Nuclear safety-related concrete structures are composed of several constituents that provide for multiple functions; i.e.
, load-carrying capacity, radiation shielding, and leak tightness. A comprehensive evaluation of potential aging-related degradation mechanisms that might impair the concrete containment durability revealed the significance of the alkali-silica reaction [1
]. Though this degradation mechanism is well documented for bridges and pavements in particular [4
], its potential consequences on the structural integrity of the containment need to be assessed. This is crucial for potential license renewal beyond the original nuclear plant licensing term. The risk of alkali-silica reaction should be evaluated for new plants at the mix design stage so as to minimize the consequences by the proper material selection. Current generation Portland cements have increased alkali contents [7
] that may result in higher content of alkalis in pore water and have a stronger influence on the reactivity of aggregates that were not reactive in the past, and in many countries the availability of good-quality aggregate materials is becoming limited.
Alkali-silica reaction (ASR) is commonly understood as chemical reaction in concrete between hydroxyl ions (OH−
) of sodium and potassium present in pore solution and certain siliceous rocks and minerals present in some aggregates [8
]. The development of the alkali-silica gel reaction product can, under certain circumstances, lead to significant swelling and cracking of concrete. It is well established that limiting the availability of reactive silica minerals present in some aggregates and the alkalis coming from Portland cement is an effective means of preventing damage due to ASR. Thus, selecting “non-reactive” aggregates has become one of the major goals during mix design for durable concrete.
Different approaches, including petrographic study, chemical tests, dilatometric evaluation, and accelerated and long-term laboratory tests are used to evaluate the ASR potential of aggregates [10
]. Since the early work by Stanton [15
], numerous investigations have been conducted in order to design a quick and reliable method to establish the susceptibility of some rocks to developing alkali-aggregate reactions [16
]. During the work of the RILEM Technical Committee 219-ACS (Alkali Aggregate Reaction in concrete structures: performance testing and appraisal), it has been stated that sedimentary and deformed metamorphic rocks are those that more commonly, but not exclusively, exhibit signs of ASR when used in concrete [12
]. Different types of rocks, implying different mineral compositions, microstructural textures, and provenances, exhibit reactive components that are characteristic of each type or group of analysed aggregates were presented in earlier publication [17
]. The most reactive forms of reactive minerals in aggregates are strained quartz, amorphous silica, cryptocrystalline quartz, chalcedony, and chert. They may cause deterioration of concrete when the reactive components are present in amounts as small as 1%. In general, aggregates containing crystalline silica are stable, and those with amorphous or very fine-grained silica are reactive. Less reactive forms of silica can eventually cause concrete deterioration after 15 or even 25 years after construction. So, the primary factors influencing alkali-silica reactions include the aggregate reactivity, namely the amount and grain size of reactive SiO2
The common features of each rock type used in concrete were explained in the context of ASR in [12
] and [17
]. However, the list of the main types of aggregates didn’t contain heavy aggregate or main minerals present in aggregates that can be used for radiation-shielding concrete. These include aggregates that contain or consist predominately of materials such as barite, magnetite, hematite, ilmenite, and serpentine [19
]. Since nuclear shielding structures are classified as high-risk structures [8
], and the time scale involved reaches 100 years of service (including the plant decommissioning period) there is a need for more advanced tools for ASR risk evaluation, which was just started in [21
Recent observations of premature deterioration of nuclear shielding concrete structures in Seabrook Power Plant (US, NH), including electrical tunnel, containment enclosure building, control building, and tank farm area have suggested the development of the ASR [22
]. The performed petrographic examinations confirmed that ASR was the main reason for concrete degradation [23
]. Also, the expansion produced by ASR has been observed in the turbine generator foundation of unit 1, Ikata nuclear power station, Japan [24
]. Previous ASR studies on radiation shielding concrete [25
] showed that crystalline quartz (or α
-quartz with a specific gravity of about 2.65), due to irradiation, is converted to distorted amorphous quartz with a specific gravity of 2.27. The decrease of the resistance to nuclear radiation with increasing the SiO2
content in aggregates strongly indicates that the deterioration is due to the acceleration of alkali-silica reaction of aggregates in concrete by nuclear radiation [26
The objective of this investigation is to develop a computer-aided microscopic method of identification of the mineralogical composition of high-density mineral aggregates on thin sections. It is assumed that an application of digital image analysis of minerals on thin sections can largely contribute to improved recognition of reactive components of such aggregates. Thin section analysis is recommended as a routine part of the petrographic examination of aggregates [12
], usually performed as the essential first stage in the classification of the alkali-reactivity potential of concrete aggregates. The use of image analysis is currently limited to rock types with grain sizes resoluble under the optical microscope and to quartz grains without a high degree of strain [11
]. The advantage of image analysis in comparison to the traditional point-counting method can be the speed. The point-counting method proposed by Wigum [28
] would take several hours to assess ~200 quartz grains. With the image analysis method used in [11
], the assessment of up to 27,606 quartz grains was possible during 10 to 120 minutes. It was found that petrographic image analysis allows the assessment of a higher number of quartz grains in a shorter time without compromising the precision of the results.
3. Image Analysis Procedure
The analyzed heavy aggregates, magnetite and hematite, consist mostly of ferrum oxides, which are opaque on thin section in transmitted light, so the identification and analysis of minerals was performed in XPL with gypsum plate. A digital image of a whole thin section is shown in Figure 3
, which illustrates a multicomponent image consisting of aggregate grains and surrounding epoxy resin. The separate images (1.5 mm × 2 mm) were automatically acquired and assembled into one image (20 mm × 40 mm), which was then subjected to a process of further analysis to estimate the total content of quartz grains.
To assess the total content of the SiO2 crystals in aggregates, it was necessary to distinguish different “phases” (this term is used in the image analysis software) in the assembled image. They correspond to the particular minerals in the aggregate specimen and the surrounding epoxy resin. The most efficient method in the case of ferrous aggregate containing grains of quartz is to define three “phases” on the image: the ferrum oxides, the epoxy resin, and the quartz. The objective is to subtract the area of the first two phases from the whole image area to calculate the total content of the quartz. The consecutive detailed grain size analysis is then performed only on the quartz phase. This is guaranteed by the proper selection of the ROIs (regions of interest) on the images containing only the quartz grains and perchance a thin margin of surrounding ferrum oxide phase which does not affect the results.
The procedure of defining the ferrum oxide and the epoxy resin phases in the analySIS software comes down to pointing the selected characteristic small area of the image with the cursor. Then the values of the Red, Green, and Blue components are attributed to the pointed area. The attributed RGB values can be further corrected by including or excluding another areas of the image to obtain more accurate selection of the phase. For the image presented in Figure 3
b, the levels of RGB values were set to R
: 166–255, G
: 38–255, B
: 0–226 for fluorescent resin selection, and to R
: 0–76, G
: 0–21, B
: 0–39 for ferrum oxides selection. For better control of the selection process, different colors were attributed to each phase so that it is possible to instantly see the results of the process and correct it. The selection results are presented in Figure 4
. After selection of ferrum oxide and epoxy resin phases, analySIS software calculates the area and the percentage of them which subtracted from the whole area of the image gives the total content of quartz.
The distribution of quartz grain size was evaluated using the size categories. It is known that aggregates containing quartz with a small grain size will be more prone to develop deleterious ASR due to an increase in available surface, provided that the increased surface is sufficiently accessible for the pore solution [11
]. When the petrographic texture of a rock is studied, the term microcrystalline is applied to a texture so fine-grained that a petrographic microscope is needed to resolve individual crystals (4–62 μm). The term cryptocrystalline is applied when the texture is too small to be resolvable under petrographic microscope (<4 μm). The threshold applied to rocks with micro and cryptocrystalline quartz texture is also different in each country [31
]. So, the classes of the quartz size were established according to the Norwegian Criteria, as shown in the research of Alaejos and Lanza [32
]. The reactive forms of quartz have been classified depending on the crystal size as innocuous quartz: >130 μm; doubtful quartz: 60–130 μm; reactive quartz: 10–60 μm and highly reactive quartz: <10 μm.
To identify and assign quartz grains to proper classes, the high quality separated images of size 1.5 mm × 2.0 mm were used. Three filter operations of analySIS software were used to prepare the pictures for image analysis. They were in sequence: Remove Noise filter, Average filter, and Posterize filter. The first was used (with parameter value 32 from parameter range 1–128) to remove very small aggregations of pixels inside a single quartz grain area that could be falsely recognized as a separate grain of a very small size. The second filter was used (with parameter value 32 from parameter range 1–128) to decrease a variability of colors within each grain of quartz. And finally the third filter was used (with parameter value 2 from parameter range 1–64) to assign a single color (the closest from the color range) to each distinguished quartz grain. The example of the original image and the result of its filtering transformations is presented in Figure 5
To illustrate the effects of filtering operations, the images were first converted from true-color images into grey-value images. In Figure 6
the computed histogram of intensity values over the selected area of quartz phase (presented in Figure 5
) is shown. The histograms are based on the grey-value image intensity. Vertical axis refers to the number of pixels of the given intensity value. After filtering operations, the grey level variability is reduced to the narrow range around two intensity levels. In the Figure 7
, results of the filtering procedures are presented differently, as a grey values profile computed for selected horizontal line marked in Figure 5
. This time, the vertical axis refers to the intensity of grey value of pixels along the line. Comparing Figure 7
a,b, a significant decrease of the initial diversity of the grey value intensity can be noticed. Flat, horizontal segments of the graph in Figure 7
b can be identified as a size of the single grain of quartz measured along the selected line. The segments of intensity value equal to 0 represent the ferrum oxide phase.
Further analysis was performed on the processed images to classify the quartz grains according to their mean diameter. The new set of “phases” was defined in accordance with the colors of quartz grains obtained after the filtering operations. On the basis of the so-distinguished “phases”, the Detect command of the analySIS software was executed. As a result, the number and the equivalent mean diameter of separated quartz grains was obtained so as to allow grain size classification.