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Proctor Compaction Test Analysis

Source: R/compaction_test.R
compaction_test.Rd

Analyses results from a standard or modified Proctor compaction test. Fits a LOESS curve to find optimum moisture content and maximum dry unit weight, builds zero-air-void (ZAV) lines for selected saturation levels, and returns summary statistics plus two diagnostic plots.

Usage

compaction_test(
  w,
  gamma,
  Gs = 2.68,
  S_lines = c(70, 80, 90, 100),
  target_compaction = 0.95,
  lang = c("en", "es")
)

Arguments

w

Numeric vector. Water content values (percent) measured in the test.

gamma

Numeric vector. Moist unit weight values (kN/m³) corresponding to each w value. Must be the same length as w.

Gs

Numeric scalar. Specific gravity of soil solids. Default is 2.68.

S_lines

Numeric vector. Saturation levels (percent) for which ZAV lines are drawn on the compaction curve plot. Default is c(70, 80, 90, 100).

target_compaction

Numeric scalar. Relative compaction target as a proportion (e.g. 0.95 for 95 percent). Used to draw the reference horizontal line on the compaction curve. Default is 0.95.

lang

Character scalar. Language for plot labels. Either "en" (default) for English or "es" for Spanish.

Value

A list with:

proctor

Tibble with the input data plus computed gamma_d, void ratio e, and degree of saturation S for each test point.

optimum

One-row tibble with the optimum water content (w) and maximum dry unit weight (gamma_d) obtained from the LOESS fit.

opt_properties

Tibble with LOESS-predicted values of gamma, gamma_d, e, and S at the optimum water content.

compaction_plot

A ggplot2 object showing the compaction curve, ZAV lines, optimum point, and the target_compaction reference line.

properties_plot

A faceted ggplot2 object showing how gamma, gamma_d, e, and S vary with water content.

See also

Other soil mechanics: Casagrande_LL(), DS_plot(), Mohr_Circle(), bearing_capacity(), fallcone_limits(), induced_stress(), insitu_stress(), particle_size_graph()

Examples

w     <- c(5, 7, 9.5, 11.8, 14.1, 17)
gamma <- c(18.46, 19.64, 20.85, 21.03, 20.84, 20.54)

res <- compaction_test(w, gamma, Gs = 2.68)
res$optimum
#> # A tibble: 1 × 2
#>       w gamma_d
#>   <dbl>   <dbl>
#> 1   9.8    19.1
res$compaction_plot
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

res$properties_plot
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'


# Spanish labels
res_es <- compaction_test(w, gamma, Gs = 2.68, lang = "es")
res_es$compaction_plot
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'