies and their related mode shapes, determined experimentally. The exact structural model was built by means of the finite element method. To obtain experimental data for the inverse analysis, operational modal analysis was applied to the structure. Three methods were considered: peak picking (PP), eigensystem realization algorithm (ERA) and natural excitation technique with ERA (NExT-ERA). The acceleration’s responses to environmental excitations, enhanced in some periods of time by sheet piling hammering or by sudden interruptions like wind stroke, were assumed within the analysis input. Different combinations of the input were considered in the PP and NExT-ERA analysis to find the most reasonable modal forms. A number of time periods of a free-decay character were considered in the ERA technique to finally calculate the averaged modal forms. Finally, the elastic modulus, Poisson’s ratio and material density of brick, sandstone and granite masonry were determined. The obtained values supplement the state of the art database concerning historic building materials. In addition, the numerical model obtained in the analysis may be used in further cases of structural analysis.

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“…We assume that the latter is impermeable (as its porosity is very low) and considerably stiff compared to the background soil, so that only rigid body movement is probable. Consequently, the rock is modelled with material properties α = 0, K = 0, M → ∞, E p = 60, 000 MPa, and ν p = 0.25 [45]). The soil has a heterogeneous porosity distribution φ = 0.25z + 0.…”

Section: Example: 3d Model Of a Rock Within Soilmentioning

confidence: 99%

Poroelastic model parameter identification using artificial neural networks: on the effects of heterogeneous porosity and solid matrix Poisson ratio

Dehghani

Zilian

2020

Comput Mech

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Predictive analysis of poroelastic materials typically require expensive and time-consuming multiscale and multiphysics approaches, which demand either several simplifications or costly experimental tests for model parameter identification.This problem motivates us to develop a more efficient approach to address complex problems with an acceptable computational cost. In particular, we employ artificial neural network (ANN) for reliable and fast computation of poroelastic model parameters. Based on the strong-form governing equations for the poroelastic problem derived from asymptotic homogenisation, the weighted residuals formulation of the cell problem is obtained. Approximate solution of the resulting linear variational boundary value problem is achieved by means of the finite element method. The advantages and downsides of macroscale properties identification via asymptotic homogenisation and the application of ANN to overcome parameter characterisation challenges caused by the costly solution of cell problems are presented. Numerical examples, in this study, include spatially dependent porosity and solid matrix Poisson ratio for a generic model problem, application in tumour modelling, and utilisation in soil mechanics context which demonstrate the feasibility of the presented framework. Keywords Artificial neural network • Multiscale and multiphysics problems • Poroelastic media • Material characterisation • Data-driven computational mechanics B Hamidreza Dehghani

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Elastic Properties of Rocks

Abstract: Abstract

Cited by 13 publications

References 3 publications

Why Deep-Water Eruptions Are So Different From Subaerial Eruptions

Why Deep-Water Eruptions Are So Different From Subaerial Eruptions

Material Parameters Identification of Historic Lighthouse Based on Operational Modal Analysis

Poroelastic model parameter identification using artificial neural networks: on the effects of heterogeneous porosity and solid matrix Poisson ratio

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