reliability of textural analysis of ancient plasters and mortars through automated image analysis

Materials Characterization

Reliability of textural analysis of ancient plasters and mortars

through automated image analysis

Federico Caro*, Andrea Di Giulio

Dipartimento di Scienze della Terra, Universita degli Studi di Pavia, Strada Ferrata, 1-27100 Pavia, Italia

Received 8 January 2004; accepted 24 June 2004

Abstract

For the study of ancient or recent plasters and mortars, it is necessary to determinate some morphological and textural

parameters to describe the aggregate fraction and its relationship with binder fraction. Textural analyses are commonly

performed with simple visual comparators or, at most, with a time-consuming manual point counting. The introduction of

automatic or semiautomatic image analysis techniques seems to have made this kind of determinations faster and less biased.

To compare data coming from different image-processing sequences or different analytical approaches, reliability and

calibration need to be tested by each operator. A critical principle setup is presented for comparison using laboratory mortars

with well-known textural features; classical analysis by sieving, point-counting technique and automated image analysis were

performed. This empirical strategy allowed (1) to develop a simple and easy-to-use software-based image analysis system for

plaster and mortar samples; (2) to pull out the relations between samples textural features and reliability of the different

approaches; (3) to provide linear relations that allow to convert apparent measured features to real features. Results also

underline the need to calibrate and standardize image analysis techniques before data handling.

D 2004 Published by Elsevier Inc.

Keywords: Mortars; Thin section; Textural analysis; Automated image analysis

1. Introduction

The characterization of building materials, such as

mortars and plasters, can give useful and unique

clues when different technological and historical

questions need to be answered. Determination of

1044-5803/$ - see front matter D 2004 Published by Elsevier Inc.

doi:10.1016/j.matchar.2004.06.014

* Corresponding author. Tel.: +39 382505846; fax: +39

382505890.

E-mail address: [email protected] (F. Caro).

chemical, physical, mineralogical and petrographical

parameters of the different components of plasters

and mortars permits:

– getting evidence of the different construction

periods;

– increasing the knowledge of regional historical

production and techniques;

– finding the correct formula of compatible restora-

tion materials and providing valuable information

for conservation purposes;

53 (2004) 243–257

F. Caro, A. Di Giulio / Materials Characterization 53 (2004) 243–257244

– weighting up the variations of statistical data

among each sampling campaign inherent to differ-

ent construction periods.

For these purposes, determination of some

morphological and textural parameters needs to

be carried out to describe the aggregate fraction

and its relationship to the binder fraction. Within

the various analytical methodologies for composi-

tional and physical identification, those related to

sample textural characterization play a very

important role as well. Variations in the quantity

and assortment of aggregate directly affect the

physical–mechanical properties of mortars and

plasters and therefore their behavior and durability.

When statistical data robustness is known, textural

information, together with mineralogical and chem-

ical details, represent a useful tool for the

discrimination and classification of sampled mate-

rials. Furthermore, such directions are of great

importance when compatible repair materials need

to be used.

It is widely recognized that the strict similarity

between mortars and sedimentary rocks requires the

adoption, for its characterization, of mineralogical–

petrographic techniques and principles belonging to

sedimentary petrography [1]. Particularly, sands and

sandstones are commonly described in terms of

grain-size distribution and composition. Grain-size

determination is normally achieved either through

sieving previously disaggregated material or by

microscopical observation of thin sections. Both

these techniques suffer from methodological limita-

tions; therefore, neither technique is always better

than the other; the choice between different

approaches depends upon each single case study.

The main limitations of the sieving technique

applied to ancient plasters and mortars are concerned

with:

– the limited amount of material available that

affects the accuracy and the statistical meaning

of the measurement;

– the presence of brittle and/or calcareous aggre-

gate that limit the use of grind and dissolution

techniques because of the big loss of informa-

tion [2].

Microscopical analysis of thin sections are faced

with:

– the problem that only two-dimensional (2D)

sections of different-shaped and randomly ori-

ented grains can be seen [2–5];

– the need to derive a weight distribution from the

frequency number distribution;

– the time-consuming and subjective analysis when

a manual point-counting technique is used;

– the poor accuracy and reproducibility when

comparative visual charts are used [6].

The introduction of automatic or semiautomatic

digital image analysis techniques contributed to over-

come some of these problems and partially solved

those related with tedious and biased investigations

[5,7–9]. Moreover, the use of statistical and unbiased

data plays an important role in stereological compu-

tations [10]. Nevertheless, problems due to sample

representative area, method accuracy and reliability

still remain. In the case of ancient plasters and

mortars, additional difficulties in automatic segmen-

tation arise when constituents of complex and various

nature are present [2,11–13].

A great number of studies have already pointed out

that a bias is introduced due to random sectioning of a

3D volume of spatial organized sample [2–5,14,15].

However, thin sections remain the most common,

cheapest and indispensable tool in petrographical

studies of building materials and can be used as a

source of additional quantitative textural information.

A fast, objective analysis permits examination of a

large number of grains so that a statistically mean-

ingful set of data is obtained to characterize the

sample. For these purposes, a critical principle setup

of an automated 2D image analysis system is

presented. Advantages and difficulties in applying

this technique to thin sections of plasters and mortars

are pointed out.

2. Materials and methods

2.1. Laboratory mortars

Before treating the data obtained from each ana-

lytical procedure, it is necessary to validate the method.

Fig. 1. Thin section of embedded loose quartz sand used as aggregate in experimental mortars; photos are taken in trasmitted light (left: crossed

nicols, right: parallel nicols).

F. Caro, A. Di Giulio / Materials Characterization 53 (2004) 243–257 245

The aim of this critical setup is to assure that measure-

ments effectively mirror the real features of the

samples. Hence, a validation procedure has been

adopted to point out possible relations between the

apparent features measured on the 2D surface of thin

sections and the textural properties of the sample.

Furthermore, when automatic textural analysis is

performed, bias introduced by the presence of hetero-

geneous aggregates must be taken into account.

Relations between quantitative results and conditions

of data acquisition will be also discussed.

For these purposes, laboratory mortars with prede-

termined textural features and composition were

prepared. A first set of mortars was made mixing a

pure quartz sand and a natural hydraulic lime; a second

set of mortars was made mixing the same hydraulic

lime and a blend of different aggregates of known

composition and ratio. In the following, results refer to

the analysis of mortars with the quartz aggregate.

This aggregate derives from the mechanical crush

of a generic Quartzite employed as a building row

Table 1

Aggregate wt.% fractions used in the laboratory mortars

�2U �1U 0U 1U 2U 3U 4U 5U

M2 5 90 5

M4 5 90 5

M5 5 90 5

M6 5 20 50 20 5

M7 5 20 50 20 5

M8 5 20 50 20 5

M9 5 20 50 20 5

material and sold in various grain-size classes. A

previous microscopical analysis showed a homoge-

nous collection of metamorphic quartz grains with

crushed shape and a very low content of impurity

(Fig. 1). These characteristics ensure constant and

compositional-free features under the light optical

microscope. Therefore, we can state that automatic

measurements are not affected by any compositional

(i.e., optical) variation of the objects.

Mechanical sieving is the most common method

adopted to measure the grain-size distribution of loose

sands and still remains a rapid, cheap and trouble-free

way to separate size fractions even for building

purposes [5,16,17].

A mechanical sieving was performed with an

automatic sieve shaker; the start sieve size and the

end sieve size were, respectively, �2U(4 mm) and

5U(0.032 mm). A sieve interval of 1U is chosen: no

pan fraction is considered. Sieving measures the width

of the minimum square aperture through which a grain

will pass. However, the following consideration

should be noted when interpreting the well-known

textural features of experimental mortars [5]: (1) not

all the particles retained by the sieve are really larger

then the sieve apertures; (2) grains can actually have

one dimension larger then the sieve aperture.

A number of aggregates different in grain size and

sorting was obtained by combining different amounts

of these eight grain classes; the used proportions are

reported in Table 1.

To produce an ordinary traditional mortar for

jointing or plastering, the aggregate is mixed with

Fig. 2. A series of settled mortars.


binder and water in different weight quantities

depending on the function of the material. The binder

used in this study was a commercial natural hydraulic

lime produced by St Astier (NHL 3.5) and sold as a

natural material for building restoration. Lime and

quartz particles are mixed with water until the mixture

reaches a good workability, described as sticky or

Table 2

Textural parameters of sieved aggregate and relative binder/aggregate rati

Sample Mean (mm) Median (mm) SU B/A ratio S

M2.0 0.7071 0.7071 0.3162 2 M

M2.1 0.7071 0.7071 0.3162 1 M

M2.2 0.7071 0.7071 0.3162 0.5 M

M2.3 0.7071 0.7071 0.3162 0.33 M

M2.4 0.7071 0.7071 0.3162 0.25 M

M3.0 0.3536 0.3536 0.3162 2 M

M3.1 0.3536 0.3536 0.3162 1 M

M3.2 0.3536 0.3536 0.3162 0.5 M

M3.3 0.3536 0.3536 0.3162 0.33 M

M3.4 0.3536 0.3536 0.3162 0.25 M

M5.0 0.0884 0.0884 0.3162 2 M

M5.1 0.0884 0.0884 0.3162 1 M

M5.2 0.0884 0.0884 0.3162 0.5 M

M5.3 0.0884 0.0884 0.3162 0.33 M

M5.4 0.0884 0.0884 0.3162 0.25 M

M

M

M

M

plastic status. The fresh mortars are worked with a

trowel and set into wooden grating until hardening

(Fig. 2).

Different binder/aggregate ratios were chosen to

reflect the manifold nature of mortars in a set ranging

from finishing coat to coarse jointing material [18].

The finest and best sorted aggregate can be compared

o of laboratory mortars

ample Mean (mm) Median (mm) SU B/A ratio

6.0 1.4142 1.4142 0.8944 2

6.1 1.4142 1.4142 0.8944 1

6.2 1.4142 1.4142 0.8944 0.5

6.4 1.4142 1.4142 0.8944 0.25

7.0 0.7071 0.7071 0.8944 2

7.1 0.7071 0.7071 0.8944 1

7.2 0.7071 0.7071 0.8944 0.5

7.3 0.7071 0.7071 0.8944 0.33

7.4 0.7071 0.7071 0.8944 0.25

8.0 0.3536 0.3536 0.8944 2

8.1 0.3536 0.3536 0.8944 1

8.2 0.3536 0.3536 0.8944 0.5

8.3 0.3536 0.3536 0.8944 0.33

8.4 0.3536 0.3536 0.8944 0.25

9.0 0.1768 0.1768 0.8944 2

9.1 0.1768 0.1768 0.8944 1

9.2 0.1768 0.1768 0.8944 0.5

9.3 0.1768 0.1768 0.8944 0.33

9.4 0.1768 0.1768 0.8944 0.25

Table 3

Graphic and moment formulae used in this study [4]

Name Graphic formula Moment formula

Mean MeU=(U16+U50+U84)/3 xm=P

i=1;nCimiU/100

Median MdU=U50 –

Sorting T.I.=(P25/P75)1/2 SU=

�Pi=1;nCi(MiU�xmU)

2/100�1/2

Skewness SK1=(U16+U84�2U50)/2(U84�U16 )+(U95+U5�2U50)/2(U95�U5) 3U=P

i=1;nCi(MiU�xmU)3/100SU

3

Kurtosis KG=(U95�U5)/2.44(U75�U25) 4U=P

i=1;nCi(MiU�xmU)4/100SU

4


to the one selected for a finishing coat; unsorted or

coarse aggregates are mixed together with lime and

used for rendering or jointing in mural works. The

complete set of laboratory mortars is shown in Table

2; their textural parameters are computed by sieving

weight percentages.

The quantification of aggregate textural parame-

ters employs statistical measures, grain-size distribu-

tion diagrams, and frequency and cumulative-

frequency probability plots [19]. Graphic techniques

are especially appropriate for analysis of an open

distribution whereas the moment methods should not

be used unless all the grain size present lies within

the defined grain-size limit [4]. Both graphical

methods and moment methods were employed in

this study, although the latter is needed for automatic

computation. Graphic measures use the formulae

devised by Folk and Ward in 1957 [20]. According

to their scheme, grain size, in phi units, is described

using the parameters of mean, median and standard

deviation, skewness and kurtosis; these properties are

determined graphically by reading selected percen-

tiles off the cumulative percent curve plotted on the

arithmetic probability paper (AAP). Conversely, the

moment is computed from the product of the weight

percentage in a given size class and the number of

class grades from the origin of the curve; the first,

second, third and fourth moments correspond to

mean, sorting, skewness and kurtosis, respectively

(Table 3). The computation of these parameters, both

Fig. 3. Examples of alizarin red

with graphical and moments methods, is based on

the assumption that the aggregate fits a normal

(Gaussian) distribution. In this paper, data refer to

the method of moment measures. An EXCEL

spreadsheet, compatible with the image analysis

software, was built allowing an instant and objective

computation of textural parameters; moreover, the

use of the modified GRAINPLOT.xls spreadsheet

application devised by Balsillie et al. [19] allows the

automatic plotting of the cumulative curve on the

AAP.

2.2. Thin sections preparation

Thin sections are cut perpendicularly to the setting

surface and affect the entire section of the settled

mortars.

The preparation of thin sections plays a very

important role especially if automated image analysis

is performed. Very steady conditions of image

acquisition are necessary and the appearance of the

discrete finite elements of the image must be as

constant as possible. The precision and relative

accuracy of the analysis also depend on the quality

of specimen preparation, the clarity of the grain

boundaries and the capability of keeping these

properties firm [21].

For an automatic image analysis, a very high

degree of grain boundary delineation is required.

For this purpose, techniques commonly used for

-S stained thin sections.

Table 4

Image details for different combinations of objectives and resolutions

Image resolution Image class Image format Pixel width with

�1.6 objective

Pixel width with

�4 objective

Image breadth

(mm)

1280�960 Color RGB 24 bit Bitmap TIFF 1 pixel=5.924 Am 1 pixel=2.439 Am 1.6�=7.6�5.7

4�=3.1�2.4

2048�1536 Color RGB 24 bit Bitmap TIFF 1 pixel=3.731 Am 1 pixel=1.533 Am 1.6�=7.6�5.7

4�=3.1�2.4


mineral identification and textural studies in thin

sections can be adopted. Both staining and colored

epoxy resin impregnation can be used. The latter is

an irreversible treatment and can hinder further

analyses on the same thin section. Furthermore, the

use of colored resins, because of their high

viscosity, does not ensure an optimal diffusion of

the adhesive through the microporosity of the

binder. To assure a good grain contrast suitable

for most of the samples of plasters and mortars, a

simple and common etching technique is used: the

uncovered thin section is immersed for 60–120 s in

a solution made with 1.2 g alizarin red-S in 100 ml

Fig. 4. Three channel intensity histogram of sample M7.4.

of 1.5% HCl [22]. A gentle wet manual polishing

with aluminium oxide powder is required to remove

surface imperfections and dye stagnations. The

staining procedure imparts a pink-to-red color to

the lime binder, thus providing the essential reso-

lution to the image (Fig. 3).

This procedure is routinely applied by petrographers

for the identification and the quantitative determination

of carbonate minerals in thin sections [23], and can also

be easily adopted in staining different varieties of lime.

Automatic discrimination between aggregate and

binder will take advantage of the reddish color given

by the alizarin red-S to the binder fraction.

After the staining treatment, the uncovered thin

section can be easily taken back to its original

appearance by gentle abrasion.

2.3. Point counting

A classical manual point-counting analysis was

performed using a polarizing microscope with a �10

objective. In sedimentary studies, direct measure-

ments in thin sections is widely used; point counting

is carrying out on thin sections allowing the con-

struction of frequency number and cumulative-fre-

quency number distribution curves.

Fig. 5. Geometrical meanings of grain-size measurements.


The size of the smaller and bigger axes of the grains

encountered at grid intersection is determined thanks

to a micrometric eyepiece; point counting is per-

formed with a fixed rate, depending on the grain size

of the aggregate. Next, apparent mean diameter is

used to build the grain-size distribution.

In this study, at least 100 grains were counted in

each thin section, providing a representative measure-

ment of textural parameters of the samples.

2.4. Image acquisition

The equipment used for image acquisition is very

simple. It consists of a Leitz Laborlux 12 POL S

polarizing microscope with a single 6-V, 25-W

tungsten–halogen lamp, equipped with an Olympus

Camedia C-4040Z Digital Camera controlled by a

computer. The compact digital camera has a 1/1.8 in.

CCD resolution of 4.1 Megapixel, which allows image

resolutions up to 2272�1704. A commercial image

analysis software (Image-Pro Plus) processed the

images.

The choice of magnification and image resolution

is of great importance and affects the precision and

the reliability of resulting parameters. Choosing a

working magnification operates as a first size

criterion. In fact, the frame has to be large enough

to observe the largest features and the size of the

pixel has to be small enough to detect the smallest

details; thus, the correct magnification will be the

result of a compromise [24]. The worse the sorting of

the aggregate is, the more troublesome the compro-

mise [12].

In this work, two fixed magnifications and focuses

were used for the whole size range. High magnifica-

tions will increase the accuracy of the measures,

decreasing the probability of a big grain to be

included in the frame; the use of a �1.6 objective

permits capturing images with a relatively big breadth

but decreases the pixel resolution (Table 4). At any

rate, there is always a lower limit for the size of an

object that can be accurately represented within the

pixel image. In this paper, data refer to images with a

resolution of 2048�1536 pixels, taken with a Leitz PL�1.6 objective.

A stage micrometer was used to determine the true

linear magnification for both the �1.6 and �4

objectives. The distortion of the field was evaluated

by determining the calibrations at each of the four

corners of the field and by comparing them with the

values at the center of the image field.

Up to six images per thin section were collected

under constant condition of illumination. For quanti-

fication of textural properties, conditions of acquis-

ition must be similar and as closely controlled as

possible. The minimum number of frames for each

thin section was determined so that the measurements

made in three successive increasing areas of measure-

ments do not change F10% relative to the next

greater area [25].

2.5. Image processing and analysis

Commercial image analysis software was used to

process and analyze the images captured by the

microscope. Images were stored and subsequently

treated by Image-Pro Plus version 4.5. This image

analysis software provides classical image processing

and analysis tools, and it needs to be adapted to the

specific case study. The goal of image processing is to

increase the image quality, to improve grain resolution

and to get evidence of specific characteristics. For the

purpose of the analysis, the grain boundaries are of

extreme importance because the reliability of measure-

ments depends on proper grain detection. Once the

aggregate elements are better discernable from the

binder, automatic histogram-based thresholding fol-

lowed by a derivative-based edge detection is per-

formed. To get considerable results, a strictly bimodal

grey tone intensity histogram is desirable (Fig. 4);

unfortunately, this condition is not always achievable

when aggregates with different nature are analyzed.

A detailed description of both the algorithms and

the image-processing procedure is outside the aim of

this paper, although some important considerations

concerning algorithms and analysis options need to be

noted. With the spread of commercial image analysis

software, each operator can develop a slightly differ-

ent arrangement of the algorithm to get the same

outcome. There is no unique and objective criteria for

algorithms’ reliability assessment and different grain-

edge detections provide a similar and useful segmen-

tation. Previous studies [26] proved that scatter is

visible in measurements made using various algo-

rithms, like observers who make slightly different

decisions. Thus, the importance of rigorous calibra-

Fig. 6. Grain-section-derived ellipsoid.


tion and standardization of each tool for image

quantification clearly emerges.

Once objects are defined, image analysis allows to

quantify information from images. The Image-Pro

Plus software provides quite a wide range of spatial

measurement options, each with a different geometrical

meaning. In this study, 15 different parameters were

computed for each grain but only four indices were

used to characterize aggregate percentages and grain-

size distribution: Area, defined as the area composed of

all pixels within the object perimeter; Feret max and

min, calculated by physically rotating the object’s

outline every few degrees and measuring respectively

the width of the biggest and the smallest rectangles

enclosing the object (by default it is 32 angles for Feret

Fig. 7. Computed mean for the d

measurement) and Feret mean, reporting the average

Feret length (Fig. 5). The use of Feret diameters is

legitimated by its robustness [27] and its similarity with

the geometrical meaning of sieve aperture.

All these operations are automated by writing a

macro in Image-Pro IPBasic language. Because of the

complex nature of the aggregates, some semiautomatic

operations are allowed during the running of the macro;

the operators can choose whether to interact or not with

the analysis. Measurements are automatically saved in

EXCEL spreadsheets where grain-size frequencies and

textural parameters are computed.

The quantities of aggregate fractions are measured

both in terms of number and volume percentage, thus

allowing a direct comparison between the two differ-

ent cumulative curves.

3. Results

The various types of mortars have been analyzed

by means of mechanical sieving, manual point

counting and digital image analysis. The combination

of these different analytical approaches produced a

wide range of data and allowed the computation of the

ifferent laboratory mortars.


following typical textural parameters: mean grain size,

median grain size, sorting of the aggregate and binder/

aggregate ratio. Grain-size parameters are computed

upon

– weight percentage of sieved particles;

– number percentage of manual point counted

particles;

– number percentage of automatic image analysis

measured particles from both 1280�960 and

2048�1536 images;

– volume percentage of automatic image analysis

measured particles from both 1280�960 and

2048�1536 images.

Direct comparison between different computations

allows to verify the reliability of the image analysis

and to calibrate the tool. To compare image analysis

data to sieve curves, some transformations are

needed. In the first place, number percentages need

Fig. 8. Examples of frequency curves of: very well sorted coarse and

moderately sorted coarse and fine aggregates (C, volume percentages; D,

to be transformed into volume percentages; in the

second place, if systematic errors exist, a correction

from thin section distributions to sieve distributions

is needed.

The conversion of number percentages into

volume percentages is founded on the assumption

that it is possible to estimate the mean thickness of

a particle if (1) grains are randomly sliced by the

thin section, and (2) particles from the same source

have more or less the same shape characteristics

[6]. Thus, the apparent volume of a grain i, Vai, is

computed as follows (Fig. 6):

Vai ¼4

3p

Feretmaxi

2

�Feretmeani

2

�Feretmini

2

�:

��

Thin section analysis allowed to measure from 200

up to 19,000 grains, depending on the aggregate grain

size. Statistical measures are based upon the max-

imum number of counted grains. The measured

fine aggregates (A, volume percentages; B, number percentages);

number percentages).


aggregate classes vary from very fine sand to very

coarse sand.

3.1. Mean grain size of the aggregate

Next, we will focus on the mean parameter

computed by the image analysis approach.

If we look at Fig. 7, it is clear that grain-size

parameters computed both with IA and point

counting mirror the original sieve data, although

with different trends, depending on the way the

cumulative percentages are computed. Data based

upon number percentages are obviously affected by

the sorting of the aggregate that amplifies the

Fig. 9. Relative percentage error in

differences between weight and number percentages

(Fig. 8).

As we can see in Fig. 9, the bias of the image

analysis measurement increases with the decreasing

of the grain size and the worsening of the sorting.

The greatest relative percentage error occurs for the

finest fractions of moderately sorted samples. This

trend differs from the one described by point

counting (Fig. 9) as the point-counting measurement

gives a better result in case of small grains both in

extremely well sorted and in moderately sorted

aggregates.

Such disagreement can be explained differently for

each of the two approaches: problems in edge detection

mean grain-size computation.

Fig. 10. Corners blurring causes difficulty in edge detection.

Fig. 11. Empirical correlation between thin section and sieve data; up: m


and small grain representation with the image analysis

procedure lead to an underestimation of small grains

[27] (Fig. 10) while point counting is deeply affected

by the number of counted grains, particularly in the

case of a poorly sorted aggregate; the higher magnifi-

cation employed in the point-counting analysis ensures

a more precise measurement of small grains.

The empirical correlation between sieve data and

image analysis data shows a linear trend with an

R2N.96 (Fig. 11). However, the equation that allows to

compare thin-section-derived parameters to sieve-

derived parameters does not explain the bias found

in the finest aggregates. A systematic deviation from

the ideal straight-line pattern, detected by both the

analyses, points out how textural parameters of

ean computed in millimeters; bottom: mean computed in phi units.

Fig. 12. Binder/aggregate ratio of different samples before and after the automatic correction.

Fig. 13. Computed sorting r of laboratory mortars.


Fig. 14. Examples of (A) bghostQ grain, (B) lost boundary and (C

bghostQ boundary.


laboratory mortars really differ from the expected

ones.

3.2. Binder/aggregate ratio

With Image-Pro Plus, the binder/aggregate ratio is

calculated as the ratio between the undetected area of

the image and the sum of the areas of the measured

objects. In point counting, the same index is computed

as the ratio between the number of the grid intersection

with the binder and the number of counted grains. Both

parameters show a direct and positive correlation with

the grain size of the aggregate (Fig. 12).

As for the grain-size parameters, in the case where

the water contents are supposed to be similar during the

mixing of the different mortars, the determination of a

robust coefficient of correlation allows the automatic

conversion of 2D binder/aggregate (Fig. 12).

3.3. Sorting of the aggregate

The sorting of the aggregate (r) is computed as the

standard deviation of the grain-size distribution

according to the following equation:

r ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPf ðmU � vvÞ2

100

s

where v is the mean, f is the frequency in volume

percent and mU is the midpoint of the class intervals

[17]. This calculation of the dispersion of the

frequency curve is faster and superior to the

equivalent graphical methods, but it is more sensitive

to variations around and far from the central

tendency of the curve. Thus, small variations in the

distribution of frequencies in volume percent result

in visible shifts from the reference values (Fig. 13).

This effect is more pronounced in the case of very

well sorted laboratory aggregates [28] since (a) the

sorting of the laboratory aggregate is the most

difficult parameter to be monitored; small grains

can be present in the finer fraction of the aggregate

affecting the final estimation of the sorting parameter

(this is confirmed by the similar trend of both the

image analysis and the point-counting data); (b)

noise and difficulties in grain detection affect the

sensible computation of this parameter; (c) the

random sectioning through grains shows only vari-

ous apparent diameters different from the expected

ones, thus increasing the relative standard deviation.

The use of volume percentages reduces the strength

of this effect but does not remove it; with the

worsening of the sorting, results from thin section

analysis and sieve analysis will converge [28].

However, point counting and image analysis show

similar results and are totally comparable.

4. Discussion and conclusions

The automatic image analysis method developed

in this study allowed the discrimination of the

aggregate of experimental mortars and the computa-

tion of its textural parameters. The preliminary test

for comparison with different analytical approaches

showed (1) the existing relations between textural

parameters and the precision and reliability of the

automatic technique; (2) the limitations and the lacks

of the procedure that affect the measurements. Thus,

when comparison between different data is needed,

the calibration of each method seems to be of great

importance.

Difficulties in applying the technique are mainly

caused by

– proper thresholding and tracing of grain edges

when quality of image declines (Fig. 14);

)


– inaccurate grain detection when too many adjacent

grains are present (i.e., for very low binder/

aggregate ratio);

– pixel resolution when measurements of unsorted

aggregate are performed on a single image.

In the case of low magnification (�1.6 objective)

and image resolution of 2048�1536, the pixel width

equals to 3.7 Am. Because of algorithm characteristics

and image defects, the grain boundaries can be

rounded up or down by at most one pixel. Therefore,

if we fix a percentage error limit of 10% [5], particles

whose size are less than 20 pixel (75 Am) are not

expected to be measured accurately.

The comparison of different computed textural

parameters shows that (1) in the case of coarse

aggregates, both in image analysis and point-counting

analysis, bias concerns the scale of the images; (2) in

the case of fine aggregates, bias concerns image

resolution and grain detection, thus resulting in a

general overestimation of grain size; this trend is more

pronounced in mortars with small grains and low

binder/aggregate ratio.

The detection of adjacent grains is more problem-

atic in the case of blurred images of fine fractions. The

blurring effect is intensified at the corners of the

image and is caused by an intrinsic lack in the image

acquisition. When a large number of grains is present

in a single image, it is possible to select a central area

of interest (AOI) not affected by noise; counting will

be performed on this area if a representative number

of grain will be attained.

From the above, it becomes apparent that a fully

automated procedure with no interaction from the ope-

rator is not always possible; the more manifold is the

nature of the aggregate, the more indispensable is the

interaction. Nevertheless, if the interaction is limited

and guided by interactive windows during the analysis,

results remain in any case objective and repeatable.

The use of etched thin sections can give consistent

results in textural analysis of mortars and plasters with

aggregates ranging from fine to very coarse sands.

Results are similar to those obtained by classical point

counting but they are more objective, repeatable and

extremely quicker then manual computation. Con-

version from point to volume percentage allows the

comparison of thin section data to those derived by

mechanical sieving.

Automatic corrections are needed to adjust the

systematic errors due to the image processing and

analysis. However, the suggested equations are valid

for this algorithm sequence only. Thus, it is of great

importance for each user to adopt a strong procedure of

calibration before any routine grain-size assessment.

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reliability of textural analysis of ancient plasters and mortars through automated image analysis

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