Developing soft soil engineering
competency by problem based
learning using ”Class B” and
”Class C” predictions
Minna Karstunen, Jelke Dijkstra, Amardeep Amavasai,
Yanling Li & Georgios Birmpilis
Introduction
•
•
•
•
Background
– Why soft soils
– Geotechnics education at Chalmers – now
– Purpose of the project
Introduction to the project: test embankment
Results
Conclusions & outlook
Why soft soils?
Gothenburg quick clay
Västlänken
Ferryfree E39
E39
KristiansandTrondheim
Ca 1100 km
8 ferry crossings to be
replaced with bridges,
floating bridges and
submerged/floating tunnels
New high speed rail line
1D Compression of “ideal” clay
Each day:
CC = compression index
  constant  
CS = swelling index
Ca = creep index
Real clay
4
'pi = 0.37 kPa
'p = 29 kPa
Vanttila clay
3.2
Cc
e
Cci
Intact
Reconst.
Remoulded
2.4
1.6
0.8
0.1
(d)
1
10
100
'v (kPa)
1000
10000
Geotechnics education at
Chalmers - Now
Undergraduate (3 years, 180 ECTS)
• Year 1: Engineering Geology (in Swedish)
• Year 3: Geotechnics and Foundation Engineering (in Swedish)
Postgraduate (2 years, 120 ECTS)
• 2 MSc programmes (in English):
– Infrastructure and Environmental Engineering
– Structural Engineering and Building Technology
• Year 4: Modelling and Problem Solving
• Year 4: Geotechnics (about 110 students, mixed
backgrounds)
• Year 5: Infrastructure Geoengineering
Purpose of the project
•
•
Learn to deal with real data & create your own geotechnical
model
Perform Class B and Class C predictions:
– Class A prediction: made with available data before the
structure is constructed
– Class B prediction: a blind prediction made with available
data, with no knowledge of the field measurement results
– Class C prediction: improved prediction with the aid of field
observations
Task: Settlement prediction
under test embankment
• Part 1: Calculate consolidation settlement of
the test embankment
– Using basic data available, create a conceptual model
of the
problem (embankment geometry, soil layering, ground water
table)
– Calculate in situ effective stresses
– Calculate increase in total stress due to embankment
loading (note: no traffic as SLS!)
– Define, using the soil data available the necessary model
parameters for settlement calculation
– Calculate consolidation settlement as a function of time (mm
vs. days)
All methods are allowed!
Task: Settlement prediction
• Part 2: Comparison of your Part 1 results with
real field measurements & improved
prediction with the help of these
– Comparison of Part 1 results with field monitoring results
– Application of Asaoka’s method on field monitoring results
for improving estimates of coefficient of consolidation in the
field and final settlements
– Improvement of settlements predictions by rethinking input
values in the light of Asaoka’s method
Test embankment on soft soil
CPTU
Swedish
weight
sounding
Field vane
Data: basic information
Data: basic information
Data: Results from stepwise
oedometer tests
30 oedometer tests from the depth of 0.5m to 17 m
Data: Results from CRS tests
14 CRS tests
Results of the Class B
predictions
Results of the Class B
predictions
Settlement predictions for 720 days
students
-30%
0
200
professional convetional calculations
professional numerical calculations
+30%
400
field
measurements
600
800
1000
Settlement (mm)
1200
1400
1600
1800
2000
Asaoka’s method
Class C predictions
•
•
Asaoka’s method enables the students to identify what went
wrong in their predictions:
– If the final values of settlement was wrong, the source could
be errors in determination of preconsolidation pressure
and/or 1D stiffness
– If the rate of settlement was wrong, the source was most
likely error in boundary conditions or determination of cv
Not surprisingly, students who did well in the design
project did well in the corresponding (closed book) exam
question (retention of knowledge is high).
Conclusions and Outlook
•
•
•
•
•
Creating a suitable case for the project required a lot of effort, as
the data needs to be well documented and consistent.
Problem based learning gives students experience in dealing with
real data, which is not perfect:
– Sample disturbance
– Testing problems
– Missing data
First encounter with engineering judgement
Real field observations data improves problem-based learning
– Students are confronted with feedback from reality (very
valuable lesson)
In future, the exercise will form part of the final grading