1. Some 2015 Updates to the TC16 DMT Report 2001
Frontespizio
Some 2015 Updates to the
TC16 DMT Report 2001
Silvano Marchetti
University of L'Aquila, Italy 1

2. IN LAST DECADES MASSIVE MIGRATION FROM LAB TESTING TO INSITU TESTING
CPT and DMT often used today as the major part of everyday investigations
Fast, economical, reproducible, informative, many data, reduced scatter, cost much less than sampling & testing….
Instrumental accuracy of CPT-DMT is “laboratory-grade” (unlike SPT)

3. Truck mounted penetro-meter pushing the blade
DMT components
Truck mounted penetro-meter pushing the blade

4. DMT can also be executed with small inexpensive pushing machines

5. DMT can also be executed using a DRILL-RIG
Test starts from bottom of a borehole (like SPT, but say 3m)
No need 2 cm/sec. Speed can be half or twice. Penetration is just for inserting the blade. Test starts later.

6. Suitable for sand, silt, clay
Can push 25 ton
water
semi-liquid soils
hard soils
Blade can break obstacles

7. DMT BLADE All mechanical
NO ELECTRONICS , no zero drift, no temperature effects
Blade is like an electrical switch. Can be off or on.

8. HOW DMT WORKS (mechanical)
Every 20 cm
DMT 30 m : ½ day
po, p1  Id, Kd, Ed  Soil parameters (M, Cu …)
  
REDUCTION FORMULAE
 DMT Report TC16 (2001) of ISSMGE

9. DMT FORMULAE
DMT Report TC16 of ISSMGE 2001

10. Two primary “DMT Manuals”.
Contains 95% of what is needed to know on DMT
TC16 DMT Report 2001 1
A supplement / Upgrade.
Clarifications and new developments
Some 2015 Updates
(SM paper to this Conf.) 2
Both are in the proceedings book (and USB key)
Then for SDMT : operator manual & software manual (down loadable)
Standards : Eurocode 7 (2005), ASTM (2007)
ISO (Tech. Spec being converted to Standard (info Powell, Arroyo…)

11. As to formulae : Mayne at DMT'15 shows systematic validation of the 1997 Ø - Kd eqn.
He measured Ø on undisturbed sand samples acquired by freezing technologies or special piston samplers.
Marchetti (1997) Eq. Ø-Kd
TRX Ø
1997 Eqn
Mayne shows that the 1997 Kd-Ø Eq. conservatively matches all the TRX Ø data.

12. DMT results ID  M Cu   KD soil type (clay, silt, sand) common use
  KD
soil type
(clay, silt, sand)
common use
or Stress History Index
KD =is po normalized to ’vo
Definition : as Ko, but an amplified Ko
V. roughly 4 times Ko
KD = 2  NC clay
shape similar to OCR helps understand history of deposit
1-D ’vo . Treat as if obtained by oed

13. SEISMIC DILATOMER is a DMT with the addition of a seismic module (tube) Vs
SDMT
SDMT is TRUE interval (two sensors)
Both Seismograms from same blow
Vs instantly and operator (and interpreter !) independent
Much faster & economical than Down hole – X hole

14. Seismic Dilatometer

15. SHEAR WAVE SOURCE

16. Example seismograms SDMT at Fucino
Delay T : automatically calculated using Cross Correlation
Repeatability Vs : 1-2 %. Mayne : Why obtain T from 1st arrival (?) - just 1 point, when can obtain T optimized from 1000 points?

17. DMT results REPEATABILITY ≈ 1-2%
SHEAR WAVE
VELOCITY
GO= ρ Vs2
Vs (m/s)
mechanical DMT
Seismic DMT

18. Zelazny Most – Repeatability of Vs (10 blows)
Routinely energize at each depth several blows to get several Vs
Zelazny Most – Repeatability of Vs (10 blows) Z Vs
Vs Repeatability
Variation Coeff.
[m]
[m/s]
[%]
7.00
179
178,178,180,180,180,179,179,180,180,180
0.50
7.50
231
234,232,232,230,229,231,232,229,230
0.68
8.00
225
227,225,224,225,225,225,226,226,225,224,224
0.40
8.50
276
276,276,280,273,275,273,271,273,287,281
1.68
9.00
296
291,286,301,292,296,288,301,300,304,303
2.09
9.50
248
244,251,250,247,250,249,250,249,242,248
1.11
10.00
292
292,289,290,293,289,292,289,0,292,296,295,293
0.79
10.50
320
321,323,320,325,323,325,316,314,308,321
1.61
11.00
291
293,291,293,291,291,290,290,291,290,290
0.38
11.50
321
324,320,320,322,320,322,319,319,320,320
0.48
12.00
309
311,307,311,309,309,311,309,309,307,311
12.50
286
287,285,285,285,287,285,285,287,287,287
0.35
13.00
265
264,265,265,265,264,265,265,265,266,265,266,264
0.24
13.50
280
287,276,279,276,276,276,294,275,278,279
2.08
14.00
312
313,312,312,322,310,312,310,310,310,312
1.10
14.50
298
301,298,299,299,298,296,299,298,299,298
0.44
15.00
307,309,307,309,309,309,309,309,309,309
0.29
15.50
282
282,281,281,282,282,281,282,282,284,282
0.30
e.g.
SDMT repeatibility : a few m/sec

19. Comparison of Vs,sdmt vs Vs by other methods Cross-hole, Down-Hole, SCPTU, SASW...
Some 20 slides. Will show Wednesday.
No time here.
Generally very good agreement

20. Main SDMT applications. Details  TC16 or specific papers
Settlements of shallow foundations
Liquefability evaluation
Compaction control
Detecting slip surfaces in OC clay
Laterally loaded piles
Diaphragm walls : “springs” for design
FEM input parameters
In situ G- decay curves
Have in common : need of Stress History (by Kd) 20

21. But before : focus on sensitivity of Kd to Stress History.
Sensitivity of Kd to SH important : not many alternatives to sense SH in situ (sand).
Kd is a protagonist in many correlations.
Stress History fundamental for realistic prediction of settlements and liquefaction resistance. 21

22. Lee 2011, Korean Researchers. Calibration Chamber in sand
Diagrams compare sensitivity of CPT-DMT to Stress History
Lee 2011, Korean Researchers. Calibration Chamber in sand
Diagr.1. Effect of SH on Qc
Diagr. 2. Effect of SH on Kd
OCR = 1,2,4,8
With
OCR
CPT
DMT
Kd much more reactive than Qc to Stress History
Kd distinguish sands with SH / no SH. Qcn  less

23. Qcn KD The indication is : reflects essentially Dr
(only to minor extent stress history)
reflects Dr + various stress history effects such as OCR, aging, Ko, structure (cementation)
Qcn KD

24. Schmertmann 1984 explained the different sensitivity of DMT &CPT to stress history :
"the cone appears to destroy a large part of the modification of the soil structure caused by the overconsolidation and it therefore measures very little of the related increase in modulus.
In contrast the lower strain penetration of the DMT preserves more of the effect of overconsolidation.“

25. OCR in sand : both Qcn and Kd needed
Diagrams also demonstrate: OCR in sand cannot be predicted by CPT alone nor from DMT alone.
OCR in sand : both Qcn and Kd needed
OCR = 1,2,4,8
With
OCR
CPT
DMT
1° diagram confirms low sensitivity of Qcn to SH  Difficult relate Qcn-OCR. Long algebraic formulae / statistics / numerical models : can not make up for tenuous correlation.

26. BUT even Kd alone not sufficient, as Kd increases with either Dr and OCR
E.g. Kd = 3 could be due :
low Dr -high OCR high Dr - low OCR.
To evaluate OCR in sand, Qc must also be available : provides an indication of Dr in the horizontal axis
Monaco .. J. Asce 2014
DMT ?
Example of necessity
multi
parameter approach
CPT+DMT
Beware of software OCR=f(Qcn) or
OCR = f(Kd) !
Move to a correlation based on both M and Qc

27. That DMT is more REACTIVE to Stress History CONFIRMED in the FIELD, during COMPACTION (apply SH)Q c
BEFORE
AFTER
Jendeby 92
Measured in a loose sandfill Qc & MDMT before-after compaction
NC : M/Qc  5-12
OC : M/Qc  12-24
The fact that MDMT /Qc increases
with compaction, indicates that MDMT increases at a faster rate than Qc, confirming higher sensitivity of DMT to SH.

28.  MDMT particularly fit for evidencing benefits of compaction.
Schmertmann (1986) & many others, including Balachowski DMT’15 : COMPACTION produces a % increase of MDMT  twice the % increase of Qc.
This higher sensitivity to compaction (by factor 2) of MDMT vs Qc documented systematically in a large No. of papers
 MDMT particularly fit for evidencing benefits of compaction.
Schmertmann 1988 : Since aim of compaction is reduce settlements  More logic specs in terms of M instead of Dr (Dr wrong target, Dr correlations sand dependent, Dr elusive ). E.g. Balachowski DMT’15 : acceptance criterion of compacted ground MDMT 80 MPa (avoiding slippery Dr).

29. Elusivity of Dr in situ There is no unique mapping Dr=f(Qc).
Jefferies – Been ISOPT 1995 :
“Hilton Mines sand at Dr=60% produces the same Qc as Monterey sands at Dr=40%”
Very difficult estimate Dr in situ  unmeasurable

30. Jendeby’s results were the cradle of the MDMT/Qc vs OCR correlation
Based on Jendeby and similar :
Semi quantitative Guidelines in TC16 DMT Report :
NC : M/Qc  5-10
OC : M/Qc  12-24
In 2014 Monaco et al. constructed a continuous MDMT /Qc vs OCR curve using the Treporti results, where a trial embankment was built then removed, thereby obtaining an OCR profile permitting a calibration….

31. This is the Monaco (2014) curve MDMT/ qc to estimate OCR in sands
Monaco’s curve in v good agreement with the Semi quantitative Guidelines in TC16 DMT 2001 Report :
NC : M/Qc  5-10
OC : M/Qc  12-24
Thus Jendeby & Monaco corroborate each other (despite the v. different origin – textbook OCR by embankment and violent SH by compaction). Monaco approx but solid correlation.
OCR sand is difficult with DMT, CPT. Imagine with just one ! Once having OCR : Ko  Ko,nc (OCR)m

32. Found similarly good regression coefficient (0.927-0.917).
Besides the 2-parameter OCR vs MDMT/Qc Monaco also constructed a 1-parameter OCR-Kd correlation (only DMT)
Found similarly good regression coefficient ( ).
OCR = f (MDMT/Qc)
OCR = f (Kd))
OCR-Kd) has a good regression just because in Treporti Dr is more or less uniform. If Dr is variable , OCR-Kd would not work (it is local).
Lesson : When a key parameter is missing, only local (not general) correlations can possibly be obtained.

33. A third independent support to Monaco’s OCR-MDMT/qc Eq. comes from  .
(Note : The ratio M/Qc is nothing less  used in M= Qc !!)
Calibration chamber in sand :
  3 to 4 in NC, up to 20 on OC sand (which are also the  values chosen by designers who use M= Qc).
Principle why  permits to estimate OCR :
If OCR is unknown, difficult to estimate .
But if  is known, we may estimate OCR. 

34. Often today in compaction jobs (°) the ratio MDMT/ qc is plotted before-after.
Jendeby Balachowsky DMT’15
The profiles MDMT / Qc permit to evaluate OCR increase, by converting MDMT / Qc into OCR using Monaco’s Eq.
MDMT / Qc  proxy of OCR. Examples of using MDMT / Qc for OCR in Sharif
Recommended distance of adjacent CPT & DMT : 1 m, no more no less

35. Sensitivity of Kd to SH important for Settlements
Jamiolkowski (Isopt-1,‘88,1) : “without Stress History, impossible to select reliable E (or M) from Qc”
Yoshimi et al. (1975) “The NC sand specimens were six times more compressible than the prestressed sand, hence it is imperative to have information on stress history to characterize compressibility of a sand
Leonards & Frost (1988). “Correlations between penetration resistance and soil modulus will seriously overestimates settlements if the deposit has been prestressed”.
Robertson et al. (1986). “Predictions of soil modulus from qc can be rather poor, especially for OC soils, with a large potential error”.
Application #1 DMT : predict settlements (operative modulus).

36. Application #1 DMT : predict settlements (operative modulus)
Insensitive to SH
Idea at base of MDMT : factorize Ed based on SH (Kd)
MDMT= ED x Rm(Kd, Id)
The analogous for CPT : MCPT= Qc x Rm(Qc) ??? …
appears more difficult - cannot use Qc twice : Qc to be factorized and Qc to estimate the correction

37. Settlement predictions by DMT
by Boussinesq
Terzaghi 1-D
Accuracy of settlements prediction : confirmed by over three decades of good comparisons measured vs DMT-predicted settlements.

38. M at Sunshine Skyway Bridge. Tampa Bay – Florida USA
World record span for cable stayed post-tensioned concrete box girder concrete construction
(Schmertmann – Asce Civil Engng – March 1988)
Modulus M from DMT : M  200 MPa (1000 DMT test points)
M from laboratory : M  50 MPa
From obs. Settlements : M  240 Mpa
Conclusion : MDMT  Ok. MLAB : too soft (factor 4)

39. Possible reasons DMT predicts well settlement
Wedges deform soil << cones
Modulus by mini load test relates better to modulus than to penetr. resistance
Availability of Stress History parameter Kd. (DMT is a 2- parameter test. Fundamental to have both: Ed and Kd)
Stiffnes 
Strength
 Need moduli, not strength !

40. Mayne compared strain levels by DMT and other in situ tests

41. COMPUTER PROGRAM
Input load Input MDMT
 
As soon as DMT is completed possible to estimate settlements
Ave load  10 kPa / floor
If s = 3cm (or 4 or 5) : OK shallow.
Otherwise piles . V. rough but instant indication.

42. Liquefiability evaluations also in need of info on Stress History / Aging
Jamiolkowski et al. (S. Francisco 1985) "Reliable predictions of sand liquefiability...require…some new in situ device [other than CPT or SPT], more sensitive to effects of past STRESS-STRAIN HISTORIES”
Leon et al. (ASCE GGE 2006) South Carolina sands. “Ignoring AGING and evaluating CRR from in situ tests insensitive to aging (SPT, CPT, VS) underestimated CRR by a large 60 %”
Salgado et al. (Jnl Asce 1997). “OCR increases liquefaction resistance CRR, but changes negligibly Qcn”

43. CRR most commonly estimated by CPT correlations. However some ??
Robertson & Wride (1998)  CRR by CPT adequate for low-risk projects. For high-risk : estimate CRR by more than one method
Youd & Idriss (2001 NCEER Workshops )  use 2 or more tests for a more reliable evaluation of CRR
Idriss & Boulanger (2006) "The allure of relying on a single approach (e.g. CPT - only) should be avoided".

44. .. difficult situation … lab too is problematic ..
Latest Research 2014
NO LABORATORY TESTS ARE SUITABLE FOR LIQUEFACTION ESTIMATION.
Only suitable FIELD TESTS MUST be used.
2014 Panel Discussion at Geo-Congress, ASCE Panelists: Prof. Idriss, Prof. Boulanger, Prof. Robertson, Prof. Cetin, Prof. Finn, Prof. Green, Prof. Stokoe, Prof. Mayne
But already Peck (1979) : it is "manifestly impossible" to obtain a completely undisturbed sample

45. However these are both one-to-one correlations.
Liquefaction Resistance CRR : is today estimated by CPT - or by DMT - but separately
CRR from CPT CRR from DMT
Idriss & Boulanger (2006)
marchetti 2015
However these are both one-to-one correlations.

46. Better to estimate CRR based at the same time on Qc and KD : CRR = f(Qc, KD) in Fig.
2015
Get CRR in two Steps
Estimate CRR exactly as we normally do today, namely using the everyday CPT-CRR correlations.
Then, if Kd is high, increase CRR, if Kd is low, reduce CRR
Method appears also as just fruit of common sense
(Fig. here is qualitative. Quantitative will soon be published Jnl. Asce 2015)

47. Possible similarity with OCR (need CPT & DMT)
Lesson with OCR: When a key parameter is missing, only local (not general) correlations can possibly be obtained.
Below two sites same Qc , different KD : In site 2 CRR can be much higher. If design is based on Qc (the same) the two sites would be treated equally, and benefits due to SH are wasted.

48. If sand has Fine content, Cementation… : adding KD to Qcn, for CRR, may not be sufficient
E.g. cementation can be ductile (toothpaste-like) or fragile (glasslike).
Clearly too many unknowns.
Go could possibly help : high Go/ Qc or high Go/ MDMT indicate cementation.
Even modulus ED from DMT could possibly help.
Additional study necessary  combining multi-parameter

49. Go/ MDMT and Go/Qc : are possibly additional useful info
Cemented soils : Go / MDMT and Go/Qc (black) higher than sedimentary soils (Schnaid 2004, Rocha et al. 2015)
(Possible reason of high Vs : cemented contacts of the grains allow a faster transmission)
Schnaid 2004
Rocha 2015
Go/ MDMT and Go/Qc : are possibly additional useful info

50. Multiparameter  inevitable
Multiparameter  inevitable. A fashionable MIT model requires 19 lab parameters. If we use insitu, we need not 19 (!), but at least a few. Ten years ago already Mayne ridiculed deriving 30 parameters from just one (NSPT).
Computer programs based on just one parameter could mislead young unexperienced engineers, whoo could believe uncritically in the output.

51. Qcn = [(qc -  v )/ pa ] (pa/'v )n
Exponent n used for obtaining the normalized parameters Kd and Qcn (used for predicting CRR)
Qcn = [(qc -  v )/ pa ] (pa/'v )n
Uniform NC sand
Due to arching : qc(z) increases less than linearly
Kd = (p0-u0)/'v
n = 1 a simplification – avoids the iterative procedure to determine Qcn and n, an additional soil unknown (n=0.5-1)
Blade no arching : side ratio 6

52. CRR can also be estimated by Vs (e. g
CRR can also be estimated by Vs (e.g. Andrus) but VS is scarcely sensitive to Stress History (necessary for liquefaction)
MATERIAL
INDEX
CONSTRAINED
MODULUS
UNDRAINED
SHEAR STRENGTH
HORIZONTAL
STRESS INDEX
SHEAR WAVE
VELOCITY
CLAY SILT SAND
Vs (m/s) KD
KD crust
Catania
Sand
crust?
“lazy”

53. BARCELONA AIRPORT

54. Monaco (2014) compared parameters measured at Treporti before-after the textbook OC applied by an embankment - subsequently removed :
Concluded : OCR is reflected almost negligibly by VS (lazy) to max degree by MDMT, to a medium degree by qt

55. May help select the design G-γ curve. (Mayne & Martin 1999)
SDMT provides Go (small strain modulus) & MDMT (working strain modulus). Two points of the G-γ curve.
May help select the design G-γ curve. (Mayne & Martin 1999)
Mayne (2001)
Ishihara (2001)

56. Go and MDMT on the G -  decay curve
Mayne (2001)
Ishihara (2001)
low GO/M
high GO/M
SDMT  G small strain modulus
MDMT - working strain modulus (γ = 0.05 – 0.1 %)
two points
G0 / MDMT may provide an estimate of in situ G- decay curve
- Marchetti et al (2008) in Schmertmann Volume
- Lehane & Fahey (2004) Porto ISC-2 – non linear settlement analysis from in situ tests

57. Every SDMT at each Z : Go Id Kd Mdmt (1 point)
Diagram is result of 34 SDMT sites various soils & geography.
Diagram permits to estimate Vs (Go) from just mechanical DMT data i.e. Vs = f(Id Kd MDMT)
No point today.
Better to measure Vs directly
However :May provide rough Vs in previous sites
If Kd was not available, impossible to predict Go from just one parameter. Need at least two parameters.

58. Comparisons Vs measured by SDMT with Vs estimated by mechanical DMT using the previous diagram (L’Aquila)
Amoroso (2013) compares correlations Vs from DMT and Vs from CPT. Concludes : Vs estimates from DMT are closer
(a two parameter test)

59. How the OCR and Cu correlations were derived (clay)
       
Theoretical
1993 Finno
Experimental
1980 & 1995
2004 Yu
1980 OCR correlation : has solid experimental & theoretical roots  a well founded average for “textbook clays”

60. DERIVATION OF Cu CORRELATION
DERIVATION OF Cu CORRELATION
Once having OCR shansep. Ladd : Best Cu not from TRX UU but from oed-pc  OCR  shansep
0.8
Ladd
 Cu = 0.22 σ’v (0.5 Kd)1.25
0.22
Mesri
Ladd (1977) & Mesri (1975) numbers were found as average for textbook clays. Thus Cu as above is average for “textbook clays”.
 the 1980 DMT correlations not correlations found/ envisioned by the author, but the obliged consequential outcome of classic mainstream averages found by others for “textbook clays”

61. Displaying the DMT results
Recommended graphical output TC16.
Diagrams should be presented not separated, but side by side, + expressive
Displaying just po & p1 is unexpressive
The struggle is between factuality (po,p1) and expressivity (figure)
The "conventional" formulae are "average" correlations in textbook soils.
In non textbook soils an alternative could be to plot, superimposed, results by site specific and conventional correlations (also evidencing deviations vs "average" sites).
Recommending the format in Fig. is not to suggest one set over another set of correlations, but preserve simultaneous vision of various profiles.

62. Detecting slip surfaces in clay slopes
(look for Kd  2) … Peiffer 2015
Method permits to verify if an OC clay slope contains active or quiescent slip surfaces(Totani et al. 1997)
Useful to know : Old slip surface may reactivate – Øresidual

63. Validation of DMT-KD method
LANDSLIDE "FILIPPONE" (Chieti)
DOCUMENTED SLIP SURFACE
Kd  2
LANDSLIDE "CAVE VECCHIE" (S. Barbara)
DOCUMENTED SLIP SURFACE
(inclinometers)
Kd  2

64. The paper includes : Section 15. DMT in semiliquid soils
Robertson and Campanella (1986) : “DMT is extremely simple to operate and to maintain“
ASTM : "DMT results are exceptionally reproducible and operator insensitive".
However : when testing semiliquid soils – nearly impossible to investigate by common method - test procedure must be followed scrupulously (details  paper).

65. Here just few tips for semi liquid soils paper.
Take A, B before &after each sounding, several times
Start inflation immediately after reaching the test depth (in practice 1-2 sec), particularly in silts.
Inflate [Zero to A] in 15 sec and [A to B] in 15 sec
If semiliquid soil is silt, try to speed, to possibly remain in undrained conditions. Seasoned operators inflate "rapidly" until 70% of expected reading (A or B), then slow to accurately read the pressure (“quick-slow inflation” - 50%t)
See also in paper Sect 15.4 : Semiliquid and submerged (e.g. bottom slurry of mining wastes hydraulically transported and discharged in a pond).

66. Drainage conditions. “Niche silts”
Sand : drained Clay : undrained Silts : partial
If we stop at a given z and take repeated A-readings…
A B
 
In niche silts : B-A, Id,Ed,M.
But a reasonable A may still be obtained (quick-slow inflation), and the parameters dependent only on A, notably Cu.
Can still do undrained analysis
E.g. in a tailings dam of niche silt : can do undrained analysis
More details in paper
Undrained
Contact
Stress h A
no h decay
substantial h decay
Niche
silt
Clay
log time
In niche silts B is too low (hence too low B-A and B-A derived Id,Ed,M).
Niche silts recognizable : Id0 (low Ed,M)

67. Software “SDMTElab” and “DMT Settlements”
DMT SDMT
File .UNI is the file usable as input by 3rd party subsequent software.
Is the “output to external world”.

68. + Program : “DMT settlements” The soil input is the file .UNI
generated by SDMT Elab
(often run seamlessly in cascade) +
Add
(a) geometry loaded area (b) applied load

69. The practitioner’s Dream
(a) Model specialists (able to avoid pitfalls) prepare FEM programs accepting as input the 3 independent parameters obtained when doing CPT & DMT : Qc Kd Ed
(b) Practitioner just assigns Qc Kd Ed inequivocally measured (+Go) to each element of the mesh...  obtains the solution
Note. Id is not included because Id = Ed / (34.7 s’vo Kd)
Sleeve fs is not included because…

70. Repeatibility v. good v. bad
Eg. Frost (2001) "Underuse" of fs is related to common sentiment that fs is unreliable… CPT in sand is essentially a one-parameter test (or 1.5?).
Repeatibility v. good v. bad
Lunne (CPT10) had CPT done by 4 different well-qualified firms. Qc was found repeatable, fs highly variable.
Lunne concluded “with the present large variations in fs, impossible to utilize this measure…for soil parameters”

71. Sensitivity to h of fs and KD
Reason not just instrumental ! fs not so “fundamental”
fs highly unstable, being what is left after an enormous stress reduction – in a situation of arching, with a stiff soil ring surrounding the sleeve.
Moreover : h sleeve is transformed into vert force, via Øsoil-steel (?)
CIRCULAR
PROBE
FLAT
Sensitivity to h of fs and KD 71

72. Note : fs & KD have the same “source”
both reflect h against probe, but...
KD measures h directly (i.e. po)
fs indirectly, transforming h to Fvertical (ɸ soil-steel ??)
Thus fs  an attenuated KD , weaker and much less stable and direct. And repeatibility... 72

73. Heavy tamping Vibroflotation
Recently : CPT researchers are investigating the possible use of fs /s’v as the CPT version of Kd
But these before-after results (Bałachowski, Nurek DMT’15) appear to indicate that fs is not more sensitive than qc (or even much less) to the Stress History applied by the compaction. Hence it seems unlikely fs can be a substitute of Kd qc fs
Heavy tamping Vibroflotation

74. CPT-DMT inter correlations
Typical dispersion Kd-Qcn in sand
Robertson (2009, Asce) has formulae for estimating DMT from CPT. V. dispersed in particular Kd - Qcn.
Expectable : no way reconstructing Kd sensitive to Stress History from insensitive Qcn.
e.g. Qcn=100 predicts Kd=2 or Kd=6
Huge Kd difference
Some researchers study opposite direction : Qc from DMT. Should have + success. Should be easier to predict one parameter from two than viceversa. DMT a genuine two parameter test. In that DMT appears a +informative test.

75. DMT Website: many papers (DMT 2006, DMT 2015)

76. CONCLUDING REMARKS (1/5)
More and more CPT & DMT replace laboratory for everyday jobs.
Sensitivity of DMT’s Kd to Stress History is important. There are not many Stress History tools.
Stress History is indispensable for good predictions of settlements and liquefaction.

77. PREDICTING SETTLEMENTS
(2/5)
PREDICTING SETTLEMENTS
A large number of comparisons appear to confirm that DMT predicts well settlement.
Good estimates of settlements permit a more rational and economical design. E.g. entity of settlement is needed to decide : piles or shallow foundations ? Info = $

78. KD can lead to a more economical design
(3/5)
KD can lead to a more economical design
KD reflects benefits of Stress History on settlement and liquefaction. SH scarcely sensed by other tools, which ignore SH  benefits are wasted.
Two sites same Qc , different KD
Site 2 “stronger”, not equal !

79. ESTIMATING CRR (liquefaction)
(4/5)
ESTIMATING CRR (liquefaction)
The best estimates of CRR possibly obtained using at same time Qcn -CPT and KD - DMT.
CRR = f(Qcn, KD )
In general : Multiparameter better than one-to-one correlations. Soil has many unknowns : need multiple responses.
Moreover (practical) : one parameter is a “watchdog” to the other parameter.

80. Experience of many researchers/ users appears to confirm :
(5/5)
Experience of many researchers/ users appears to confirm :
DMT is simple. No electronics, no temperature effects, no deairing, no area correction. Not many things can go wrong
As soon as on the site, start immediately to test (no saturation or preliminary)
Easy to run, short training time ( half day)
Highly repeatable. Operators gets same results
No need highly skilled workers