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

E39 Kristiansand-Trondheim Ca 1100 km

Ferryfree E39

8 ferry crossings to be replaced with bridges, floating bridges and submerged/floating tunnels

New high speed rail line

CC = compression index CS = swelling index Ca = creep index

Each day: constant

1D Compression of “ideal” clay

Real clay

0.8

1.6

2.4

3.2

4

0.1 1 10 100 1000 10000'v (kPa)

e

IntactRemoulded

(d)

Vanttila clay

'pi = 0.37 kPa 'p = 29 kPa

Cci CcReconst.

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

Field vane

CPTU

Swedish weight sounding

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

200 250 300 350 400 450

Settlement predictions for 720 daysstudents professional convetional calculations professional numerical calculations

Settlement (mm) field

measurements

-30% +30%

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