Assessment of Rock Strength from Measuring While Drilling ... · 1 Rodgers M., McVay M., Ferraro C., Horhota D., Tibbetts C., Crawford S. 2017. Measuring Rock Strength While Drilling

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Assessment of Rock Strength from Measuring While Drilling

Shafts in Florida Limestone

Journal: Canadian Geotechnical Journal

Manuscript ID cgj-2017-0321.R1

Manuscript Type: Article

Date Submitted by the Author: 12-Oct-2017

Complete List of Authors: Rodgers, Michael; University of Florida, Civil Engineering McVay, Michael; University of Florida, Civil Engineering Horhota, David; Florida Department of Transportation, State Materials Office Hernando, Jose; Florida Department of Transportation, State Materials Office

Is the invited manuscript for consideration in a Special

Issue? : N/A

Keyword: Drilled Shaft, Measuring While Drilling, Construction Monitoring, Florida, Limestone

https://mc06.manuscriptcentral.com/cgj-pubs

Canadian Geotechnical Journal

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Assessment of Rock Strength from Measuring

While Drilling Shafts in Florida Limestone

Authors:

1Michael Rodgers, [email protected]

1Michael McVay, [email protected]

2David Horhota, [email protected]

2Jose Hernando, [email protected]

Affiliations:

1University of Florida – Herbert Wertheim College of Engineering

Engineering School of Sustainable Infrastructure & Environment

300 Weil Hall, P.O. Box 116550, Gainesville, Florida 32611

2Florida Department of Transportation – State Materials Office

5007 Northeast 39th Avenue, Gainesville, Florida 32609

Corresponding Author:

Michael Rodgers

365 Weil Hall, Gainesville, Florida, 32611

352-422-3882

[email protected]

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Abstract

The focus of this research is the real time assessment of rock strength

(unconfined compressive strength, qu) during drilled shaft installations in Florida

Limestone; where measures of rock strength are provided through five monitored drilling

parameters: torque, crowd, rotational speed, penetration rate, and bit diameter. In

order to complete the study, both a laboratory and field investigation were required.

This paper covers drill rig instrumentation, measuring rock strength during field drilling,

and the comparative analysis of rock strength with conventional methods.

Real time measurements were recorded for each drilling parameter and

graphically displayed on an in-cab monitor and wirelessly transmitted to an external

computer. Measures of rock strength were estimated using a laboratory developed

equation with the monitored drilling parameters for real time field assessment.

Measuring while drilling (MWD) in the field took place at three separate locations where

drilled shaft load testing occurred. Comparative analyses between the monitored shaft

installations and core samples subjected to unconfined compression indicated the

results aligned well when recoveries were good. As recoveries diminished, the mean

strengths were comparable, but more variable.

Keywords

Drilled Shaft, Measuring While Drilling, Construction Monitoring, Florida, Limestone

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Introduction

Over the past few decades there has been an increase in the use of drilled shafts to

support large structures in Florida. The shift from driven piles to drilled shafts is largely

due to the reduction in noise and vibration experienced during construction in urban

areas; and the ability to develop significant axial resistance when the shafts are

socketed into layers of rock. Unfortunately, there are no current methods to quantify

production shaft capacities during drilled shaft installations similar to driven piles. Even

with limited load testing, spatial variability concerns for production shafts on bridges with

multiple piers versus the test shafts is of concern. However, if rock strength could be

assessed during shaft drilling, this would provide a means to ensure that every as-built

foundation meets or exceeds the engineering design. Codes such as Load and

Resistance Factor Design, LRFD, enable engineers to consider different resistance

factors based on the reliability of design and construction practices. Much of the

reliability based design stems from reducing the uncertainty of the subsurface strata by

accounting for the variability of the in situ soil and rock conditions (AASHTO 2010; Abu-

Farsakh and Yu 2010; Abu-Farsakh et al. 2010; Baecher and Christian, 2003; Brown et

al. 2010; Christian 2004; Fenton et al. 2015; Harr 1996; Kuo et al. 2002; Liang and Li

2009; McVay et al. 2002; McVay et al. 2003; O’Neill and Reese 1999; Paikowsky 2004;

National Highway Institute 2001; Yang et al. 2008; Zhang et al. 2005).

The goal of this research is to provide a viable method for monitoring drilled shaft

installations in real time during the drilling process (i.e., measuring while drilling, MWD;

ISO 2016). Utilizing MWD reduces spatial uncertainty concerns by providing a means

to quantify the quality and length of rock sockets for every drilled shaft installed on a

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site. In a laboratory study conducted by 1Rodgers et al. a unique relationship was

developed using Teale’s specific energy equation (1965) for non-percussive rotary

drilling with unconfined compressive strength, qu, for soft-weathered, medium, and

strong Florida Limestone (Figure 1). Teale’s equation only requires measurements of

torque (T), crowd (F), rotational speed (N), penetration rate (u), and bit diameter (d) in

order to measure specific energy (e).

It was ideal to develop the specific energy relationship with unconfined compressive

strength because qu is the most common function of rock strength used in drilled shaft

design for layers of rock and intermediate geomaterial (Brown et al. 2010). The equation

was also developed using only rock augers since this is the tooling most commonly

employed during Florida shaft installations when layers of rock and intermediate

geomaterial (IGM; O’Neill et al. 1996) are encountered. Therefore, the equation is only

intended to be used when a rock auger is employed during drilling, as the mechanical

efficiency of various drilling tools (e.g., roller bits, core barrels, etc.) may not align with

that of a rock auger. Consequently, the results presented within this paper only reflect

the use of rock augers during shaft drilling.

Implementing the developed drilling equation, field monitoring was conducted at

three different drilled shaft installation sites in Florida. This study covers comparisons of

compressive strength obtained from monitoring shaft installations with rock cores

obtained either near the tests shafts or throughout the site. A later paper will focus on

shaft capacity estimates by comparing the estimated capacities obtained from

1 Rodgers M., McVay M., Ferraro C., Horhota D., Tibbetts C., Crawford S. 2017. Measuring Rock

Strength While Drilling Shafts Socketed Into Florida Limestone. ASCE Journal of Geotechnical and Geoenvironmental Engineering. (In Press)

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monitoring drilled shaft installations with conventional load test methods. This paper

also covers instrumenting drill rigs to monitor shaft installations for multiple rig types.

Equipment for Drilled Shaft Construction Monitoring in Real Time

In order to gain a better understanding of what types of drill rigs and tooling were

being used in the field to install drilled shafts; a survey was created and presented to

leading contractors and district geotechnical engineers that practice in the state of

Florida. The intent of the survey was to develop a better understanding of drilled shaft

equipment, as well as typical operating parameters of the rigs used in Florida. This

included general questions such as:

• What types of drill rigs are used and who are the manufacturers?

• How are forces, torque and crowd, applied to the drill bit?

• Are any of the five drilling parameters monitored in any way? If so, how?

From the compiled data, a trend towards the use of hydraulic powered rigs was

observed. The survey results indicated that both applied forces, torque and crowd, are

either provided or measureable via hydraulically driven systems. In most cases, the

surveys indicated that monitoring capabilities are available on many drilled shaft rigs but

not for every rig type.

Monitoring Equipment

With the understanding that a field monitoring system would be required to monitor

the drilling parameters in real time, focus turned to investigating how each drilling

parameter could be monitored on the drill rigs. Several different options were explored

for each drilling parameter. This section covers the options chosen to monitor each

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drilling parameter on the drill rig and the equipment required to provide the monitoring in

real time.

In order to measure torque, crowd, rotational speed, and penetration rate on the drill

rig, the following steps are required. First, the hydraulic pressure lines controlling crowd

and torque need to be tapped with individual pressure transducers. The recorded

pressures then need to be converted to physical measures (e.g., kN for crowd and kN-

m for torque) for use with the laboratory drilling equation. Next, measuring the rotational

speed of the drilling tool requires a proximity sensor to be attached near the rotating

collar of the rotary table, with no conversion necessary. Then, the vertical movement of

the drilling tool may be monitored from line movement of the cabling attached to the

Kelly bar and main winch using a rotary encoder. Penetration rate is determined as a

function of cable movement per unit time, with no conversion necessary. All drilling

parameters and estimated strengths then need to be recorded and displayed as a

function of depth as the drilling tool advances and account for the drilling tool going in

and out of the hole.

After a thorough investigation of available options, it was found that a number of rig

manufactures and commercial vendors (e.g., Bauer, Soilmec, Jean Lutz, etc.) provide

instrumentation for recording and viewing T, F, N, and u in real time. For this work, the

Jean Lutz system was chosen because it was portable, compatible with many different

rig types, and it employed a data acquisition module, the DIALOG, which recorded and

converted the drilling parameters, displayed them in the cab, and wirelessly transmitted

them to a laptop without interrupting the construction process.

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Field Monitoring Equipment Setup and Installation

Throughout the course of the research, drill rigs from three different manufacturers

were monitored. A different drill rig manufacturer was used at each different site:

1. IMT AF250, used at the Little River bridge site in Quincy, FL

2. Bauer BG30 Premium Line, used at the Overland bridge site in Jacksonville, FL

3. Soilmec SR30, used at the FDOT’s Kanapaha site in Gainesville, FL

For the IMT drill rig, a crowd sensor needed to be installed on the rig, but the

remaining sensors were preexisting and installed by the rig manufacturer. To complete

the monitoring setup, the torque, rotational speed, and penetration rate sensors were

simply tapped into and connected to the DIALOG via a junction box.

The Bauer rig was brand new and equipped with the B-tronic monitoring system.

The B-tronic system provides fully functional sensors with the capability of monitoring

and recording all the needed drilling parameters. Integrating the DIALOG required

wired connections to be made for the rotational speed and penetration rate sensors at

the preexisting terminal connections, located in the electrical unit on the drill rig. For the

torque and crowd sensors, “copy modules”, provided by Jean Lutz, were installed. The

copy modules were also wired into the preexisting terminal connections located in the

electrical unit, and used to bridge the original connections. This routed the received

signals from each of the sensors to both the B-tronic and the DIALOG. For this site,

both systems were active and used to record the monitored drilling parameters.

The Soilmec rig was also equipped with its own fully functional monitoring system,

the Drilling Mate System (DMS). However, the signals produced from the DMS sensors

were not compatible with the DIALOG. Therefore, monitoring was provided through the

use of Jean Lutz sensors. This required sensors to be installed for rotational speed,

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penetration rate, torque, and crowd. Each sensor was routed to a junction box and

connected to the DIALOG to provide full monitoring capabilities.

Tapping into preexisting sensors typically requires splicing cabling with a multi-pin

connection that matches the existing connection. However, the IMT rig was equipped

with extra multi-pin connections, greatly simplifying the installation. Figure 2 shows the

depth sensor tie-in connection on the IMT rig. Cabling from the tie-in was routed along

the same path as the existing sensor cabling and connected to a junction box located in

the electrical compartment. Tapping into the IMT rotational speed and torque sensors

was completed using the same method.

The existing depth sensor in Figure 2, is a preinstalled rotary encoder integrated into

the main cable winch. Similarly, the Jean Lutz sensor used on the Soilmec rig,

presented in Figure 3, was also a rotary encoder, mounted on the outer rim of the main

cable winch. After installation, the depth sensor is calibrated by comparing the tracked

movement with that of the in-cab monitor readout as well as physical measurements of

vertical drill bit movement.

Monitoring rotational speed is performed using a proximity sensor mounted on a

stationary location at the base of the rotary table as indicated in Figure 4. Steel bolts

are evenly spaced around the rotating collar of the rotary head, where rotation occurs

without wobbling, and welded into positon. The proximity sensor detects each bolt as

the collar rotates and the rotational speed can be determined directly. The sensor is

then calibrated by comparing measured rotary speeds to the in-cab monitor readout,

and through visual inspection by counting the approximate number of rotations over a

minute.

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The torque and crowd sensors are tied into the hydraulic lines where the existing

sensors are located or in locations along the hydraulic lines where differential pressures

are not experienced as presented in Figure 5. Cabling from both sensors are routed to

the junction box.

Cabling from the junction box is routed to the main cab and connected to the

DIALOG (Figures 6 and 7). Typically there are openings on the floor board or the back

wall of the main cab that allow the cabling to be easily routed to a data acquisition

module. Presented in Figure 7, are both the DIALOG and B-tronic systems monitoring

a drilled shaft installation in real time.

All of the monitoring equipment used in this study was designed and installed in a

manner that does not interrupt or interfere with the drilling process. The DIALOG was

also used to provide real time external visualization of the monitored shaft installation

away from the drill rig. The monitored data was wirelessly transmitted to an external

computer, via Bluetooth technology, where a graphical display of torque, crowd,

rotational speed, and penetration rate was provided in real time. Additionally, measures

of rock strength (qu) were estimated using the laboratory developed equation with the

monitored drilling parameters for the construction engineering inspector’s analysis at a

safe distance (Figure 8).

Once the monitoring equipment is installed and calibrated, the next step is deriving

conversion coefficients, K, for both the hydraulic torque and crowd measurements. The

conversion coefficients transform the recorded hydraulic pressures to physical

measures of the drilling parameters that are compatible with the developed laboratory

drilling equation. This is achieved through inspection of the drill rigs operator’s manual

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and the drill rig itself. The following provides the development of rig conversion

coefficients for both torque and crowd.

The first step to developing the K-coefficients is to determine whether or not the drill

rig is a multi-drive system that will use multiple gears during rock drilling. This is

important because K-coefficients for torque need to be derived for each gear that is

used during rock drilling, whereas crowd only requires one coefficient that can be used

with any gear. In most cases, at least one additional gear is found on the drill rig that

provides higher rotational speeds with less available torque. However, this gear is

typically only used for spinning off drilled debris from the auger bit. In order to monitor

torque, generally only one gear needs to be considered. Of the three drill rigs

monitored during this research, only the IMT rig utilized a multi-drive system which

required K-coefficients to be developed for two gears used during rock drilling. Once

the gear setup is confirmed, the next step is to determine the maximum torque, crowd,

and hydraulic pressures available within the system. These specifications are often

provided in the operator’s manual and in most cases found on the drill rig itself. Once

the needed parameters are determined, the conversions are made using the following

equation:

Torque or Crowd = K * (Operating Pressure – Threshold Pressure) (1)

The threshold pressure can be determined by checking the pressures recorded in

the hydraulic lines on the in-cab monitor or DIALOG while the bit is at rest and no

rotation or penetration is taking place. This provides a single equation with a single

unknown that can be solved straightforward. With the installation, calibration, and

conversion coefficients derived, the rig is now ready to begin monitoring.

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Throughout the course of the research, it was found that drill rig monitoring systems

typically sample the drilling parameters at several hundred to thousands of samples per

second. For this study, each sensor was monitored at a 1 kHz sampling rate and

simultaneously converted to measurements compatible with the laboratory drilling

equation. An average value for each drilling parameter was recorded and visually

displayed for every 2 centimeters of penetration.

Figures 9 through 18, provide elevation vs. drilling parameter plots displaying the

large quantity of measurements that are recorded for each drilling parameter as the drill

bit is advanced. Frequency distributions are also provided to show the spread of data

and the variability of each drilling parameter. As previously discussed, crowd and

torque must be converted to physical measures using the developed conversion

coefficients. Therefore, frequency distributions are provided for the raw data recorded

for both torque and crowd, as hydraulic pressures, as well as the distributions after

conversions to physical measures. The following drilling data was obtained at the Little

River bridge site, where the IMT drill rig was used, to illustrate the importance of

developing K-coefficients for each gear used on a multi-drive torque system.

During general field drilling operations, the rotational speed is held fairly constant,

which requires a certain amount of torque to keep the bit spinning. The crowd is

regulated to prevent the bit from stalling, and the penetration rate is a byproduct of the

consistent rotational speed, regulated crowd, required torque, and the strength of rock

encountered (1Rodgers et al.). Since rotational speed and crowd are essentially

constants during field drilling, the two drilling parameters provide little insight for



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changes in rock strength. Consequently, the torque to penetration rate ratio (T/u)

indicates changes in rock strength in terms of specific energy. Therefore, it is expected

that the frequency distributions for drilling parameters T and u should be reflective of the

strata encountered, and the distributions for crowd and rotational speed should indicate

a fairly consistent application. Upon inspection of Figures 10 and 15, normal

distributions are found for both crowd and rotational speed, which indicates consistent

application with some expected variation. In Figure 10, when all of the rotational speed

data points are included, the distribution appears bimodal. However, this is simply a

result of two gears being used during drilling. When the data points are separated by

each gear, two individual normal distributions are found. This is validated by the nearly

identical mean and median values found for each gear, which was also found for the

crowd distribution. Observed in Figures 12 and 18, both torque and penetration rate

produced lognormal distributions which is indicative of the strata encountered, verified

by the recovered core samples, and will be discussed later. Therefore, the recorded

drilling parameters were indicative of general field drilling operations and the site

stratigraphy.

In Figures 17 and 18, there are torque distributions provided for each gear that was

used with the multi-geared IMT drill rig. When the raw data is presented (Figure 17) it

appears that the 2nd gear torque values are generally higher than the 1st gear values.

This was counterintuitive to what was found in the lab where higher rotational speeds

produced lower torque values (1Rodgers et al.). The deceptive raw result stems from

each gear requiring a different K-coefficient, derived from Equation 1, to make the



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conversions from hydraulic pressures to physical measures. Once the conversions are

applied using a unique K-coefficient for each gear, the 2nd gear torque values are

reduced and the results are in agreement with the laboratory findings. This can be seen

in the summary of statistics box (Figure 18) that shows 1st gear, which produced the

lowest rotational speeds, provided higher torque values than 2nd gear on average.

Once the hydraulic pressures are properly converted for torque and crowd, the

assessment of specific energy and average rock strength, per 2 centimeters of

penetration, may be determined. Using the drilling equation developed in 1Rodgers et

al. (Figure 1) for specific energy in terms of unconfined compressive strength,

� = 9.54 × 10�� + 13.7� (2)

where,

e = specific energy (kPa);

qu = unconfined compressive strength (kPa).

The equation is first set to equal to zero,

9.54 × 10�� + 13.7� − � = 0

and a quadratic solution for rock strength (qu) in terms of specific energy (e),

� =�±√��

�� (3)

is developed by substituting terms in for a, b, and c,

� (��) =��. !"(��. )��(#.$�×�%&')(()

�(#.$�×�%&') (4)

Equation 4 was used to estimate the rock’s unconfined compressive strength during

drilling using the monitored drilling parameters.

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Analysis of Rock Strength from Real Time Field Monitoring

As previously stated, three field monitoring opportunities were presented which

provided the first field MWD trials using the laboratory derived drilling equation. The

locations were at the Little River bridge site in Quincy, FL, the FDOT’s Kanapaha site in

Gainesville, FL, and the Overland bridge site in Jacksonville, FL. The sites were

chosen because each location had planned drilled shaft installations with subsequent

load testing. In addition, all of the test shafts were instrumented with strain gauges

along their length to assess skin friction by layer. This provided a means to directly

compare compressive strength and shaft capacity estimates, obtained from MWD, with

recovered core samples and the actual measured capacity in mobilized portions of each

shaft using conventional methods. Additionally, each location used a different type of

load testing, Osterberg testing at Little River, Static Top-down testing at Kanapaha, and

Statnamic testing at Overland. This provided direct comparative data from three of the

most conventional load testing methods used in practice. This also provided the field

MWD study with three variations in the following categories: location (limestone

formations), shaft diameter, drill rigs used to install the shafts, drilling crews, drill bits,

and drill bit tooth configurations. These variable drilling parameters provided great

insight as to how well the laboratory drilling equation performed when drilling conditions

and rig configurations changed. This section and the remainder of this paper will cover

the comparative analysis of unconfined compressive strength values obtained from

MWD and recovered core samples tested (ASTM, 2002) in the laboratory at the Florida

Department of Transportation’s State Materials Office. Comparative analysis of skin

friction and shaft capacity estimates will be covered in a later paper.

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Using the laboratory developed drilling equation, Equation 4, measurements of rock

strength, qu, were obtained at the three sites during the installation of each test shaft.

Core samples obtained within 30 meters (100 feet) of each test shaft, and tested in the

laboratory, were used for the comparison. This provided comparative core data from

four borings obtained at Little River, nine borings at Kanapaha, and five borings at

Overland. At each location, different degrees of subsurface and site variability were

experienced, as well as rock core recoveries (REC%).

At Little River, core data obtained from the entire site indicated there was a high

degree of variability, as the coefficient of variation (CV) was equal to 1.79 (Herrera and

Jones 2016). At the site, the subsurface strata was interlaced with cemented clays,

IGM, and limestone with a wide range of compressive strengths, spanning from 4 to

4,300 psi. Fortunately, the mean core sample recovery was very good (REC% = 85%)

providing 37 core strength samples for comparison within the investigated depth range.

Figures 19 and 20 provide the qu frequency and cumulative frequency distributions for

both the recovered/measured core sample strengths as well as the values obtained

from MWD using a 1.2 meter (4 foot) rock auger.

Observed in Figure 19, the core data and MWD results both indicate a lognormal

distribution, which was indicated by the torque and penetration rate distributions

presented earlier. In the summary of statistics box of Figure 19, the average qu value

obtained from MWD is in good agreement with the values obtained from the laboratory

tested core samples. Additionally, the frequency and cumulative frequency distributions

align very well with one another. This indicates that the recovered core samples, used

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for standard drilled shaft design, were reflective of the strata encountered during the

shaft installation.

At site 2, Kanapaha, the Ocala Limestone formation was encountered. Located

within the Ocala uplift, which is identified as karst and potentially cavernous from

weathering, the Ocala Limestone formation is one of the oldest in Florida, formed 35

million years ago (Bryan et al. 2001). In locations where seismic testing indicated rock

was present (Figure 21), SPT and core runs were performed. Generally, the SPT and

core borings showed little if any loss of circulation during progression of the casing.

However, the SPT spoon and core barrel samples showed that the rock was weak and

very friable due to the high degree of weathering as presented in Figures 22 and 23.

Since the planned load test was Static Top-down, three shafts were installed at the

site, with a total of nine borings performed in close proximity to each of the three shafts

(2 reaction shafts and 1 test shaft) comprising the Static Top-down load test setup

(ASTM 2013). Three of the borings were performed within the footprint of each shaft.

The remaining six borings were performed within five feet from the center of each shaft,

providing three borings per shaft. From the 9 borings, with 18 meter (60 foot) core runs

at each location, only 19 core samples were viable for qu testing in the depth range of

interest (9.1 to 15.2 meters; 30 to 50 feet). All core runs had an approximate average

recovery of 30%, with REC% ranging from 20% to 40%. Figures 24 and 25 provide the

frequency and cumulative frequency distributions at Kanapaha from core sampling as

well as measuring while drilling all three shaft installations using a 0.9 meter (3 foot)

rock auger.

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In Figure 24, the summary of statistics shows that both the mean and median

compressive strength from core sampling is higher than the average obtained from

MWD. However, the standard deviation and CV are much higher for MWD versus the

core sample data. This is attributed to lack of low strength samples recovered during

core sampling. Evident from Figure 25, nearly 60% of the MWD qu measurements were

less than 1,000 kPa, whereas 15% was reported from core sampling. As previously

stated, little if any loss of circulation occurred during the progression of the casing,

indicating weak friable rock existed throughout the site. It is proposed that the low

recoveries (30% recovered and 70% unaccounted) may be associated with the friable

nature of the rock as well as coring practices. To clarify, coring was completed at each

site using conventional methods with a double wall core barrel, however, core samples

at the Kanapaha site were not recovered until the core sample diameter was reduced

from 6.1 cm to 4.8 cm and the injection flow rate was restricted to a minimal level. Also

contributing to the limited core data is the ASTM required 2:1 aspect ratio for unconfined

compression testing of field cores (ASTM 2002).

The lack of intact core samples available for testing provided limited insight for the

stratigraphy encountered at Kanapaha. It was estimated that 2 layers, 11.9 to 13.7

meters and 13.7 to 15.2 meters (39 to 45 feet and 45 to 50 feet), existed in the test shaft

location. Unfortunately, with less than 10 samples per layer from all nine borings,

estimates of individual layer statistics were limited. Shown in Figure 26 are the MWD

point values along with mean rock strength per 0.3 meters (solid black line) estimated

during drilling. Also shown in Figure 26 are the average rock strengths from the core

data (red dashed lines). As evident from the data, conventional coring practices were

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unable to properly address the layering present at the site due to the poor recoveries.

The MWD results indicated 4 or 5 values near the core data average for the bottom

layer, but the majority were lower, showing the benefits of the MWD approach which

produces a much larger sampled population with a high degree of precision.

Since core strengths are generally averaged over large areas in practice, higher CV

values are often found in locations such as Kanapaha and lower LRFD resistance

factors, Φ, are warranted. However, with MWD, obtaining measures of lower strength

rock can be achieved. Therefore, changing the embedment depth of drilled shafts

socketed in layers of weak rock becomes viable; which will result in considerable

savings compared to applying a lower LRFD resistance factor for all shafts on a site due

to the high variability of rock strength.

This concept was quite relevant at the Overland bridge site in Jacksonville, Florida,

where three Statnamic load tests were performed to assess the high degree of

variability and poor recoveries. However, due to the conservative designs, few of the

instrumented shaft segments exhibited full mobilization of resistance, even though the

shafts were loaded to four times the design capacity. Overall, the Overland site had the

largest variability with the CV = 2.54 as well as the worst average site recovery (REC%

= 17%). The recoveries were so poor, that in the depth range where a rock auger was

employed during shaft installation, there were only four qu core samples recovered;

even though 4 of the 5 core borings were completed within three meters of the test

shaft. Similar to Kanapaha, 85% of the MWD qu values at Overland were lower than the

lowest qu core sample recovered, suggesting that core sampling tends to recover higher

strength material. Furthermore, the 4 closest borings, less than three meters from the

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test shaft, were performed specifically for this research and still provided little

information due to poor recoveries.

Conclusions

This work focused on implementing measuring while drilling (MWD) practices during

Florida drilled shaft installations. Measurements of unconfined compressive strength

(qu) were provided using the drilling equation developed by 1Rodgers et al. for rock

augers. The new equation estimates qu based on monitored drilling parameters, torque,

crowd, rotational speed, penetration rate, and drill bit diameter, through a nonlinear, 2nd

order, expression of Teale’s specific energy for non-percussive rotary drilling. The MWD

results were subsequently compared to rock cores recovered within 30 meters (100

feet) of each monitored shaft installation.

In the field, monitoring equipment was acquired and used to measure the same five

drilling parameters used in Teale’s specific energy equation. The equipment included

pressure transducers used to tap into the hydraulic lines providing torque and crowd to

the drill bit, a proximity sensor to monitor rotational speed at the rotary table, a rotary

encoder mounted on the rim of the main cable winch to monitor penetration rate, a

junction box to receive the signals from each sensor, and a data acquisition module to

record, display, and transmit the data wirelessly to an external computer in real time via

Bluetooth.

Field monitoring took place at three separate locations where drilled shaft load

testing occurred. The locations were at the Little River bridge site in Quincy, Florida,

the FDOT’s Kanapaha site in Gainesville, Florida, and the Overland bridge site in



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Jacksonville, Florida. The field investigation provided three variations in the following

categories: location, drill rigs used to install the shafts, shaft diameters, drilling crews,

drill bits, drill bit tooth configurations, and limestone formations encountered. These

variable drilling conditions provided great insight on how well the laboratory drilling

equation performs when drilling conditions, rig configurations, and Florida limestone

formations change. Based on the results of this study the following conclusions can be

drawn:

• For sites with good recoveries (Rec > 85%) both the frequency distribution and

the cumulative frequency distribution compare very favorably between MWD

estimated and laboratory measured rock strength.

• As the rock core recoveries on a site diminish, the average strengths, MWD

estimated and laboratory measured, of the site may compare, but the variability

will differ quite a bit. This is attributed to missing lower strength data that is not

recovered during conventional core sampling.

• MWD provides good insight for vertical layering at low recovery sites. This was

quite evident at Kanapaha, where 9 core borings only produced 19 core samples

for compression testing and provided limited insight in terms of layering.

Whereas, monitoring the shaft installations through MWD provided 430 qu

measurements and vertical layering was able to be identified. At the site, MWD

produced 20 times the amount of data as standard core sampling, in a third of

the sampled locations.

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• Drilled shaft MWD produced qu measurements for every 2 centimeters of

penetration, providing a profile of rock layering at a degree of precision that could

not be achieved through any other method.

• This research took the first steps towards identifying and reducing the influence

of spatial variability on drilled shaft response during the construction phase.

Acknowledgments

The assistance of the FDOT’s State Materials Office as well as the district and

central Geotechnical Engineers is greatly appreciated. The authors would like to thank

all participants that assisted with the field research, Jean Lutz: Michel Lariau, RS&H:

Tony Manos and Tim Brown; Case Atlantic: J.R. “Hawk” Hawkins and Chris Patrick;

Archer Western: Heath Bunn, Patricio Degaudenzi, Paul Harrell, Joshua Bachman,

Jimmy Graham, and Mike Close; Eisman & Russo, Inc.: Bill Brown, Joe Delucia, Al

Moyle, John Kemp, and Tony Mahfoud; Moretrench: Jeff Lewman, Kris Strenberg, Tom

Robertson, and Harley; Reliable Constructors, Inc.: Roger Rehfeldt, Ray Rehfeldt,

Graylan “Spyder” Hodge, Craig Eggert, Austin West, Arthur Wright, and Reagen Norris;

Loadtest: Bill Ryan, Roberto Singh, Denton Kort, Dany Romero, and Adam Scherer;

AFT: Don Robertson, Michael Muchard, Nicholas Pigott, and Evan Clay; Universal

Engineering: Jeff Pruett, Adam Kirk, Chris Shaw, and Josh Adams; FDOT: Bruce

Swidarski, Todd Britton, Kyle Sheppard, Travis “Dalton” Stevens, Jimmy Williams,

Michael Horst, Jesse Sutton, Chandra Samakur, John Hardy, Jamie Rogers, Enondrus

Phillips, Patrick Munyon, Jason Thomas, Sam Weede, Gabriel Camposagrado, Chuck

Crews, Bo Cumbo, and David Gomez; University of Florida: Jon Sinnreich, Richard

Booze, Sudheesh Thiyyakkandi, Scott Wasman, Mike Faraone, Khiem Tran, Cary

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Peterson, and Anand Patil. Without your assistance, this research would not have been

possible.

Funding: This work was supported by the Florida Department of Transportation

through research contract No. BDV 31 977 20. The opinions, findings and conclusions

expressed in this publication are those of the authors and not necessarily those of the

Florida Department of Transportation or the U.S. Department of Transportation.

References

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State Highway and Transportation Officials. Washington, DC.

Abu-Farsakh, M.Y., and Yu X. 2010. Interpretation Criteria to Evaluate Resistance

Factors for Axial Load Capacity of Drilled Shafts. Transportation Research Record

2202, Transportation Research Board. Washington, DC; 20–31.

Abu-Farsakh, Y.M., Yu, X., Yoon, S., and Tsai, C. 2010. Calibration of Resistance

Factors Needed in the LRFD Design of Drilled Shafts. Louisiana Transportation

Research Center, Rep. No. 470. Baton Rouge, LA.

American Society for Testing and Materials. 2002. Standard Test Method for

Unconfined Compressive Strength of Intact Rock Core Specimens. ASTM

International, ASTM D2938-95. West Conshohocken; PA.

American Society for Testing and Materials. 2013. Standard Test Methods for Deep

Foundations Under Static Axial Compressive Load. ASTM International, ASTM

D1143. West Conshohocken; PA.

Baecher, G.B., and Christian, J.T. 2003. Reliability and Statistics in Geotechnical

Engineering. Wiley. West Sussex, England.

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Brown, D.A., Turner, J.P., and Castelli, R.J. 2010. Drilled Shafts: Construction

Procedures and LRFD Design Methods. Publication No. FHWA-NHI-10-016, Federal

Highway Administration. Washington, DC.

Bryan, J.R., Scott, T.M., and Means, G.H. 2001. Roadside Geology of Florida. Mountain

Press Publishing Company. Missoula; MT.

Christian, J.T. 2004. Geotechnical Engineering Reliability: How Well Do We Know What

We Are Doing? ASCE Journal of Geotechnical and Geoenvironmental Engineering.

130:10; 985-1003.

Fenton, G.A., Naghibi, F., Dundas, D., Bathurst, R.J., and Griffiths, D.V. 2015.

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Harr, M.E. 1996. Reliability Based Design in Civil Engineering, Dover, NY.

Herrera, R., and Jones, L. 2016. Drilled shaft design and load testing in Florida

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Journal of the Transportation Research Board, No. 2579, Transportation Research

Board. Washington, D.C.; pp. 32-39. DOI: 10.3141/2579-04.

ISO/IEC. 2016. Geotechnical Investigation and Testing – Field Testing – Part 15:

Measuring While Drilling. ISO 22476-15:2016, International Standards Organization.

Geneva, Switzerland.

Kuo, C.L., McVay, M., and Birgisson, B. 2002. Calibration of Load and Resistance

Factor Design. Transportation Research Record 1808, Transportation Research

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Liang, R., and Li, J. 2009. Resistance Factors Calibrated from FHWA Drilled Shafts

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Congress and Equipment Expo 2009, ASCE. Reston, VA.

McVay, M., Birgisson, B., Nguyen, T., and Kuo, C. 2002. Uncertainty in LRFD Phi, ϕ,

Factors for Driven Prestressed Concrete Piles. Transportation Research Record

1808, Transportation Research Board. Washington, DC; 99–107.

McVay, M., Ellis, R., Birgisson, B., Consolazio, G., Putcha, S., and Lee, S. 2003. Load

and Resistance Factor Design, Cost, and Risk: Designing a Drilled Shaft Load Test

Program in Florida Limestone. Transportation Research Record 1849,

Transportation Research Board. Washington, DC; 98–106.

National Highway Institute. 2001. Load and Resistance Factor Design (LRFD) of

Highway Bridge Substructures. Publication FHWA-HI-98-032, Federal Highway

Administration. McLean, VA.

O’Neill, M.W., and Reese, L.C. 1999. Drilled shafts: Construction Procedures and

Design Methods. Publication FHWA-IF-99-025, Federal Highway Administration.

Washington, DC.

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Drilled Shafts in Intermediate Geomaterials. Publication FHWA-RD-95-172, Federal

Highway Administration. Washington, DC.

Paikowsky, S.G. 2004. Load and Resistance Factor Design (LRFD) for Deep

Foundations. Publication NCHRP-507, Transportation Research Board. Washington,

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Teale, R. 1965. The Concept of Specific Energy in Rock Drilling. International Journal of

Rock Mechanics and Mining Sciences. 2:57–73.

Yang, X.M., Han, J., Parsons, R.L., and Henthorne, R. 2008. Resistance Factors for

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Figure Captions

Figure 1. The specific energy (e) vs. unconfined compressive strength (qu) relationship developed by 1Rodgers et al. – With permission from ASCE.

Figure 2. Tapping into the IMT depth sensor.

Figure 3. Jean Lutz penetration rate sensor (Soilmec rig).

Figure 4. Jean Lutz rotational speed sensor (Soilmec rig).

Figure 5. Jean Lutz sensors tapping into the torque and crowd hydraulic lines.

Figure 6. Jean Lutz junction box located in electrical compartment.

Figure 7. DIALOG and B-tronic both monitoring a shaft installation in real time.

Figure 8. External viewing of a monitored shaft installation via Bluetooth.

Figure 9. Elevation vs. rotational speed.

Figure 10. Rotational speed frequency distribution.

Figure 11. Elevation vs. penetration rate.

Figure 12. Penetration rate frequency distribution.

Figure 13. Elevation vs. crowd.

Figure 14. Crowd raw data frequency distribution.

Figure 15. Crowd converted frequency distribution.

Figure 16. Elevation vs. torque.

Figure 17. Torque raw data frequency distribution.

Figure 18. Torque converted frequency distribution.

Figure 19. Little River qu frequency distribution.

Figure 20. Little River qu cumulative frequency distribution.

Figure 21. Seismic results displaying higher waves speeds which is indicative of rock.

Figure 22. Limestone recovered at 10 meters with grey clay at the top of the spoon.

Figure 23. Very friable limestone at a depth of 10 meters.

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Figure 24. Kanapaha qu frequency distribution.

Figure 25. Kanapaha qu cumulative frequency distribution.

Figure 26. Monitored drilling at Kanapaha, elevation vs. rock strength (qu).

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Figure 1. The specific energy (e) vs. unconfined compressive strength (qu) relationship developed by 1Rodgers et al. – With permission from ASCE.

e = 9.54E-04*qu2 + 13.7*qu

R² = 0.85

50 000

100 000

150 000

200 000

250 000

300 000

350 000

400 000

450 000

500 000

2 000 4 000 6 000 8 000 10 000 12 000

Spec

ific

Ener

gy, e

(kPa

)

Compressive Strength, qu (kPa)

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Figure 2. Tapping into the IMT depth sensor.

Tapping into the depth sensor using a multi-pin connection. The Jean Lutz cable is green.

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Figure 3. Jean Lutz penetration rate sensor (Soilmec rig).

Depth Sensor

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Figure 4. Jean Lutz rotational speed sensor (Soilmec rig).

Proximity Sensor Steel Bolts

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Figure 5. Jean Lutz sensors tapping into the torque and crowd hydraulic lines.

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Figure 6. Jean Lutz junction box located in electrical compartment.

Junction Box

Cable running to the cab

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Figure 7. DIALOG and B-tronic both monitoring a shaft installation in real time.

DIALOG

B-tronic

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Figure 8. External viewing of a monitored shaft installation via Bluetooth.

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Figure 9. Elevation vs. rotational speed.

8.0

9.0

10.0

11.0

12.0

13.0

14.0

15.0

16.0

17.0

0 5 10 15 20 25 30

Elev

atio

n (m

)

Rotational Speed, N (RPM)

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Figure 10. Rotational speed frequency distribution.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Freq

uenc

y

Rotational Speed, N (RPM)

All

1st Gear

2nd Gear

Rotational Speed, N (RPM) Stats All 1st Gear 2nd Gear Average 12.2 10.6 22.6 Median 10.6 10.6 22.9 Maximum 26.0 15.9 26.0 Minimum 6.7 6.7 17.6 Std. Dev. 4.3 0.6 2.1 CV 0.35 0.05 0.09 Count 370 318 52

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Figure 11. Elevation vs. penetration rate.

8

9

10

11

12

13

14

15

16

17

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Ele

vati

on

(m

)

Penetration Rate, u (cm/min)

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Figure 12. Penetration rate frequency distribution.

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

3 8 13 18 23 28 33 38 43 48 53 58 64 69 74 79 84 89 94 99 152Fr

eque

ncy

Penetration Rate, u (cm/min)

Stats u (cm/min) Average 16.3 Median 12.5 Maximum 143.5 Minimum 0.2 Std. Dev. 16.4 CV 1.01 Count 370

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Figure 13. Elevation vs. crowd.

8

9

10

11

12

13

14

15

16

17

0 20 40 60 80 100 120 140 160 180

Elev

atio

n (m

)

Crowd, F (N)

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Figure 14. Crowd raw data frequency distribution.

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

35 40 45 50 55 60 65 70 75 80 85 90 95 100105110115120125130135140145150155160

Freq

uenc

y

Crowd, F (Bar)

Stats F (Bar) Average 88.7 Median 87.8 Maximum 151.9 Minimum 32.8 Std. Dev. 19.8 CV 0.22 Count 370

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Figure 15. Crowd converted frequency distribution.

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

23 29 35 41 47 53 59 64 70 76 82 88 94 100106112118124130136142148154160166172

Freq

uenc

y

Crowd, F (kN)

Stats F (kN) Average 86.8 Median 85.7 Maximum 162.3 Minimum 20.1 Std. Dev. 23.6 CV 0.27 Count 370

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Figure 16. Elevation vs. torque.

8

9

10

11

12

13

14

15

16

17

0 20 40 60 80 100 120 140 160 180 200

Elev

atio

n (m

)

Torque, T (kN-m)

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Figure 17. Torque raw data frequency distribution.

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

40 50 60 70 80 90 100

110

120

130

140

150

160

170

180

190

200

210

220

230

240

250

260

270

Fre

qu

ency

Torque, T (Bar)

All

1st Gear

2nd Gear

Raw Data Torque, T (Bar) Stats All 1st Gear 2nd Gear Average 131.8 119.9 204.6 Median 119.1 110.4 209.1 Maximum 258.8 258.8 251.7 Minimum 40.5 40.5 147.9 Std. Dev. 49.2 41.1 27.4 CV 0.37 0.34 0.13 Count 370 318 52

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Figure 18. Torque converted frequency distribution.

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

28 35 42 49 56 63 70 77 84 91 98 105112119126133140147154161168175182189

Freq

uenc

y

Torque, T (kN-m)

All

1st Gear

2nd Gear

Converted Torque, T (kN-m) Stats All 1st Gear 2nd Gear Average 84.4 84.9 81.4 Median 78.6 78.1 83.2 Maximum 183.3 183.3 100.2 Minimum 28.7 28.7 58.9 Std. Dev. 27.3 29.1 10.9 CV 0.32 0.34 0.13 Count 370 318 52

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Figure 19. Little River qu frequency distribution.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

3 750

7 500

11 250

15 000

18 750

22 500

26 250

30 000

33 750

37 500

41 250

45 000

48 750

52 500

Freq

uenc

y

Unconfined Compressive Strength, qu (kPa)

Core Data

MWD

Compressive Strength, qu (kPa) Stats Core Data MWD Average 4 924 4 469 Std. Dev. 6 556 5 681 CV 1.33 1.27 Median 761 2 702 Maximum 24 236 51 992 Minimum 31 55 Count 37 370

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Figure 20. Little River qu cumulative frequency distribution.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

7 500 15 000 22 500 30 000 37 500 45 000 52 500

Freq

uenc

y


Core Data

MWD

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Figure 21. Seismic results displaying higher waves speeds which is indicative of rock.

Rock at 10 meters

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Figure 22. Limestone recovered at 10 meters with grey clay at the top of the spoon.

Weak Weathered Limestone

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Figure 23. Very friable limestone at a depth of 10 meters.

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Figure 24. Kanapaha qu frequency distribution.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

500

1 000

1 500

2 000

2 500

3 000

3 500

4 000

4 500

5 000

5 500

6 000

Freq

uenc

y


Core Data

MWD

Compressive Strength, qu (kPa) Stats Core Data MWD Average 1 964 1 354 Std. Dev. 945 1 214 CV 0.48 0.90 Median 1 957 823 Max 3 747 5 988 Min 548 24 Count 19 430

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Figure 25. Kanapaha qu cumulative frequency distribution.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 000 2 000 3 000 4 000 5 000 6 000

Freq

uenc

y


Core Data

MWD

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Figure 26. Monitored drilling at Kanapaha, elevation vs. rock strength (qu).

-15.2

-14.2

-13.2

-12.2

500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 5 000

Dep

th (m

)


MWD

MWD Average

Core Data

Page 53 of 53



Documents

Assessment of Rock Strength from Measuring While Drilling ... · 1 Rodgers M., McVay M., Ferraro C., Horhota D., Tibbetts C., Crawford S. 2017. Measuring Rock Strength While Drilling