CE 251 CIVIL ENGINEERING SYSTEMS
FINAL PROJECT
DEPARTMENT OF CIVIL & ENVIRONMENTAL ENGINEERING
UNIVERSITY OF CONNECTICUT
Project Generalities: This project will facilitate assimilation of and practicing with concepts
discussed in the class CE251: Civil Engineering Systems. It will emphasize the synthesis aspect
by requiring that students use a variety of tools and knowledge gained throughout the semester in
order to solve a practical engineering problem. The emphasis of this project is in the
transportation and geotechnical engineering field.
Project Objectives: The objectives of the final project are to promote material assimilation and
test whether learning has taken place. In particular, the concepts that will be practiced and tested
are:







Calculation of basic statistics
Estimation of distribution parameters
Testing of distribution types
Linear regression
Testing for significance of slope and intercept
Monte Carlo simulation
Hypothesis testing
Project Specifics: The New Mexico State Department of Transportation (DOT), through their
Project Officer Mr. U. R. Hired, has retained your consulting team in order to provide guidance
for the design of a filter system that is to be installed in a highway embankment as shown below.
Original ground level
Groundwater level
Filter
Highway level
Design of proper filters comprises choosing the dimensions and material of the filter such that,
among other things, no significant invasion of soil is permitted into the filter. If this were
CE251: Final Project
1/4
permitted, the resulting erosion could cause serious stability difficulties for the embankment. The
eroded fine soil particles could also clog the filter further exacerbating the problem by providing
a feedback mechanism whereby faster flowing water could promote erosion, etc.
The US Army Corps of Engineers (USACE) experimental station at Vicksburg, Miss., has
developed a series of criteria that filters must meet. One of these criteria is as follows (Lambe
and Whitman, 1969):
D15f
4  s  20
D15
D15 is the diameter corresponding to the 15th percentile finer for either the soil or the filter. Your
team has conducted a field sampling campaign and collected soil embankment data for porosity n
(relatively inexpensive to measure) and hydraulic conductivity K (relatively expensive to
measure). These data are tabulated below (Wierenga et al., 1989):
0.345
HYDRAULIC
CONDUCTIVITY
(CM/SEC)
0.0098
0.352
0.0079
0.323
0.0034
0.311
0.0068
0.329
0.0053
0.301
0.0058
0.288
0.0042
0.300
0.0062
0.315
0.0052
0.381
0.0072
0.335
0.0160
0.345
0.0070
0.364
0.0085
0.343
0.0047
0.342
0.0050
0.309
0.0031
0.318
0.0026
0.333
0.0047
0.347
0.0053
0.341
0.0032
0.341
0.0081
0.384
0.0118
0.343
0.0068
0.364
0.0047
0.351
0.0054
0.342
0.0062
0.312
0.0064
0.364
0.0103
POROSITY
CE251: Final Project
2/4
0.360
0.0141
0.334
0.0054
0.322
0.0032
0.376
0.0078
0.320
0.0078
0.335
0.0074
0.327
0.0032
0.339
0.0092
0.334
0.0069
0.336
0.0042
0.341
0.0054
0.369
0.0057
0.397
0.0110
0.373
0.0152
0.357
0.0101
0.342
0.0034
0.381
0.0101
0.359
0.0057
0.332
0.0044
0.338
0.0113
0.342
0.0043
0.366
0.0055
According to Hazen (1911) the hydraulic conductivity, K (cm/sec) is related to a parameter very
closely approximated by D15 (cm) through the following formula:
2
K  100 D15
You have selected a vendor for the filter material that guarantees its size characteristics as
follows:
D15f  N (0.13,0.001)
Your team of expert engineers and statisticians is asked to do the following:
1. Analyze the soil data for porosity n and hydraulic conductivity K in order to estimate all
basic statistics.
2. Infer the underlying distributions for both soil parameters using a chi-square test (normal and
log-normal are 2 good starting points).
3. Establish a linear regression between porosity and the logarithm of hydraulic conductivity.
4. Test for significance of regression in step 3 by establishing statistical significance for the
slope, the intercept and regression coefficient.
5. Given the relatively low-cost associated with porosity measurements, use the Hazen formula
and the linear regression derived in step 3 to develop the relationship between the soil D15
and porosity n; what distribution do you expect the soil D15 to satisfy?
6. Using the linear regression equation derived in step 3 and Monte Carlo simulation produce a
probability distribution for the soil D15 for a large number of samples (i.e., 5,000 or 10,000);
what are the probabilistic characteristics of D15 for the soil? Again normal and log-normal
CE251: Final Project
3/4
distributions are 2 good starting points. Are the Monte Carlo simulation results consistent
with your expectations based on step 5?
7. Test whether the USACE filter design criterion is satisfied for the site soil and filter choice;
what is the probability of the criterion being violated?
8. Summarize your findings in a report submitted to the client.
Your report should be professionally looking (typed, graphics produced using computers, etc.).
Repetitive calculations may be included in neat hand-written form on engineering paper in an
appendix at the end of your report. The report should include:













Submittal letter (accompanying the report)
Title page
Summary
Table of contents
Objectives and methodology
Simulation data
Probabilistic characteristics for D15
Basic statistics of data given and testing of distributions
Linear regression and testing for significance
Filter criterion violation probability
Conclusions
References
Appendices
The final project is due on the last day of classes.
CE251: Final Project
4/4