SPT Subaccessor#
In this guide, the basics of the spt subaccessor methods
are presented. The spt subaccessor is a collection of
methods related to standard penetration test (SPT) calculations for each sample of each point in the
DataFrame. For information about the columns used by this
subaccessor, see SPT Columns.
First, we import the necessary libraries,
In [1]: import pandas as pd
In [2]: import geotech_pandas
Next, we create a DataFrame with the following data,
In [3]: df = pd.DataFrame(
...: {
...: "point_id": ["BH-1", "BH-1", "BH-1", "BH-1", "BH-1", "BH-1", "BH-1", "BH-1"],
...: "bottom": [1.0, 1.5, 3.0, 4.5, 6.0, 7.5, 9.0, 10.0],
...: "sample_type": ["spt", "spt", "spt", "spt", "spt", "spt", "spt", "spt"],
...: "sample_number": [1, 2, 3, 4, 5, 6, 7, 8],
...: "blows_1": [0, 10, 14, 18, 25, 33, 40, 50],
...: "blows_2": [0, 12, 13, 20, 29, 35, 45, None],
...: "blows_3": [0, 15, 13, 23, 27, 37, 50, None],
...: "pen_1": [150, 150, 150, 150, 150, 150, 150, 10],
...: "pen_2": [150, 150, 150, 150, 150, 150, 150, None],
...: "pen_3": [150, 150, 150, 150, 150, 150, 50, None],
...: }
...: )
...:
In [4]: df = df.convert_dtypes()
In [5]: df
Out[5]:
point_id bottom sample_type sample_number ... blows_3 pen_1 pen_2 pen_3
0 BH-1 1.0 spt 1 ... 0 150 150 150
1 BH-1 1.5 spt 2 ... 15 150 150 150
2 BH-1 3.0 spt 3 ... 13 150 150 150
3 BH-1 4.5 spt 4 ... 23 150 150 150
4 BH-1 6.0 spt 5 ... 27 150 150 150
5 BH-1 7.5 spt 6 ... 37 150 150 150
6 BH-1 9.0 spt 7 ... 50 150 150 50
7 BH-1 10.0 spt 8 ... <NA> 10 <NA> <NA>
[8 rows x 10 columns]
Note
Notice that the df is reassigned with the result of
convert_dtypes() method. This method converts the columns of
df to the best possible dtypes that support NA to consistently
represent missing data.
Use the dtypes property to show the current dtypes of df,
In [6]: df.dtypes
Out[6]:
point_id string[python]
bottom Float64
sample_type string[python]
sample_number Int64
blows_1 Int64
blows_2 Int64
blows_3 Int64
pen_1 Int64
pen_2 Int64
pen_3 Int64
dtype: object
Getting the seating penetration#
The get_seating_pen() method returns a
Series of penetration measurements in the first increment, where the
measurements are exactly 150 mm. Measurements that do not meet the requirements are
masked with NA.
In [7]: df.geotech.in_situ.spt.get_seating_pen()
Out[7]:
0 150
1 150
2 150
3 150
4 150
5 150
6 150
7 <NA>
Name: seating_pen, dtype: Int64
Getting the main penetration#
The get_main_pen() method returns a
Series with the sum of the penetration in the second and third 150 mm
increment for each sample/layer.
In [8]: df.geotech.in_situ.spt.get_main_pen()
Out[8]:
0 300
1 300
2 300
3 300
4 300
5 300
6 200
7 <NA>
Name: main_pen, dtype: Int64
Getting the total penetration#
One of the methods under spt is the ability to get the
total penetration of each SPT increment. The
get_total_pen() method returns a
Series with the sum of the penetration per inteval in each sample/layer.
In [9]: df.geotech.in_situ.spt.get_total_pen()
Out[9]:
0 450
1 450
2 450
3 450
4 450
5 450
6 350
7 10
Name: total_pen, dtype: Int64
Getting the seating drive#
It is also possible to get the seating drive, which is, by definition, the number of blows required
to penetrate the first 150 mm increment. The
get_seating_drive() method returns such a result for
each sample/layer.
In [10]: df.geotech.in_situ.spt.get_seating_drive()
Out[10]:
0 0
1 10
2 14
3 18
4 25
5 33
6 40
7 <NA>
Name: seating_drive, dtype: Int64
Note
Notice that the last value is NA, this is because the first increment
didn’t reach the full 150 mm requirement. Such cases are usually considered as invalid tests or
hint to the start of a hard layer of soil or rock.
Getting the main drive#
The main drive, which is the total number of blows in the second and third 150 mm increment, can
also be returned by the get_main_drive() method for each
sample/layer.
In [11]: df.geotech.in_situ.spt.get_main_drive()
Out[11]:
0 0
1 27
2 26
3 43
4 56
5 72
6 95
7 <NA>
Name: main_drive, dtype: Int64
Note
This method simply sums up the second and third increment regardless if the increments are completely penetrated or not. Due to this, the main drive may not always correspond to the reported N-value.
Getting the total drive#
It is also possible to calculate the total number of blows in all three 150 mm increments for each
sample/layer through the get_total_drive() method.
In [12]: df.geotech.in_situ.spt.get_total_drive()
Out[12]:
0 0
1 37
2 40
3 61
4 81
5 105
6 135
7 50
Name: total_drive, dtype: Int64
Checking for refusal samples#
It is possible to check for which samples refused penetration through
is_refusal(). This method will return True for any
sample that may be considered a refusal.
A sample is considered a refusal when any of the following is true:
a total of 50 blows or more have been applied during any of the three 150 mm increments;
a total of 100 blows or more have been applied; and
partial penetration, which signifies that the sampler can no longer penetrate through the strata, is present in any of the increments.
In [13]: df.geotech.in_situ.spt.is_refusal()
Out[13]:
0 False
1 False
2 False
3 False
4 False
5 True
6 True
7 True
Name: is_refusal, dtype: boolean
Checking for hammer weight samples#
It is also possible to check which samples are hammer weights through
is_hammer_weight(). This method will return True for
any sample that may be considered hammer weight.
A sample is considered hammer weight when all of the following are true:
a total of 450 mm or more was penetrated by the sampler through sinking; and
each 150 mm increment has 0 blows recorded.
This can be defined in a DataFrame similar to how the first sample is
recorded. The blow counts and penetration measurements for all three increments are 0 and
150, respectively.
In [14]: df.geotech.in_situ.spt.is_hammer_weight()
Out[14]:
0 True
1 False
2 False
3 False
4 False
5 False
6 False
7 False
Name: is_hammer_weight, dtype: boolean
Getting the N-value#
The SPT is mainly done to calculate the N-value. This can easily be calculated using the
get_n_value() method for each sample/layer.
In [15]: df.geotech.in_situ.spt.get_n_value()
Out[15]:
0 0
1 27
2 26
3 43
4 56
5 50
6 50
7 50
Name: n_value, dtype: Int64
As you can see, the N-values for the last three samples are set to 50, but why? This is because these samples are refusals and are assumed to have an N-value of 50; however, this behavior can be customized.
Setting the assumed refusal N-value#
The assumed refusal N-value can easily be changed by setting the refusal parameter like so,
In [16]: df.geotech.in_situ.spt.get_n_value(refusal=100)
Out[16]:
0 0
1 27
2 26
3 43
4 56
5 100
6 100
7 100
Name: n_value, dtype: Int64
You can also set it to NA if you don’t want to assume a refusal N-value,
In [17]: df.geotech.in_situ.spt.get_n_value(refusal=pd.NA)
Out[17]:
0 0
1 27
2 26
3 43
4 56
5 <NA>
6 <NA>
7 <NA>
Name: n_value, dtype: Int64
Limiting the N-values#
The limit parameter is also available if you wish to limit the non-refusal N-values to the
refusal N-value. To limit the N-values, just set the limit parameter to True,
In [18]: df.geotech.in_situ.spt.get_n_value(limit=True)
Out[18]:
0 0
1 27
2 26
3 43
4 50
5 50
6 50
7 50
Name: n_value, dtype: Int64
As you can see, the N-value at index 4 was limited from 56 to 50.
Warning
Setting limit to True while also setting refusal to NA will
have a similar output to Out[16] above. That is to say, the refusal N-value will change as
expected, however, since it is essentially nothing, nothing will get limited as well.
In [19]: df.geotech.in_situ.spt.get_n_value(refusal=pd.NA, limit=True)
Out[19]:
0 0
1 27
2 26
3 43
4 56
5 <NA>
6 <NA>
7 <NA>
Name: n_value, dtype: Int64
Don’t worry if you forget about this warning, since geotech-pandas will warn you when it detects you using such settings.
Getting a simple SPT report#
A simple descriptive report of the blow counts and the N-value can be obtained through the
get_report() method,
In [20]: df.geotech.in_situ.spt.get_report()
Out[20]:
0 0,0,0 N=0(HW)
1 10,12,15 N=27
2 14,13,13 N=26
3 18,20,23 N=43
4 25,29,27 N=56
5 33,35,37 N=72(R)
6 40,45,50/50mm N=95/200mm(R)
7 50/10mm,-,- N=(R)
Name: spt_report, dtype: string