Package 'newmark'

Description

Drop-in replacement for stats::approx. Uses the Catmull-Rom spline (with the same smoothing parameter as Highcharts type = "spline") instead of straight-line segments. The call signature and return value are identical to stats::approx, so it can be substituted transparently.

Usage

approx.spline(
  x,
  y = NULL,
  xout = NULL,
  n = 50,
  rule = 1,
  log = FALSE,
  smoothing = 1.5,
  ...
)

Arguments

Value

A list with components x and y, just like stats::approx.

See Also

approx

Examples

x <- c(0.05, 0.10, 0.20, 0.30, 0.50, 1.0, 2.0, 3.0)
y <- exp(-x)
Td <- seq(0.05, 3.0, length.out = 100)
approx.spline(x, y, xout = Td, log = TRUE)$y

Description

Assembles an annual-exceedance-probability (AEP) table, uniform-hazard-spectrum (UHS) table, and–where available–a magnitude-distance disaggregation table, using OpenQuake or user-supplied hazard files.

Usage

buildGMDP(
  path,
  IDo = "GEM",
  engine = "openquake",
  vref = 760,
  TRo = c(100, 200, 500, 1000, 2000, 2500, 5000, 10000)
)

Arguments

Value

A named list with AEPTable, UHSTable, and RMwTable (NULL if disaggregation is unavailable).

Examples

## Not run: 
# Requires an 'OpenQuake' Engine output directory containing classical
# PSHA + disaggregation ZIPs; not bundled with the package.
out <- buildGMDP(path = "path/to/openquake/output", vref = 760)

## End(Not run)

Description

Build a monotone quantile spline Q(u) for ln(Sa)

Usage

buildQSpline(SaTable)

Arguments

Value

A closure Q(u) returning ln(Sa) for any u in[0,1].

Examples

tbl <- data.table::data.table(p = c(0.16, 0.50, 0.84), Sa = c(0.3, 0.5, 0.8))
Q <- buildQSpline(tbl)
Q(0.5)   # median ln(Sa)

Description

Ensures that, for every combination TR, Vs30, Vref, ID, Tn, there is exactly one row per probability p and that Sa(p) is non-decreasing.

Usage

checkUHS(UHSTable, epsMonot = 1e-06)

Arguments

Value

invisibly TRUE; stops on error.

Examples

## Not run: 
# Requires a fully populated UHSTable produced by buildGMDP() or an
# equivalent upstream pipeline (must include the ID column that
# disaggregates per-quantile rows). Not bundled with the package.
checkUHS(UHSTable)

## End(Not run)

Description

This dataset contains cylinder roots.

Usage

data(CylinderRoots)

Format

A data frame with 307128 rows and 4 variables:

n

Integer: Mode number (1-8)

m

Double: Inhomogeneity ratio (0-0.95)

l

Double: Aspect Ratio (0-0.5)

an

Double: Cylinder roots

Description

Builds the two-period design/MCER spectrum shape of ASCE 7-22 Sec. 11.4.5.2 (Eqs. 11.4-3/-4/-5, DESIGN = 2/3 x MCER), with the spectral parameters derived from the input site-specific spectrum per ASCE 7-22 Sec. 21.4: at MCER level, SMS = 0.9 x max SaM(T) over T from 0.2 to 5 s, and SM1 = max(0.9 x max of T x SaM(T), SaM(1 s)) over T from 1 to 2 s when vs30 > 442 m/s, else 1 to 5 s (the 1.5 and 2/3 factors of Eqs. 21.4/11.4-1/-2 cancel at MCER level). Evaluation windows are clamped to the period range of the input. The Sec. 21.2.3 80-percent floor and the Sec. 21.2.2 deterministic cap are NOT applied (both require mapped/deterministic inputs this function does not receive). See NEWS.md for the audit summary.

Usage

designUHS(UHS, TL = 8, spectrum = c("MCER", "DESIGN"), vs30 = NULL)

Arguments

Details

The input UHS slice must contain at least Tn and SaF. Typical usage passes a single TR and p == "mean".

Value

data.table with columns ⁠Tn, SaF⁠.

Examples

uhs <- data.table::data.table(Tn = c(0, 0.2, 0.5, 1, 2), SaF = c(0.4, 1.0, 0.9, 0.6, 0.3))
designUHS(uhs)

Description

Ambraseys & Menu (1988) rigid sliding-block model

Usage

Dn_AM88(PGA, ky)

Arguments

Value

data.table(muLnD, sdLnD, ID = "AM88")

Examples

Dn_AM88(PGA = 0.5, ky = 0.1)

Description

Bray & Macedo (2017) - subduction/interface events

Usage

Dn_BM17(ky, Sa, Ts, Mw = 7.5)

Arguments

Value

data.table(muLnD, sdLnD, ID = "BM17")

Examples

Dn_BM17(ky = 0.1, Sa = 0.8, Ts = 0.3, Mw = 8.0)

Description

Multi-equation structure per BM23 (Soil Dynamics and Earthquake Engineering 168, 107835, DOI 10.1016/j.soildyn.2023.107835): Eq (10): ordinary motions (default; see pulse) Eq (6): D100 max-component pulse equation Eq (8): D50 median-component pulse equation with a sub-regime split at PGV = 150 cm/s within Eqs (6) and (8).

Usage

Dn_BM19(ky, Ts, Sa, PGA, PGV, Mw = 6.5, NFC = "D100", pulse = FALSE)

Arguments

Value

data.table(muLnD, sdLnD, ID = "BM19")

Examples

Dn_BM19(ky = 0.1, Ts = 0.3, Sa = 0.8, PGA = 0.5, PGV = 80, Mw = 7.0)

Description

Bray & Travasarou (2007) flexible sliding-block model

Usage

Dn_BT07(ky, Sa, Ts, Mw = 6.5)

Arguments

Value

data.table(muLnD, sdLnD, ID = "BT07")

Examples

Dn_BT07(ky = 0.1, Sa = 0.8, Ts = 0.3, Mw = 7.0)

Description

Implements Jibson (2007) Eq. 10 (Arias intensity + critical acceleration ratio; sigma_log10 = 0.616), one of the four regressions published in that paper.

Usage

Dn_JB07(PGA, ky, AI = NULL)

Arguments

Value

data.table(muLnD, sdLnD, ID = "JB07")

Examples

Dn_JB07(PGA = 0.5, ky = 0.1)

Description

Implements the two-parameter (PGA, Arias intensity) vector model (Saygili & Rathje 2008 Eq. 6, Tables 1-2), including its amplitude-dependent sigma 0.46 + 0.56 (ky/PGA). Note the authors' recommended vector model was (PGA, PGV); the (PGA, Ia) variant is published and adopted here for consistency with the Arias-based rigid ensemble.

Usage

Dn_SR08(PGA, ky, AI = NULL)

Arguments

Value

data.table(muLnD, sdLnD, ID = "SR08")

Examples

Dn_SR08(PGA = 0.5, ky = 0.1)

Description

Implements the period-dependent, Vs30-dependent NGA-East site amplification recommendations of the 2017 PEER-report generation (nonlinear block transcribed from PEER Report 2017/05, Table 2.1; the historical "ST17" id is kept for backward compatibility) for the natural-log mean and standard deviation of the amplification factor $F(T_n,PGA,V_{s30})$. The nonlinear sigma applies the PGA-dependent multiplier of the source model, $\sigma_{F_{nl}} = \sigma_{f2}\,\ln((PGA_{3000}+f_3)/f_3)$ (corrected 2026-07; earlier versions used bare $\sigma_{f2}$, making sdLnF amplitude-independent). For the final 2020 model generation see F_ST20.

Usage

F_ST17(PGA, Tn, vs30, vref = 760, Vl = 200, Vu = 2000)

Arguments

Details

If vs30 == vref, the site factor equals 1 deterministically, so the function returns muLnF = 0 and sdLnF = 0 (zero dispersion; rnorm(n, 0, 0) returns zeros and ln F = 0).

Value

A data.table with columns:

muLnF

natural-log mean of $F$

sdLnF

natural-log standard deviation of $F$ (set to 0 when vs30 == vref)

ID

character string "ST17"

References

Stewart, J.P., Parker, G.A., Harmon, J.A., Atkinson, G.M., Boore, D.M., Darragh, R.B., Silva, W.J. & Hashash, Y.M.A. (2017) "Expert Panel Recommendations for Ergodic Site Amplification in Central and Eastern North America." PEER Report 2017/04 (linear model and F760 tables, transcribed from its electronic supplement).

Hashash, Y.M.A., Harmon, J., Ilhan, O., Stewart, J.P., Rathje, E.M., Campbell, K.W., Silva, W.J. & Xu, B. (2017) "Recommendation for Ergodic Nonlinear Site Amplification in Central and Eastern North America." PEER Report 2017/05 (nonlinear model, Table 2.1).

Description

Final-generation NGA-East ergodic site-amplification model: linear component and F760 impedance/gradient weighting per Stewart et al. (2020), nonlinear component per Hashash et al. (2020), with the period-dependent nonlinear reference velocity (760 m/s for Tn < 0.4 s, 3000 m/s above) and the PGA-dependent nonlinear sigma $\sigma_{F_{nl}} = \sigma_{f2}\,\ln((PGA_{3000}+f_3)/f_3)$. Coefficients are the USGS nshmp-lib table (see data-raw/SiteAmp2020.R for provenance); reference implementation cross-checked function-by-function against OpenQuake gsim/nga_east.py.

Usage

F_ST20(PGA, Tn, vs30, vref = 760)

Arguments

Details

Conventions:

• The published model is referenced to 3000 m/s. For vref = 760 the mean is the exact difference $F(V_{s30}) - F(760)$ of the composite model (including the F760 weighting difference below 600 m/s and the small $F_{nl}(760)$ term at Tn >= 0.4 s), and the input PGA (at 760 m/s) is converted to the 3000 m/s anchor via $PGA_{3000} = PGA_{760}/2.275$ (PEER 2017/05 Eq. 2.4).

• sdLnF is evaluated at the target vs30 only; for vref = 760 no reference-side sigma is added (the rock hazard carries its own dispersion). For vref = 3000 the weighted F760 sigma enters the SRSS.

• vs30 == vref returns muLnF = 0, sdLnF = 0 (deterministic unit factor, same contract as F_ST17).

• Coefficients interpolate linearly in $\ln T_n$ (OpenQuake CoeffsTable rule); Tn = 0 uses the PGA row; Tn above the published 10 s limit is clamped to 10 s with a warning.

Value

A data.table with columns muLnF, sdLnF, ID = "ST20".

References

Stewart, J.P. et al. (2020) "Ergodic site amplification model for central and eastern North America." Earthquake Spectra 36(1). Hashash, Y.M.A. et al. (2020) "Nonlinear site amplification model for ergodic seismic hazard analysis in central and eastern North America." Earthquake Spectra 36(1).

Examples

# soft-soil amplification of the 0.2 s ordinate, hazard at 760 m/s
F_ST20(PGA = c(0.05, 0.2, 0.5), Tn = 0.2, vs30 = 300)

# very-hard-rock reference (3000 m/s)
F_ST20(PGA = 0.1, Tn = 1.0, vs30 = 760, vref = 3000)

Description

Evaluates ensemble and per-submodel Dn over a vector of ky values. The scenario is sampled once and the same epsilon is reused across all ky so that each realisation s represents a coherent physical curve Dn_s(ky).

Usage

fitDnCurve(
  uhs,
  ky,
  Ts,
  Mw = 6.5,
  NS = 30L,
  weights,
  NFC = "D100",
  pulse = FALSE
)

Arguments

Value

list(curve, draws). curve = data.table(ky, p, Dn, IDn, w). draws = data.table(ky, s, Dn, IDn) - always populated (possibly empty when no model produces output). Dn in cm.

Examples

## Not run: 
uhs <- data.table::fread(
  system.file("extdata", "uhs.csv", package = "newmark"))
out <- fitDnCurve(uhs, ky = getDnKy(uhs, Ts = 0.3), Ts = 0.3, Mw = 7.5,
                  NS = 100, weights = c(AM88=1, BT07=1, SR08=1, BM17=1))

## End(Not run)

Description

Newmark displacement for a single submodel and scenario

Usage

fitDnModel(uhs, ky, Ts, Mw = 6.5, NS = 30L, IDn, NFC = "D100", pulse = FALSE)

Arguments

Value

data.table with columns p, Dn, IDn. Dn in cm.

Examples

## Not run: 
uhs <- data.table::fread(
  system.file("extdata", "uhs.csv", package = "newmark"))
fitDnModel(uhs, ky = 0.1, Ts = 0.3, Mw = 7.5, NS = 100, IDn = "AM88")

## End(Not run)

Description

Fit Newmark displacement for one scenario row

Usage

fitDnScenario(row, weights)

Arguments

Value

list with curve and draws data.tables. curve includes individual model rows and aggregate IDn rows: ensemble, rigid, and flexible. draws contains raw per-realisation model draws.

Description

Fit Newmark displacement for multiple scenario rows

Usage

fitDnScenarios(records, weights, useParallel = FALSE, workers = 1L)

Arguments

Value

list with row-bound curve and draws data.tables.

Description

Fit kmax for one scenario row

Usage

fitKmaxScenario(row, draws, weights, Da)

Arguments

Value

data.table with Da, p, kmax, PGA, Kh, TR, IDg, and IDn.

Description

Fit kmax for multiple scenario rows

Usage

fitKmaxScenarios(
  records,
  draws,
  weights,
  Da,
  useParallel = FALSE,
  workers = 1L
)

Arguments

Value

row-bound data.table of kmax results.

Description

Fundamental period of a layered soil column (fixed base, free surface) by the classical Rayleigh quotient with the static- deflection trial shape (e.g., Dobry, Oweis & Urzua 1976): under a uniform horizontal body force the shear strain at depth z is z / Vs(z)^2 (uniform density), the mode shape is its integral from the base, and

$T_s = 2\pi\sqrt{\sum h_i X_i^2 / \sum h_i X_i}$

with X_i the shape at each layer midpoint. For a uniform profile this yields 3.974 H/Vs (exact: 4 H/Vs, -0.7 percent); across layered test profiles the error against the exact transfer-matrix solution is a few percent on the low side. Layer order (top-down or bottom-up) is irrelevant: layers are sorted internally by depth.

Usage

fitModel.Ts(VSm, hs, zm)

Arguments

Value

Double. Site's fundamental period Ts in seconds

Description

Computes site-amplified spectral acceleration using two modes: (A) If the input UHS contains only p == "mean", rock Sa is treated as deterministic and the dispersion in SaF comes solely from the site-response model (sdLnF), sampled on the natural-log scale. (B) If the input UHS contains numeric quantiles (e.g., "0.10", "0.50", ...), ln Sa(Tn) is sampled from those quantiles using an empirical inverse quantile function (buildQSpline). Dependence between PGA and Sa(Tn) is represented by a per-period Gaussian "star" copula with the Baker & Jayaram (2008) correlation rhoBJ(Tn, T0 = 0), which has no distance dependence. For each Monte Carlo draw, the site-response model is evaluated with the paired PGA draw, and ln SaF = ln Sa + ln F with ln F ~ Normal(muLnF, sdLnF). Independence between ln Sa and ln F is assumed. Each sampled vector is re-centered to the tabulated mean of its marginal.

Usage

fitSaF(
  uhs,
  vs30,
  vref = 760,
  ns = 1000,
  models = "ST20",
  p_TARGET = c(0.05, 0.1, 0.16, 0.5, 0.84, 0.9, 0.95)
)

Arguments

Details

For scenario (DSHA) tables with analytic mixture components available, prefer fitSaFMixture (exact, sampling-free).

Requirements:

• The input table must have columns Tn, p, Sa.

• It must include exactly one row (Tn == 0, p == "mean") providing PGA on rock.

Output columns: Tn, p, Sa, SaF, AF, where AF = SaF / Sa. In mode (B), the same numeric p values found in the input are returned plus "mean". In mode (A), the set of p values is taken from p_TARGET plus "mean".

Value

data.table with columns Tn, p, Sa, SaF, AF.

Examples

## Not run: 
uhs <- data.table::fread(
  system.file("extdata", "uhs.csv", package = "newmark"))
fitSaF(uhs, vs30 = 360, vref = 760, ns = 500)

## End(Not run)

Description

Exact (sampling-free) composition of a scenario rock-hazard mixture with the lognormal site-response dispersion. The rock demand per period is a finite mixture of lognormal components indexed by rupture-variant x GMPE-branch (the DSHA scenario engine output); conditioning on the component and on its same-realization rock PGA, $\ln SaF$ is exactly normal:

$\mu_k(q) = m_k(T_n) + \rho\,\frac{s_k(T_n)}{s_k(0)}(q - m_k(0)) + \mu_{\ln F}(T_n, V_{s30}, e^q)$

$\sigma_k(q)^2 = (1-\rho^2)\,s_k(T_n)^2 + \sigma_{\ln F}(T_n, V_{s30}, e^q)^2$

so the pooled CDF is a mixture of one-dimensional Gaussian integrals over the PGA channel, evaluated here by Gauss–Hermite quadrature (nq nodes per component) and inverted monotonically. The same inverter produces the rock quantiles Sa, so Sa and SaF are quantiles of the same class of random object and AF = SaF/Sa is the amplification of the p-quantile. The within-component correlation $\rho(PGA, Sa(T_n))$ is the Baker–Jayaram (2008) within-event model (rhoBJ). No random numbers are drawn: outputs are byte-stable.

Usage

fitSaFMixture(
  gmm,
  vs30,
  vref = 760,
  models = "ST20",
  p_TARGET = c(0.05, 0.1, 0.16, 0.5, 0.84, 0.9, 0.95),
  nq = 32
)

Arguments

Details

If vs30 == vref the site factor is deterministically 1 and SaF = Sa exactly.

Value

data.table with columns Tn, p, Sa, SaF, AF; one row per (Tn, p) with p = p_TARGET plus "mean".

Examples

# 6-component scenario mixture (2 rupture variants x 3 GMPE branches)
gmm <- data.table::CJ(Tn = c(0, 0.2, 1), k = 1:6)
gmm[, `:=`(mu = log(0.2) + 0.1 * k - 0.6 * Tn, sigma = 0.6, w = 1)]
fitSaFMixture(gmm, vs30 = 360, nq = 16)

Description

Builds one Monte Carlo realisation of a layered soil column from a total height Hs and a USCS classification, samples random geotechnical properties (void ratio, unit weight, plasticity, shear modulus parameters), computes the fundamental period Ts via Rayleigh's method (fitModel.Ts) and the Vs30-equivalent shear-wave velocity. getSiteProperties calls this function repeatedly to assemble the Monte Carlo ensemble; call directly when one realisation suffices.

Usage

geSiteTable(
  Hs,
  Water = 0,
  USCS,
  Group = NULL,
  h = 0.5,
  DrID = NULL,
  Vref = 760,
  UniformDistribution = TRUE,
  POP = 100,
  IgnoreModelIntervals = TRUE,
  getSiteLayers = FALSE
)

Arguments

Value

data.table with one row per (Hs, POP, Hw, sample) combination and columns Hs, Hw, NL, Z500, Z1000, SID, Go, mo, Ts, VSo, VS30, plus USCS-fraction columns.

Description

Returns the eigenvalue an of mode no for a Gazetas & Dakoulas (1985) inhomogeneous truncated shear beam parameterised by inhomogeneity ratio mo and truncation ratio lo. The value is interpolated from the precomputed CylinderRoots table using the requested model (linear glm, non-linear glm, decision tree, or random forest).

Usage

getCylinderRoots(mo, lo, no = 1, model = "nlm", extrapolate = TRUE, OSF = 0.1)

Arguments

Value

Double. Characteristic root an for the requested mode and parameters.

Examples

getCylinderRoots(mo = 0.45, lo = 0.20, no = 1, model = "nlm")

Description

Derives the effective intensities PGA, Sa(1.3Ts), Sa(1.5Ts) from the scenario UHS and constructs a log-spaced ky grid covering the calibrated range of the displacement models.

Usage

getDnKy(uhs, Ts, kyN = 30L, pRef = "mean")

Arguments

Details

Default bounds come from the calibration ranges of the models in the ensemble:

• lower: 0.01g (BM17 lowest calibration point)

• upper: max(PGA, 0.80g). The physical limit is ky < PGA (no slip below the yield acceleration); the 0.80g floor extends the grid for low-PGA scenarios up to BM17's calibration ceiling.

Value

numeric vector of ky values (log-spaced)

Examples

uhs <- data.table::data.table(Tn = 0, p = "mean", Sa = 0.5)
getDnKy(uhs, Ts = 0.3)

Description

Returns the ky bounds within which each model's regression is calibrated. For rigid models (AM88, SR08) the bounds are relative to PGA. For flexible models the bounds are absolute.

Usage

getKyLimits(PGA)

Arguments

Value

data.table with columns IDn, ky.lo, ky.hi

Examples

getKyLimits(PGA = 0.5)

Description

Generates NR realisations of synthetic soil profiles for a given total height Hs and USCS classification, then summarises the resulting Ts, mo, Go, VSo and VS30 by user-requested quantile levels (and/or the mean). Each realisation is built by geSiteTable().

Usage

getSiteProperties(
  Hs,
  USCS,
  POP = 100,
  Water = 0,
  NR = 1,
  h = 1,
  levels = c(0.05, 0.5, "mean", 0.95),
  Vref = 760
)

Arguments

Value

data.table with one row per (Hs, POP, Hw, level) combination and columns USCS, Go, mo, Ts, VSo, VS30, Z500, Z1000, level. Numeric properties are rounded for readability.

Examples

getSiteProperties(
  Hs   = 30,
  USCS = c("GC", "CL", "ML"),
  NR   = 50,
  levels = c(0.16, "mean", 0.84)
)

Description

Interpolate Sa(p) at arbitrary period using log-log interpolation

Usage

interpolateSaTable(uhs, Tn)

Arguments

Value

data.table with interpolated Tn, p, Sa

Examples

uhs <- data.table::data.table(
  Tn = c(0.2, 0.5, 1.0, 0.2, 0.5, 1.0),
  p  = c("0.16", "0.16", "0.16", "0.84", "0.84", "0.84"),
  Sa = c(0.8, 0.6, 0.3, 1.2, 0.9, 0.5)
)
interpolateSaTable(uhs, Tn = 0.3)

Description

For each realisation s, inverts each model's Dn(ky) curve to kmax(Da), builds weighted ensemble mean, then averages over realisations.

Usage

invertDnDraws(draws, Da, weights, p = c(0.05, 0.1, 0.16, 0.84, 0.9, 0.95))

Arguments

Details

Log-log linear extrapolation is used when Da falls outside the support of the computed Dn(ky) curve. No calibration-range clamping is applied - the natural support of the curve bounds the inversion.

All inputs must be in the same displacement units (cm).

Value

data.table(Da, p, kmax). Column p is character: "mean" or the numeric quantile formatted as "0.84", "0.90", etc.

Examples

## Not run: 
uhs <- data.table::fread(
  system.file("extdata", "uhs.csv", package = "newmark"))
out <- fitDnCurve(uhs, ky = getDnKy(uhs, Ts = 0.3), Ts = 0.3, Mw = 7.5,
                  NS = 100,
                  weights = c(AM88=1, BT07=1, SR08=1, BM17=1))
invertDnDraws(out$draws, Da = c(2.5, 5, 10),
              weights = c(AM88=1, BT07=1, SR08=1, BM17=1))

## End(Not run)

Description

Computes the Pearson correlation coefficient between the residuals of ln(Sa) at two oscillator periods, using the canonical predictive equation of Baker and Jayaram (2008), Earthquake Spectra 24(1), 299-317 (DOI 10.1193/1.2857544), Eqs. 5 and 6 verbatim.

Usage

rhoBJ(Tn, T0 = 0.01)

Arguments

Details

The model has no dependence on rupture distance or magnitude. Periods of zero are treated as 0.01 s (standard PGA proxy in B&J 2008).

Form (Eqs. 5-6 of B&J 2008): C1 = 1 - cos(pi/2 - 0.366 * ln(Tmax / max(Tmin, 0.109))) C2 = (1 - 0.105 * (1 - 1/(1 + exp(100*Tmax - 5))) * (Tmax-Tmin)/(Tmax-0.0099)) if Tmax < 0.2; else 0 C3 = C2 if Tmax < 0.109; else C1 C4 = C1 + 0.5 * (sqrt(C3) - C3) * (1 + cos(pi * Tmin / 0.109))

rho = C2 if Tmax < 0.109 = C1 else if Tmin > 0.109 = min(C2, C4) else if Tmax < 0.2 = C4 otherwise

Validity: 0.01 <= T1, T2 <= 10 s.

Value

Numeric scalar correlation coefficient rho(T0, Tn).

References

Baker, J. W. & Jayaram, N. (2008). Correlation of Spectral Acceleration Values from NGA Ground Motion Models. Earthquake Spectra, 24(1), 299-317. DOI: 10.1193/1.2857544.

Examples

rhoBJ(Tn = 0.3, T0 = 0.01)   # rho(PGA, 0.3 s)
rhoBJ(Tn = 1.0, T0 = 0.5)

Description

Computes site-amplified spectral acceleration Sa_F using the 2017-generation NGA-East non-linear site factor model (PEER Report 2017/05 lineage; see F_ST17). This wrapper calls the underlying F_ST17() function.

Usage

SaF_ST17(Sa, pga, Tn, vs30, vref = 760)

Arguments

Value

A data.table with columns:

muLnSaF

natural-log mean of amplified Sa

sdLnSaF

natural-log standard deviation of amplified Sa

ID

character string "ST17"

References

Hashash, Y.M.A. et al. (2017) "Recommendation for Ergodic Nonlinear Site Amplification in Central and Eastern North America." PEER Report 2017/05, Pacific Earthquake Engineering Research Center.

See Also

F_ST17 for the underlying site factor model

fitSaF for Monte-Carlo site amplification analysis

Examples

library(data.table)

# Site amplification for soft soil site
Sa_rock <- c(0.1, 0.2, 0.3, 0.4)
PGA_rock <- c(0.1, 0.1, 0.1, 0.1)

result <- SaF_ST17(
  Sa = Sa_rock,
  pga = PGA_rock,
  Tn = 0.5,      # 0.5s period
  vs30 = 300,    # Soft soil
  vref = 760     # Rock reference
)

print(result)

Description

Draw a correlated sample of spectral accelerations

Usage

sampleSaCorr(uhs, Tn, rho, NS)

Arguments

Value

matrix of dimension NS x length(Tn) with Sa draws (g).

Examples

## Not run: 
uhs <- data.table::data.table(
  Tn = c(0, 0, 0, 1, 1, 1),
  p  = c("0.16", "mean", "0.84", "0.16", "mean", "0.84"),
  Sa = c(0.3, 0.5, 0.7, 0.1, 0.2, 0.3)
)
rho <- rhoBJ(Tn = 1.0, T0 = 0.01)
mat <- sampleSaCorr(uhs, Tn = c(0, 1), rho = rho, NS = 100)

## End(Not run)

Description

This dataset contains hear Model Parameters from Ishihara (1996)

Usage

data(ShearModelParameters)

Format

A data frame with 20 rows and 10 variables:

ModelID

Character: Model ID

GroupID

Character: Group ID

NameID

Character: NameID

AuthorID

Character: AuthorID

emin

Double: Min Void Ratio

emax

Double: Max Void Ratio

A

Double: A

Ce

Double: Ce

n

Double: Ce

UN

Character: Go Units

Description

Convert Site Class to Vs30 in m/s

Usage

SIDtoVs30(SID = NULL)

Arguments

Value

Vs30 in m/s

Examples

SIDtoVs30("A")

SIDtoVs30("AB")

SIDtoVs30(c("BC","C","CD","D"))

Description

This dataset contains Site Class data from ASCE 7-22

Usage

data(SiteClass)

Format

A data frame with 9 rows and 4 variables:

SC

Character: Site Class

Description

Character: Category description

Vs30 (m/s)

Character: Vs30 in m/s

Vs30 (ft/s)

Character: Vs30 in ft/s

Description

This dataset contains simulations of different site conditions

Usage

data(SiteTable)

Format

A data frame with 255614 rows and 38 variables:

Hs

Double: Height in m.

Hw

Double: Water Table in m.

NL

Integer: Number of Layers

Z500

Double: Depth to 500 m/s in m.

Z1000

Double: Depth to 1000 m/s in m.

SID

String: Site ID

Go

Double: Shear Modulus in kPa.

mo

Double: Shear Modulus Exponent.

Ts

Double: Fundamental Period in s.

VSo

Double: Shear Wave Velocity in m/s.

VS30

Double: Shear Wave Velocity at 30 m in m/s.

UID

String: USCS ID

Gravels

Double: Percentage of Gravels.

Sands

Double: Percentage of Sands.

Fines

Double: Percentage of Fines.

Clays

Double: Percentage of Clays.

Silts

Double: Percentage of Silts.

Organic

Double: Percentage of Organic.

Water

Double: Percentage of Water.

GP

Double: GP Fraction.

GM

Double: GM Fraction.

GC

Double: GC Fraction.

GW

Double: GW Fraction.

SP

Double: SP Fraction.

SM

Double: SM Fraction.

SC

Double: SC Fraction.

SW

Double: SW Fraction.

CL

Double: CL Fraction.

ML

Double: ML Fraction.

OL

Double: OL Fraction.

PT

Double: PT Fraction.

OH

Double: OH Fraction.

MH

Double: MH Fraction.

CH

Double: CH Fraction.

POP

Double: Overpressure in Kpa.

POP_Units

String: Overpressure Units.

Go_Units

String: Shear Modulus Units.

Vs_Units

String: Shear Wave Velocity Units.

Description

Given one or more Vs30 values (in m/s), returns a character vector of site class designations ("A", "B", "BC", "C", "CD", "D", "DE", "E"). The classification thresholds are:

• A: Vs30 >= 1500

• B: 900 <= Vs30 < 1500

• BC: 640 <= Vs30 < 900

• C: 440 <= Vs30 < 640

• CD: 300 <= Vs30 < 440

• D: 210 <= Vs30 < 300

• DE: 150 <= Vs30 < 210

• E: 0 <= Vs30 < 150

Usage

Vs30toSID(Vs30)

Arguments

Value

A character vector of the same length as Vs30, with site class designations.

Examples

Vs30toSID(1500)  # returns "A"
Vs30toSID(c(120, 790, 3000, 455))