Posted in STADYN

STADYN Wave Equation Program 3: Match Quality vs. Least Squares Analysis

Having broached the subject of Poisson’s Ratio and how it is computed for forward methods, we can turn to how it affects inverse methods.  However, at the same we need to consider an issue that is vital to understanding either this method or methods such as CAPWAP: how the actual pile head signal is matched with the signal the model proposes.  There is more than one method of doing this, and the method currently used by CAPWAP is different than what is widely used in many engineering applications.  Is this difference justified?  First, we need to consider just what we are talking about here, and to do that we need a brief explanation of vector norms.

Vector Norms

A vector is simply a column (or row) of numbers.  We want to compare vectors in a convenient way.  To do this we must aggregate the entries in the vector into a scalar number, and we use what we call norms to accomplish this.  In theory there are an infinite number of ways to do this: according to this reference, there are three types of norms in most common use, they are as follows:

Left: the 1-norm.  Center: the 2-norm, or Euclidean norm.  Right: the infinity-norm.

So how do use norms in signal matching? We reduce the force-time (or in our case the velocity-time) history at the pile top after impact into a series of data points, and then for each point of time of each data point we compute the results our proposed model gives us and subtract it from the actual result.  In the equation above each data point is a value x_j .  When we have all the differences in hand, we take them and compute a vector of differences, and then in turn take the norm of those differences.  We do this successively by changing parameters until we get a norm value which is the minimum we can reach.  For the STADYN program, we use the \xi - \eta values as parameters and iterate using a polytope method (standard or annealed, for our test case the latter.)

For our purposes the infinity norm can be eliminated up front: in addition to having uniqueness issues (see Santamarina and Fratta (1998), we have enough of those already) it only concerns itself with the single largest difference between the two data sets.  Given the complexities of the signal, this is probably not a good norm to use.

That leaves us with the 1-norm and 2-norm.  To keep things from getting too abstract we should identify these differently, as follows:

  1. 1-norm = “Match Quality” for CAPWAP (see Rausche et. al. (2010))
  2. 2-norm = Least Squares or Euclidean norm (think about the hypotenuse of a triangle.)  This relates to many methods in statistics and linear algebra, and has a long history in signal matching (Manley (1944).)  This is what was used in the original study.

One thing that should be noted is that the norm we actually use is modified from the above formulae by division of the number of data points.  This is to prevent mishap in the event the time step (and thus the number of data points) changes.  However, for the Mondello and Killingsworth (2014) pile, the wall thickness of the steel section drove the time step, which did not change with soil changes; thus, this division is immaterial as long as it is done every time, which it was.

Application to Test Case

As noted earlier, we will use the four-layer case using the annealed polytope method of matching.  Let us start at the end, so to speak, by showing the static load test data that the program runs with the final configuration:

Davisson Load, kN Original \nu \phi -based \nu % Change
1-norm 278 300 7.91%
2-norm 187.1 218 16.52%
% Change 48.58% 37.61%

The runs were done for both the original Poisson’s Ratio (\nu ) and that computed using the internal friction angle \phi .  The CAPWAP run done on this project recorded a Davisson simulated static load capacity of 146.3 kN.

Changing the way \nu is computed produces larger variations in SRD (soil resistance to driving) for each of the norms than was evident in the last post.  This is because the values of \nu now vary with \phi , which overall tends to increase the value of \nu for the same value of \phi .  We will discuss this in detail below.

The most dramatic change took place with the norm was changed; the value for SRD is a third to a half higher with the Least Squares solution, depending upon the way \nu is computed.

xi results
Layer Original nu, 1-norm Original nu, 2-norm Phi-based nu, 1-norm Phi-based nu, 2-norm
Shaft Layer 1 -0.708 -0.812 -0.686 0.471
Shaft Layer 2 -0.709 -0.751 -0.845 -0.96
Shaft Layer 3 -0.71 -0.984 0.966 -0.439
Shaft Layer 4 -0.586 0.428 -0.75 0.196
Pile Toe -0.69 0.366 -0.491 0.804
Average -0.681 -0.351 -0.361 0.014

The values of \xi (degree of cohesion) tend to decrease for the Match Quality but the opposite for the Least Squares method.  it is interesting to note that the Least Squares \phi based \nu is the only run to venture into predominately cohesive territory (\xi > 0 , which is interesting in a soil which is generally characterized as cohesive.

eta results
Layer Original nu, 1-norm Original nu, 2-norm Phi-based nu, 1-norm Phi-based nu, 2-norm
Shaft Layer 1 -1.71 -0.622 -8.68 -1.08
Shaft Layer 2 -1.62 -1.38 3.29 -0.117
Shaft Layer 3 -0.838 -4.373 -1.86 -5.85
Shaft Layer 4 -1.74 -28.363 -14 -27.5
Pile Toe -1.29 1.814 8.19 1.52
Average -1.440 -6.585 -2.612 -6.605

The values of \eta (consistency or density) are all low, but more so for the Least Squares cases than the Match Quality cases.  Low values of \eta are to be expected in a soil like this, but these tend to be extreme.  Although limiters such as for elastic modulus are included to prevent serious misadventure in the soil properties, the existence of extreme values of \eta is something that needs to be re-examined.  (-1 < \xi < 1 by physical necessity.)

Poisson’s Ratio Result
Layer Original nu, 1-norm Original nu, 2-norm Phi-based nu, 1-norm Phi-based nu, 2-norm
Shaft Layer 1 0.279 0.269 0.45 0.45
Shaft Layer 2 0.279 0.275 0.158 0.312
Shaft Layer 3 0.279 0.252 0.45 0.45
Shaft Layer 4 0.291 0.393 0.45 0.45
Pile Toe 0.281 0.387 0 0.45
Average 0.282 0.315 0.302 0.422

As was the \eta values, the values of \nu tend to increase with the \phi based values.  The Match Quality \phi -based values are highly irregular, which in turn reflect the wide swings in \eta with less cohesive values of \xi .

Now let us present the optimization tracks for each of these cases.

The original study discusses the numbering system for the xi and eta parameters.  In short, tracks 1-6 are for the shaft and 7-8 are for the toe.  From these we can say the following:

  1. The Match Quality runs tend to converge to a solution more quickly. The x-axis is the number of steps to a solution.
  2. The Match Quality run tended to eta values that were more “spread out” while the Least Squares solution tended to have one or two outliers in the group.
  3. The runs go on too long.  This is because, in the interest of getting a working solution, the priority of stopping the run at a convergence was not high.  This needs to be addressed.

Now the norms themselves should be examined as follows:

Final Norm Original Nu Phi-Based Nu % Change
1-norm 0.148395682775873 0.134369614266467 -9.45%
2-norm 0.001494522212204 0.001456397402301 -2.55%

In both cases the difference norms decreased with the \phi -based nu , the Match Quality difference was more pronounced.  The difference norm for the Match Quality is higher than the Least Squares solution, which is to be expected.

We finally look at the tracks compared with each other for the four cases.

It’s tempting to say that the Match Quality results “track more closely” but the whole idea of using a norm such as this is to reduce the subjective part of the analysis.  However, this brings us to look at why one norm or the other is used.

The Least Squares analysis is widely used in analyses such as this.  It is the basis for almost all regression analysis.  However, the Match Quality has some advantages.  It is considered more “robust” in that it is less sensitive to outliers in the data.  In this case, the most significant outlier is the region around L/c = 1.5, which was discussed in the original study.  Situations such as this reveal two sources of uncertainty in the model: the integrity of the mounting of the instrumentation, and the accuracy of the pile data (lengths, sizes, acoustic speed of the wood, etc.) The Match Quality certainly can help to overcome deficiencies caused by this and other factors.  Whether this is at the expense of accuracy has yet to be determined.

So we are left with two questions:

  1. If we were to improve the quality of the data by addressing the present and other issues, would we be better off if we used Least Squares?  The answer is probably yes.  Getting this in the field on a consistent basis is another matter altogether.
  2. Will the two methods yield different results?  With STADYN this is certainly the case; the use of the Match Quality with STADYN however yields results that are double those of CAPWAP.  With CAPWAP we have no way of comparing the two; the Match Quality is all we have.


Based on all of this we conclude the following:

  1. The use of a \phi based \nu leads to an improvement in the signal matching, due probably to the reduction in the number of real parameters being considered.  It will probably remain as the default option.
  2. Any final conclusions on this topic depend upon limiting the values of \eta “within the box” to prevent serious outliers.  This will be the topic of future study.
  3. We also need to address the issue of stopping the runs at a more appropriate point.
  4. The results for \xi bring up again the question of the soil properties at the soil-pile interface vs. those in the soil body.  We will discuss this in a later post.


Other than those in the original study, the following work was cited:

  • Santamarina, J.C., and Fratta, D. (1998) Introduction to Discrete Signals and Inverse Problems in Civil Engineering.  ASCE Press, Reston, VA.
Posted in Uncategorized

Celebrating Twenty Years of

It’s official: twenty years ago today, this website had its beginning.  That’s a long time on the internet, and there have been many changes.

Ten years ago I commemorated that anniversary starting with this:

Ten years ago today, I went online, logged onto my new GeoCities site, and uploaded the first page and images of “The Wave Equation Page for Piling,” my first website.  That website—which is still a part of the companion site—was the beginning of a long odyssey which led to the site as it is today.

You can read about the site’s first decade in that post.  The purpose of the site is unchanged, so it’s time to bring you up to date on our progress.

The first big change took place a few months after that post when was split off from  The basic idea was to give the Vulcan Iron Works material its own site.  Later the driven pile material was moved there also, to feature it separately.  Perhaps that site’s history can be featured later.

The second was the growth of our printed materials at  This site has always been about free stuff and continues to offer everything that way.  But many want printed books for one reason or another, and so many of the publications offered on this and the companion sites are now available at  The most popular of these have been NAVFAC DM 7.01 and 7.02; putting these back into print and make them available to the geotechnical engineering community has been well received and popular.  For a while we also offered CD-ROM compilations of our documents, but these fell out of favour with increasing bandwith; by the time our publisher discontinued offering optical media, they had stopped selling.  Even with this, the revenue from these sales continues to underwrite the hosting and domain expenses of this site.

That brings us into the early years of this decade.  Although updates and additions to the material available on this site have been ongoing, in 2011 I began the pursuit of my PhD and, to be frank, the site’s progress stalled a bit during those years.  But my MS pursuit was part of the genesis of this site, and the spinoff from the latest effort can be seen, from the page on finite element analysis in geotechnical engineering to the ongoing series on the STADYN wave equation program.  But not all slowed down: I continued to teach at the University of Tennessee at Chattanooga, which meant that the course materials section of the site continued to grow with each semester.

And that leads us to the most recent major change in the site: in January of this year the site was moved to the WordPress platform. The reasons for this are discussed here (along with the change in the marine documents) in the inaugural post.  The result has been a site with interactivity, both on the site and with social media (the Facebook group is still active.)  It is also secure (as has been the case with Positive Infinity for a long time) and adaptive to mobile devices, both of which enhance the site’s search engine draw.  And finally there is evidence that the documents on the site download more quickly, which is the whole point of the site.

It’s easy to say that this site has pretty much accomplished what it set out to do: to provide geotechnical information in an affordable format to those which many not have the resources to purchase them, both in our universities (which keep getting more expensive) and in countries around the world.  It is true that now there are many sites that offer information such as this, including obviously the U.S. government sites where most of this information came from to start with (although its presence there comes and goes.)  But we still claim to offer it with the fewest strings attached, and that’s saying something.

So once again we thank you for your visiting this site and your support, and may God richly bless you.

Posted in STADYN

STADYN Wave Equation Program 2: Effective Stress, At-Rest Lateral Earth Pressures and Poisson’s Ratio

With the output improved, we can turn to the first topic of interest. Before we do that, we need to discuss our test cases.

Test Cases

The original study featured several test cases. For this and subsequent installments, we will concentrate on three of those:

  1. FINNO2, which features the actual static load test output from Finno (1989).
  2. SEASIA, which is a GRLWEAP comparison from a notional offshore pile case in Southeast Asia.
  3. MANDK3, which features the inverse solution of an instrumented pile in the New Orleans area. The original GRL report for this is Mondello and Killingsworth (2014). Several soil profiles were analyzed using both standard and annealed polytope methods of analysis. For this study the four-layer annealed polytope case will be featured, as its results a) seemed to be the most reasonable and b) matched the standard results very closely.

Details of the original results are shown in the original study.

Effective Stresses, Vertical and Horizontal

The concept of effective stress is a foundational one in geotechnical engineering, and is discussed in textbooks such as Fellenius (2015) and Verruijt and van Bars (2007). As is the case with “classical” methods of analysis, it is necessary to compute these for successful geotechnical finite element analysis. However, there are two important considerations that come up with finite element analysis that can usually be ignored with simpler methods.

The first is that it is necessary to apply gravity forces at the start of the run to the elements to simulate the impact of effective stresses on the soil finite elements. This is one of those important steps in analysis that most manuals and discussions of commercial codes mention in passing but do not detail how they are done. It is one of those phenomena that has “gone dark” in the literature. The original study presented an outline on the procedure for computing the effective stresses and applying them to the elements.

The second is that the computation of effective stresses concentrate on the vertical stresses and generally ignore the horizontal ones until retaining walls come into play. Nevertheless, for any three-dimensional continuum such as the semi-infinite soil mass we assume in geotechnical analysis, horizontal stresses are guaranteed to exist, if nothing else via the theory of elasticity. If we use the theory of elasticity, which is valid in an elastic-purely plastic model such as is used in STADYN until the yield point is exceeded, the relationship between the horizontal and vertical stresses is given by the equation (Verruijt and van Bars (2007))

\frac{\sigma_{x}}{\sigma_{z}}=\frac{\nu}{1-\nu}\ (1)

where \nu is Poisson’s Ratio, \sigma_z is the vertical stress, and \sigma_x is the horizontal stress.  We normally define the left hand side thus:

K = \frac{\sigma_{x}}{\sigma_{z}} (2)

For our case, the lateral earth pressure coefficient K is the at-rest lateral earth pressure coefficient, generally expressed as K_o . This is reasonable for this case because, since we have a semi-infinite soil mass, the soil literally has nowhere to go, thus all of the horizontal strains are zero. This is a key assumption for Equation 1. We can thus combine Equations 1 and 2 to yield

K_o = \frac{\nu}{1-\nu}\ (3)

In theory, we could compute the lateral earth pressure coefficient using Equation 3 and “reasonable” values of Poisson’s Ratio.

Turning back to STADYN itself, soil properties in most cases (and especially for inverse problems) are defined using the “ξ – η” system, which in turn uses typical values of various soil properties to reduce computing same for a given typical soil state to two dimensionless variables. Using this system, Poisson’s Ratio is a function of ξ and η, and is thus varied as these dimensionless parameters are varied. The variation of ν with ξ and η is shown in the original study.  As a practical matter, even if Poisson’s Ratio is measured for each project and soil profile (an unlikely situation at best,) the problematic nature of soil elasticity makes accuracy of the parameter equally problematic. Another approach is to begin by considering the following empirical relationship

K_o = 1 - sin(\phi) (4)

This is Jaky’s Equation. It has been shown to be reasonable for normally consolidated soils, although there are other relations in use for both normally and overconsolidated soils. Values of the at-rest lateral earth pressure coefficients are limited to 0\leq K_{o}\leq1 , the upper limit achieved for a purely cohesive soil where \phi = 0 . As the original study noted, Equation 4 is a common expression to compute horizontal stresses from vertical effective stresses in finite element codes, and is used to compute the horizontal effective stresses in STADYN.

Unfortunately this leaves an inconsistency between the way horizontal stresse sare computed between the effective stress computation and subsequent computations. To remedy this problem, we can combine Equations 3 and 4 and solve for Poisson’s Ratio to yield

$latex \nu=\frac{sin\phi-1}{sin\phi-2}\ $ (5)

Poisson’s Ratio is varied here as 0\leq\nu\leq0.5, where once again the upper limit is for purely cohesive soils. This indicates that these soils act as a fluid, which is nearly true for very soft clays. The main problem with this result is that, when ν = 0.5, the consitutive matrix experiences singularities. The simplest way to deal with this problem is to limit Poisson’s Ratio to a value below this one. In STADYN this value is 0.45.

Comparison With Previously Generated Values and Forward Test Cases

Having defined a new way of generating values of Poisson’s Ratio, we can compare these values both with the original values and with the two forward test cases. We will leave the inverse test cases for a later post.First, the original “ξ – η” relationship to generate Poisson’s Ratio values is shown in Figure 1.

Poissons Ratio

Figure 1: Poisson’s Ratio “ξ – η” Relationship, Original Configuration

We can see that Poisson’s Ratio is independent of η in this configuration,and
0.25\leq\nu\leq0.45 for -1\leq\xi\leq1. Computing Poisson’s Ratio based on Equation 5 yields the result shown in Figure 2.

Poissons Ratio Revised

Figure 2 Poisson’s Ratio “ξ – η” Relationship, Jaky’s Equation

There are several differences to note, as follows:

  1. The maximum value is ν = 0.5 for the revised relationship. To prevent singularities in the constitutive matrix, in actual application Poisson’s Ratio is limited as described earlier, a similar concept to the “corner cutting” for Mohr-Coulomb failure.
  2. For purely cohesive soils, ν is invariant in both cases. As ξ is reduced and internal friction is increased, ν varies with η. In other words, Poisson’s Ratio tends to decrease in cohesionless soils as the relative density of the soil increases.
  3. The range of possible values for Poisson’s Ratio in both cases is very much the same; it is simply distributed differently in the continuum.

As far as the forward test cases (the first two) are concerned, SEASIA is the same in both cases because the soil is assumed to be purely cohesive, thus Poisson’s Ratio is the same in both cases.

For FINNO2, the simplest way to compare the two is to compare the hammer blow counts and the Davisson static load test result. That comparison is as follows:

  • Original Poisson’s Ratio Computation: Blow count 17.7 blows/300 mm, Davisson failure load 976 kN.
  • Revised Poisson’s Ratio Computation: Blow count 17.8 blows/300 mm, Davisson failure load 980 kN.

The differences for this case are not that substantial. The differences which emerge in the inverse case will be discussed in a subsequent post.

One other change that was made in the program was the stopping point for the static load test. The program is capable of interpreting the static load test for several criteria; however, how long the static load test is conducted (in the computer or in the field) depends upon the criteria being used to interpret it. The program now stops the test depending upon when the selected criterion is reached; Davisson’s criterion is the default. It is also interesting to note that, since the Jacobian is fixed, Davisson’s criterion, which generally stops before the others, is probably more suitable for STADYN’s current algorithm.

References are given in the original study.

Posted in Uncategorized

Tribute to Harry M. Coyle

It is with sadness that we report the death of Dr. Harry M. Coyle, professor of civil engineering at Texas A&M University from 1964 to 1987, back in January.  You can read the entire obituary here.

For those of us involved in deep foundations, his name is a familiar one, and his monographs have graced this site and its companion,, for many years.  Among other things he is known for the Coyle and Castello method for estimating pile capacity in sand, the Coyle and Gibson method for determining damping for pile dynamics analysis, and the co-developer of the PX4C3 routine for axial load-settlement estimation, which we feature on this site, and which is the ancestor of many of those in use today.  He was deeply involved in the development of the TTI wave equation program, and some of his work relating to that is here.

Our continued condolences and prayers go to his family, and, as the obituary states, “Having loved his friends and family well, Harry Coyle will be missed by all until we are reunited with him in Glory. “

Posted in Uncategorized

My Perspective on Driven Pile Drivability Studies

This post originally appeared in 2013 on my companion site.

Recently I had a round of correspondence with a county official in Washington state re pile drivability studies and their place in the contract process.  (If you’re looking for some explanation of this, you can find it here).  His question was as follows:

During the bidding process, is the contractor’s sole basis for anticipating the size of the hammer needed the WEAP analysis? Does a contractor rely solely on design pile capacities or does the contractor combine geotechnical boring logs and cross-sections with his expertise? Who will be ultimately responsible that a large enough hammer is considered in the bid and brought to the site, the contractor or the preparer of the design package?

My response was as follows:

First, at this time the WEAP analysis is the best way for contractor and owner alike to determine the size of a hammer (both to make sure it isn’t too small with premature refusal, or too large and excessive pile stresses) necessary to install a certain pile into a certain soil.

It is a common specification requirement for a contractor to furnish a wave equation analysis showing that a given hammer can drive a pile into a given soil profile.  As far as what soil profile is used, that’s a sticky issue in drivability studies.  Personally I always attempt to estimate the ultimate axial pile capacity in preparation of a wave equation analysis.  There are two important issues here.

The first is whether the piles are to be driven to a “tip elevation” specification vs. a blow count specification.  For the former, an independent pile capacity determination is an absolute must.  For the latter, one might be able to use the pile capacities if and only if he or she can successfully “back them out” from the allowable capacities, because the design factors/factors of safety will vary from one job and owner to the next.  Some job specs make that easy, most don’t.

Even if this can be accomplished, there is the second problem: the ultimate capacity of interest to the designer and the one of interest to the pile driver are two different things.  Consider this: the designer wants to know the pile with the lowest capacity/greatest settlement for a given load.  The pile driver wants to know the pile with the highest capacity.  If you use the design values, you may find yourself unable to drive many of the piles on a job or only with great difficulty.  I’m seeing a disturbing trend towards using the ultimate capacity for design and running into drivability problems.

As far as responsibility is concerned, that of course depends upon the structure of the contract documents.  I’ve discussed the contractor’s role; I would like to think that any driven pile design would include some consideration of the drivability of the piles.

Some of the FHWA publications I offer both in print and online (including the Driven Pile Manual) have sample specifications which you may find helpful.

Hope this long diatribe is of assistance.

After this, there’s another way of looking at this problem from an LRFD (load and resistance factor design) standpoint that might further illuminate the problem.  The standard LRFD equation looks like this:

\sum _{i=1}^{n}{\it \gamma}_{{i}}Q_{{i}} \leq \phi\,R_{{n}}

This is fine for design.   With drivability, however, the situation is different; what you want to do is to induce failure and move the pile relative to the soil with each blow.  So perhaps for drivability the equation should be written as follows:

\sum _{i=1}^{n}{\it \gamma}_{{i}}Q_{{i}} \geq \phi\,R_{{n}}

It’s worthy of note that, for AASHTO LRFD (Bridge Design Specifications, 5th Edition)  \phi can run from 0.9 to 1.15, which would in turn force the load applied by the pile hammer upward more than it would if typical design factors are used.  Given the complexity of the loading induced by a hammer during driving, the LRFD equation is generally not employed directly for drivability studies, and the fact that \phi hovers around unity makes the procedure in LRFD very similar to previous practice.

The problem I posed re the hardest pile to drive vs. the lowest capacity pile on the job is still valid, especially with non-transportation type of projects where many piles are driven to support a structure.  When establishing a “standard” pile for capacity, it is still the propensity of the designer to select the lowest expected pile capacity of all the pile/soil profile combinations as opposed to the highest expect pile resistance of all the pile/soil profile combinations necessary for drivability studies.

Put another way, the designer will tend to push the centre of the probability curve lower while the pile driver will tend to push the centre of the probability curve higher.  This is a design process issue not entirely addressed by LRFD, although LRFD can be used to help explain the process.