# Evaluating the Influence of Carbon Fiber on the Mechanical Characteristics and Electrical Conductivity of Roller-Compacted Concrete Containing Waste Ceramic Aggregates Exposed to Freeze-Thaw Cycling

Authors: Danial Nasr; Rezvan Babagoli; Mohsen Rezaei; Arsalan Andarz
Journal: Advances in Materials Science and Engineering
 (2023)

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## Abstract

Traditional methods of removing snow and ice from pavements using chemicals are combined with mechanical removal that involves a lot of manpower, advanced machinery, chemicals that are harmful to the environment, and damage to pavements. Furthermore, annually, large quantities of ceramic materials become waste due to their fragile nature during processing, transport, and installation, and their accumulation in the nature has brought about environmental and health-related concerns. Therefore, the study aims to investigate the effect of using waste ceramic as a replacement for fine aggregate in roller compacted concrete (RCC) and the application of carbon fiber to improve the mechanical properties and electrical conductivity of RCC. To achieve this goal, several tests such as compressive strength, indirect tensile strength, electrical resistance, chloride ion penetration, specific gravity, and skid resistance tests were carried out on the fabricated samples before and after freeze-thaw cycling exposure. The experimental results illustrated that replacing waste ceramics with fine-grained aggregate increased the compressive strength and tensile strength of RCC. Furthermore, carbon fiber increased tensile strength but had no noticeable influence on compressive strength. Freeze-thaw conditioning led to a reduction in the compressive and tensile strength regardless of the aggregate type and carbon fiber utilization. In the samples containing waste ceramic aggregate, the electrical conductivity was reduced, and by adding carbon fiber, its electrical conductivity was increased. Exposure to freeze-thaw cycling resulted in an increase in electrical resistance and the passing charge. Waste ceramic incorporation created a similar mixture in terms of skid resistance, while in contrast, the carbon fiber slightly reduced the skid resistance. In addition, freeze-thaw conditioning resulted in an increase in the skid resistance. Besides, in this study, kernelized support vector regression (KSVR) and radial bias function (RBF) neural network models were proposed to estimate the indirect tensile strength (ITS) and compressive strength (CS) values. The results showed that both models have high performance in estimating these values, but RBF was a more efficient model.


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## Body

## 1. Introduction

Roller compacted concrete (RCC) is a zero-slump concrete made up of the same components as regular concrete but in different proportions. However, its building qualities differ significantly from those of traditional concrete [1]. In comparison to asphalt concrete mixtures, roller compacted concrete (RCC) pavements’ long-term durability has drawn a lot of attention because of their resistance to higher temperatures, low water absorption, and enough compressive strength, which leads to reduced deformation under traffic loading.Snow and ice on the cement concrete pavement of the airport have a significant impact on the safety of landing, moving, and taking off of the plane in winter, because snow and ice reduce the friction coefficient between the tire and the surface of the airport pavement, which not only hinders the transportation of people and become goods, but they also threaten people’s lives and property. Traditional methods of removing snow and ice from pavements using chemicals are combined with mechanical removal that involves a lot of manpower, advanced machinery, chemicals that are harmful to the environment, and damage to the pavement. The use of chemicals has negative effects on the environment. Due to economic, safety, and environmental concerns regarding the use of chemicals, numerous research studies have been conducted to find sustainable and environmentally friendly methods for safely removing ice and snow from pavements [2]. For this reason, another important outcome of this research is to try to make roller concrete that prevents the road from freezing and slipping.Additionally, despite potential damage, concrete pavement in cold climates is resistant to frost cycles [3, 4]. RCC can be mixed in batch-type or continuous flow mixers, and it can be laid out on concrete pavers or asphalt pavers (with or without modifications) (without needle vibrators) [5]. RCC must be both dry enough to support the weight of the vibratory machinery and wet enough to allow for appropriate distribution of the paste binder during mixing and vibration in order to achieve successful compaction [6]. Because of its stiffness, the RCC must be compacted using large vibratory steel drums and rubber tire rollers [1]. In order to maximize the compressive strength of RCC, a suitable compression ratio is required [7]. The compaction quality of the RCC material is affected by a wide range of compaction parameters (including compaction pass, roller velocity, vibration frequency, and compacted thickness), making the vibrating compaction control complex [8]. The American Society of Testing and Materials (ASTM) has standardized two alternative compaction procedures for use in the laboratory preparation of RCC specimens. These two approaches make use of two distinct pieces of equipment: a vibrating table and a vibrating hammer [9].The carbon fiber (CF) has been used extensively in concrete building projects to enhance its mechanical properties [10, 11]. According to [12], steel, asbestos, glass, metallic glass ribbons, polymeric, carbon, natural fibers, and textile reinforcements were employed to reinforce the cement matrix [13, 14]. Carbon fibers have specific advantages over other types of fibers utilized in cement-based composites [12]. Carbon fibers (CF) are becoming increasingly popular due to their exceptional mechanical qualities and high lightweight nature [15]. Carbon fiber improves the flexural strength, toughness, and tensile strength of concrete. Furthermore, the usage of carbon fiber improves the impact resistance and cracking resistance of concrete [16].In contrast to glass and polymer fibers, CF is electrically conductive. Furthermore, the lightness and flexibility of CF make it ideal for constructing electrically conductive cement matrices [15]. The electrically conductive concrete (ECON) compounds are cement, aggregates, water, electrically conductive additives (ECA), and other compounds [17]. Unlike ordinary cementitious composites, where electricity is mostly transferred through electrolytic conduction, the principal conduction mechanism in ECON is electron motion rather than ion motion [2]. Normally, conductive phases, such as conductive particles or conductive fibers, are injected into the cement matrix to make conductive concrete [18]. Concrete must have a certain amount of electrically conductive components, such as steel shavings, steel fibers, graphite products, carbon fibers, and nickel particles, in order to achieve high electrical conductivity [19]. Carbon fibers can be used in concrete along with steel fibers [20]. One of the most important uses of conductive concrete is in airport runways to melt ice and snow at pavement surfaces [21].Most ceramic products are made of natural materials, which contain large amounts of clay. Ceramic products are used in most buildings [22]. The use of ceramic materials in the form of tiles, bathrooms, electrical insulation, etc. is increasing year by year. Large quantities of ceramic materials become waste due to their fragile nature during processing, transport, and installation. As a result, using these wastes in concrete manufacturing can be an efficient way to lessen the environmental effects of these elements’ accumulation in nature while also improving the qualities of concrete [23, 24].Sassani et al. investigated carbon fiber-based electrical concrete as an alternative to the desalination of road pavement. The use of carbon fiber-based electrically conductive concrete (ECON) in hot paving systems (HPS) as an alternative to existing methods was studied in this study. The findings indicated that the investigated technique will be considerably successful in melting ice and snow on pavement surfaces [17].The mechanical, electrical, and sensitivity properties of cement mortar incorporating short carbon fibers were examined by Donnini et al. The purpose of this study was to look into the mechanical, electrical, and durability properties of cement mortar after adding varying percentages of short-length carbon fibers (2%, 3%, and 4% by weight of cement). Carbon fiber reinforced mortar was subjected to a flexural strength test (CFRM). The addition of carbon fibers demonstrated that increasing the amount of fibers increased the flexural strength of the mortar. Furthermore, the electrical resistance of carbon fiber-reinforced mortars was tested on different processing days by measuring impedance with alternating current and two networks of stainless steel wire as electrodes. The electrical resistance dropped with time and eventually became constant [15].Bommisetty et al. investigated the impact of using ceramic tile waste as an aggregate in concrete. Crushed ceramic tile scraps with ratios of 0%, 5%, 10%, 15%, 20%, and 25% were employed as substitutes for natural coarse aggregates in this study. After examining the results, it was discovered that the optimal proportion of ceramic tile waste in concrete (with a water-to-cement ratio of 0.5) was around 20%. The findings revealed that using ceramic tile waste strengthens the characteristics of concrete [23].The influence of reinforcement of the carbon fiber mechanism on the mechanical and electrical properties of cement-based composites was examined by Han et al. To evaluate the reinforcing impact of carbon fibers, the mechanical and electrical properties of cement mortar containing carbon fibers were tested in this study. Scanning electron microscopy (SEM) and theoretical calculations were employed to investigate the mechanism of the carbon fibers’ effect on cement mortar at the microscopic scale. The testing results revealed that the inclusion of carbon fiber in the cement mortar boosted its mechanical strength while decreasing its electrical resistance. SEM results revealed that carbon fibers improved the mechanical characteristics of cement mortar by overcoming the extrusion force and reducing the formation of tiny cracks due to energy absorption [25].The purpose of the study was to look into the effect of carbon fiber on the mechanical and long-term properties of roller compacted concrete with waste ceramic fine-grained aggregates before and after freeze-thaw exposure. As a result, various mix design series combining waste ceramic, aggregate, and carbon fiber (1 to 3% carbon fiber by total weight of cement) were created. The materials’ mechanical qualities were assessed using compressive strength, indirect splitting tensile strength, and skid resistance tests. Several tests were performed to determine the longevity of the mix designs, including electrical resistance, fast chloride permeability, and specific gravity testing.


## 2. Research Method

### 2.1. Materials

Materials used in this research included cement, waste ceramic aggregates, carbon fiber (10 mm long), natural sand and gravel, and water. Type II Portland cement from the Ardestan cement factory was used, whose physical and chemical characteristics are stated in Tables1 and 2, respectively.Table 1
Physical properties of cement.
Setting time (min)Initial95Final150Fineness (gr/cm2)3000Shrinkage percentage0.14Compressive strength (MPa)3 days>177 days>2728 days>37Table 2
Chemical components of cement are used.
Chemical compoundsAvailable percentageSiO221AL2O35Fe2O33.8CaO65MgO1.8SO31.6K2O0.49Na2O0.25CL0.019Insoluble residue0.45L.O.I1L.S.F92C3A6.5CaOfree1.2Carbon fiber with a length of 10 mm (Figure1) was used as a substitute for natural aggregate left on the # 4 sieve whose physical characteristics are given in Table 3.Figure 1
The carbon fibers used in this study.Table 3
Physical properties of carbon fiber.
Length of carbon fiber (mm)10Density (gr/cm3)1.5Tensile strength (MPa)4900Tensile modulus (GPa)230In this research, coarse grain aggregate was prepared from the Lashotor stone mine in Isfahan. The maximum size of the aggregate was 12.5 mm, and its physical characteristics are stated in Table4. Natural sand was prepared from the Lashotor stone mine in Isfahan, whose physical characteristics are shown in Table 4. Also, waste ceramic was used as an alternative to natural aggregates passing through the 4# sieve. The prepared crushed waste ceramic was crushed and kept in closed spaces. The physical characteristics of the crushed waste ceramics are displayed in Table 4. The aggregate gradation (Table 5) was selected in accordance with ASTM C33.Table 4
Aggregate physical properties.
CharacteristicsStandardNatural coarse-grained aggregateNatural fine-grained aggregateWaste ceramic fine-grainedaggregateNominal maximum aggregate sizeASTM-C 13612.54.754.75Los Angeles abrasion (%)ASTM-C 13117.2——Bulk density (gr/cm3)ASTM-C 1272.682.712.13SSD density (gr/cm3)ASTM-C 1272.7162.732.15Water absorption (%)ASTM-C 1270.530.581.30Clay (%)ASTM-C 1420.010.017—Table 5
The aggregate gradation used in this research.
Sieve size (mm)Passing percentPassing percent limit25.41001001910083–10012.49072–939.58066–854.7558.851–692.384938–561.23728–460.62618–360.31211–270.1576–180.07522–8


### 2.2. Mix Design Preparation

In this research, six mixing designs have been used in which carbon fibers have replaced the natural materials left on the # 4 sieve, and waste ceramics have replaced the natural materials passing through the # 4 sieve. The mix designs are shown in Table6.Table 6
Sample mixing scheme.
Sample namesCement (kg/m3)Carbon fiber (% of cement volume)Natural coarse aggregate (kg/m3)Natural fine aggregate (kg/m3)Waste ceramic fine aggregate (kg/m3)w/cWater (kg/m3)N-CF03000841120000.38114WC-CF0300084109430.38114N-CF13001841120000.38114WC-CF1300184109430.38114N-CF33003841120000.38114WC-CF3300384109430.38114Cylindrical molds with a diameter of 15 cm and a height of 30 cm (for tests of compressive strength and tensile strength) and cylindrical molds with a diameter of 10 cm and a height of 20 cm (for testing electrical conductivity and rapid chloride permeability) were used. For compaction of samples, the Vebe device was used with a surcharge of 22.7 kg. After removing from the molds, the specimens were placed in the water pool for 28 days to complete the curing period.


### 2.3. Tests

#### 2.3.1. Workability Test

In order to analyze the influence of different characteristics such as waste ceramic, aggregate, and fiber content, the workability of RCCP mixes in the plastic state was assessed using a modified Vebe test in line with ASTM C1170. Vebe time is defined as the time it takes for a full ring of concrete mortar to develop around the Vebe molding during compaction. A surcharge of 22.7 kg was employed in this investigation to vibrate the RCCP specimens.


#### 2.3.2. Compressive Strength Test

Mechanical compressive strength tests were performed till failure on 30 × 15 cm cylindrical specimens according to ASTM C109/C109 M [37] after curing for 28 days. As a result, the relative compressive strength values for each mix design were determined using triplicate samples of the target temperatures. The average compressive strength of the three samples was determined and compared to the control sample values’ average compressive strength.


#### 2.3.3. Indirect Tensile Strength Test

The IDT test instrument was applied to assess the ITS of RCC samples according to the AASHTO T 283–03 [4] test procedure. For this test, a uniform compressive force of 50 mm per minute was applied until failure. At least four replicate samples were evaluated for each of the mixed design categories. In this test method, after applying a vertical compressive force, the tensile tension arises in the horizontal direction and the indirect tensile stress (psi) can be calculated using the following equation:(1)ITS=2PπDt,where P, D, and t represent the peak load in Newtons, the diameter in centimeters, and the thickness in centimeters, respectively. The study was conducted using the observed average diameters and thicknesses. No adjustments were made to the readings acquired from the test instrument.


#### 2.3.4. Electrical Resistance Test

In this research, the Wenner method (four-point) was utilized to obtain the electrical resistance of 10 × 20 cylindrical specimens cured for 28 days. To perform the experiment, a Reisman device was used which has four electrodes that are in a straight line and their distance from each other is the same. The two internal electrodes of the machine calculate the difference in the electrical potential created, and the two external electrodes introduce alternating current to the concrete surface.


#### 2.3.5. Rapid Chloride Permeability Test (RCPT)

After 28 days of water curing, two 5-cm thick specimens were cut from a 10 cm (diameter) by 20 cm (height) cylinder for each combination. A water-cooled diamond saw was used to cut each specimen from the cylinder’s central part. A belt sander was used to smooth down the rough edges and notches, resulting in a smooth surface on both sides of the specimen. Following vacuum conditioning, two specimens of each combination were placed between two containers containing solutions of 3% sodium chloride (NaCl) and 1.2% sodium hydroxide (NaOH) (Table7). Following that, 60 volts of electricity were applied for 6 hours in accordance with the ASTM C 1202–91 standard, and the results for each blend were averaged.Table 7
The usage rate of solutions.
NaOHNaClSample size (cm)Caustic soda (%)Water (%)Salt (%)Water (%)10 (diameter) × 5 (height)1.298.8397


#### 2.3.6. Bulk Specific Gravity Test

Because of the difference in specific gravity between ceramics, carbon fibers, and natural aggregates, the specific gravity of the specimens was calculated using the ASTM C 642 standard. After measuring the dry weight of the samples, the weight of water in each sample was then determined using the Archimedes scale, and the specific gravity of the samples was estimated.


#### 2.3.7. Skid Resistance Test

The skid resistance test was carried out in accordance with ASTM E 303–93 [26]. Skid resistance was determined using pendulum friction testing equipment. The pendulum and pointer were released from the horizontal position, and the pendulum arm was measured on its return swing. The scale’s pointer location was recorded (pendulum test value).


#### 2.3.8. Freeze Thaw Exposure

RCC cylindrical samples of 150 × 300 mm were submitted to quick freeze-thaw testing in accordance with ASTM C666 procedure B for freeze-thaw resistance [27]. The samples were placed in a freeze-thaw chamber configured to freeze in the air at −18°C for 1 hr and thaw in water at +4°C for 1 hr. All RCC samples were treated to a total of 300 freeze-thaw cycles. At 150 and 300 cycles, the compressive strength, indirect tensile strength, electrical resistance, rapid chloride permeability, specific gravity, and pendulum tests were performed.


### 2.4. Background of Prediction Methods

#### 2.4.1. Kernelized Support Vector Regression

The support vector regression (SVR) model is a supervised learning method which is applied to predict discrete values. This method uses the same principle of the support vector machines (SVMs) and is only used for the prediction of linear data. Kernelized support vector regression (KSVR) is an extension of the SVR model that uses some kernel functions [19]. This model can be used for nonlinear data [28]. The kernel function transfers the data to a higher dimension space for allowing linear separation by equation (2) [29]:(2)predictx=∑i=1Nαi−αi∗Kxi,x−b,in which x is the model input, K is the kernel function, b is a bias b∈R, and αi and αi∗ are Lagrangian multipliers that fulfill the equality αiαi∗=0,αiandαi∗≥0.Some of the most important of the kernel functions are polynomials and Gaussian that are obtained by equations (3) and (4), respectively.(3)Kxi,xj=xi.xj+1d,(4)Kxi,xj=exp−xi−xj22r2,where r is the width of the kernel function and d is an integer value greater than one.


#### 2.4.2. Radial Basis Function Neural Networks

Radial-bias function (RBF) neural networks have significant advantages in comparison with back propagation neural networks, including the ownership of a simple structure, fast learning algorithms, and no local minimum problems [30, 31]. A three-layer feed-forward design is typical of RBFS: an input layer, a hidden layer, and an output layer. The hidden layer nodes are centered at specific positions with a given radius, and the distance between the input vectors and their centers is calculated [16].Usually, the Gaussian functionφ is applied as a transfer function of the RBF model that is calculated by the following equation [2]:(5)φjx=exp−x−cj2rj2,where cj and rj are the center and width of the jth RBF node, respectively. The output of the RBF ANN ym is calculated by the equation as follows:(6)ym=∑i=0nφjwj.Here,wj is the weight value between the output layer node and the hidden layer node.


## 2.1. Materials

Materials used in this research included cement, waste ceramic aggregates, carbon fiber (10 mm long), natural sand and gravel, and water. Type II Portland cement from the Ardestan cement factory was used, whose physical and chemical characteristics are stated in Tables1 and 2, respectively.Table 1
Physical properties of cement.
Setting time (min)Initial95Final150Fineness (gr/cm2)3000Shrinkage percentage0.14Compressive strength (MPa)3 days>177 days>2728 days>37Table 2
Chemical components of cement are used.
Chemical compoundsAvailable percentageSiO221AL2O35Fe2O33.8CaO65MgO1.8SO31.6K2O0.49Na2O0.25CL0.019Insoluble residue0.45L.O.I1L.S.F92C3A6.5CaOfree1.2Carbon fiber with a length of 10 mm (Figure1) was used as a substitute for natural aggregate left on the # 4 sieve whose physical characteristics are given in Table 3.Figure 1
The carbon fibers used in this study.Table 3
Physical properties of carbon fiber.
Length of carbon fiber (mm)10Density (gr/cm3)1.5Tensile strength (MPa)4900Tensile modulus (GPa)230In this research, coarse grain aggregate was prepared from the Lashotor stone mine in Isfahan. The maximum size of the aggregate was 12.5 mm, and its physical characteristics are stated in Table4. Natural sand was prepared from the Lashotor stone mine in Isfahan, whose physical characteristics are shown in Table 4. Also, waste ceramic was used as an alternative to natural aggregates passing through the 4# sieve. The prepared crushed waste ceramic was crushed and kept in closed spaces. The physical characteristics of the crushed waste ceramics are displayed in Table 4. The aggregate gradation (Table 5) was selected in accordance with ASTM C33.Table 4
Aggregate physical properties.
CharacteristicsStandardNatural coarse-grained aggregateNatural fine-grained aggregateWaste ceramic fine-grainedaggregateNominal maximum aggregate sizeASTM-C 13612.54.754.75Los Angeles abrasion (%)ASTM-C 13117.2——Bulk density (gr/cm3)ASTM-C 1272.682.712.13SSD density (gr/cm3)ASTM-C 1272.7162.732.15Water absorption (%)ASTM-C 1270.530.581.30Clay (%)ASTM-C 1420.010.017—Table 5
The aggregate gradation used in this research.
Sieve size (mm)Passing percentPassing percent limit25.41001001910083–10012.49072–939.58066–854.7558.851–692.384938–561.23728–460.62618–360.31211–270.1576–180.07522–8


## 2.2. Mix Design Preparation

In this research, six mixing designs have been used in which carbon fibers have replaced the natural materials left on the # 4 sieve, and waste ceramics have replaced the natural materials passing through the # 4 sieve. The mix designs are shown in Table6.Table 6
Sample mixing scheme.
Sample namesCement (kg/m3)Carbon fiber (% of cement volume)Natural coarse aggregate (kg/m3)Natural fine aggregate (kg/m3)Waste ceramic fine aggregate (kg/m3)w/cWater (kg/m3)N-CF03000841120000.38114WC-CF0300084109430.38114N-CF13001841120000.38114WC-CF1300184109430.38114N-CF33003841120000.38114WC-CF3300384109430.38114Cylindrical molds with a diameter of 15 cm and a height of 30 cm (for tests of compressive strength and tensile strength) and cylindrical molds with a diameter of 10 cm and a height of 20 cm (for testing electrical conductivity and rapid chloride permeability) were used. For compaction of samples, the Vebe device was used with a surcharge of 22.7 kg. After removing from the molds, the specimens were placed in the water pool for 28 days to complete the curing period.


## 2.3. Tests

### 2.3.1. Workability Test

In order to analyze the influence of different characteristics such as waste ceramic, aggregate, and fiber content, the workability of RCCP mixes in the plastic state was assessed using a modified Vebe test in line with ASTM C1170. Vebe time is defined as the time it takes for a full ring of concrete mortar to develop around the Vebe molding during compaction. A surcharge of 22.7 kg was employed in this investigation to vibrate the RCCP specimens.


### 2.3.2. Compressive Strength Test

Mechanical compressive strength tests were performed till failure on 30 × 15 cm cylindrical specimens according to ASTM C109/C109 M [37] after curing for 28 days. As a result, the relative compressive strength values for each mix design were determined using triplicate samples of the target temperatures. The average compressive strength of the three samples was determined and compared to the control sample values’ average compressive strength.


### 2.3.3. Indirect Tensile Strength Test

The IDT test instrument was applied to assess the ITS of RCC samples according to the AASHTO T 283–03 [4] test procedure. For this test, a uniform compressive force of 50 mm per minute was applied until failure. At least four replicate samples were evaluated for each of the mixed design categories. In this test method, after applying a vertical compressive force, the tensile tension arises in the horizontal direction and the indirect tensile stress (psi) can be calculated using the following equation:(1)ITS=2PπDt,where P, D, and t represent the peak load in Newtons, the diameter in centimeters, and the thickness in centimeters, respectively. The study was conducted using the observed average diameters and thicknesses. No adjustments were made to the readings acquired from the test instrument.


### 2.3.4. Electrical Resistance Test

In this research, the Wenner method (four-point) was utilized to obtain the electrical resistance of 10 × 20 cylindrical specimens cured for 28 days. To perform the experiment, a Reisman device was used which has four electrodes that are in a straight line and their distance from each other is the same. The two internal electrodes of the machine calculate the difference in the electrical potential created, and the two external electrodes introduce alternating current to the concrete surface.


### 2.3.5. Rapid Chloride Permeability Test (RCPT)

After 28 days of water curing, two 5-cm thick specimens were cut from a 10 cm (diameter) by 20 cm (height) cylinder for each combination. A water-cooled diamond saw was used to cut each specimen from the cylinder’s central part. A belt sander was used to smooth down the rough edges and notches, resulting in a smooth surface on both sides of the specimen. Following vacuum conditioning, two specimens of each combination were placed between two containers containing solutions of 3% sodium chloride (NaCl) and 1.2% sodium hydroxide (NaOH) (Table7). Following that, 60 volts of electricity were applied for 6 hours in accordance with the ASTM C 1202–91 standard, and the results for each blend were averaged.Table 7
The usage rate of solutions.
NaOHNaClSample size (cm)Caustic soda (%)Water (%)Salt (%)Water (%)10 (diameter) × 5 (height)1.298.8397


### 2.3.6. Bulk Specific Gravity Test

Because of the difference in specific gravity between ceramics, carbon fibers, and natural aggregates, the specific gravity of the specimens was calculated using the ASTM C 642 standard. After measuring the dry weight of the samples, the weight of water in each sample was then determined using the Archimedes scale, and the specific gravity of the samples was estimated.


### 2.3.7. Skid Resistance Test

The skid resistance test was carried out in accordance with ASTM E 303–93 [26]. Skid resistance was determined using pendulum friction testing equipment. The pendulum and pointer were released from the horizontal position, and the pendulum arm was measured on its return swing. The scale’s pointer location was recorded (pendulum test value).


### 2.3.8. Freeze Thaw Exposure

RCC cylindrical samples of 150 × 300 mm were submitted to quick freeze-thaw testing in accordance with ASTM C666 procedure B for freeze-thaw resistance [27]. The samples were placed in a freeze-thaw chamber configured to freeze in the air at −18°C for 1 hr and thaw in water at +4°C for 1 hr. All RCC samples were treated to a total of 300 freeze-thaw cycles. At 150 and 300 cycles, the compressive strength, indirect tensile strength, electrical resistance, rapid chloride permeability, specific gravity, and pendulum tests were performed.


## 2.3.1. Workability Test

In order to analyze the influence of different characteristics such as waste ceramic, aggregate, and fiber content, the workability of RCCP mixes in the plastic state was assessed using a modified Vebe test in line with ASTM C1170. Vebe time is defined as the time it takes for a full ring of concrete mortar to develop around the Vebe molding during compaction. A surcharge of 22.7 kg was employed in this investigation to vibrate the RCCP specimens.


## 2.3.2. Compressive Strength Test

Mechanical compressive strength tests were performed till failure on 30 × 15 cm cylindrical specimens according to ASTM C109/C109 M [37] after curing for 28 days. As a result, the relative compressive strength values for each mix design were determined using triplicate samples of the target temperatures. The average compressive strength of the three samples was determined and compared to the control sample values’ average compressive strength.


## 2.3.3. Indirect Tensile Strength Test

The IDT test instrument was applied to assess the ITS of RCC samples according to the AASHTO T 283–03 [4] test procedure. For this test, a uniform compressive force of 50 mm per minute was applied until failure. At least four replicate samples were evaluated for each of the mixed design categories. In this test method, after applying a vertical compressive force, the tensile tension arises in the horizontal direction and the indirect tensile stress (psi) can be calculated using the following equation:(1)ITS=2PπDt,where P, D, and t represent the peak load in Newtons, the diameter in centimeters, and the thickness in centimeters, respectively. The study was conducted using the observed average diameters and thicknesses. No adjustments were made to the readings acquired from the test instrument.


## 2.3.4. Electrical Resistance Test

In this research, the Wenner method (four-point) was utilized to obtain the electrical resistance of 10 × 20 cylindrical specimens cured for 28 days. To perform the experiment, a Reisman device was used which has four electrodes that are in a straight line and their distance from each other is the same. The two internal electrodes of the machine calculate the difference in the electrical potential created, and the two external electrodes introduce alternating current to the concrete surface.


## 2.3.5. Rapid Chloride Permeability Test (RCPT)

After 28 days of water curing, two 5-cm thick specimens were cut from a 10 cm (diameter) by 20 cm (height) cylinder for each combination. A water-cooled diamond saw was used to cut each specimen from the cylinder’s central part. A belt sander was used to smooth down the rough edges and notches, resulting in a smooth surface on both sides of the specimen. Following vacuum conditioning, two specimens of each combination were placed between two containers containing solutions of 3% sodium chloride (NaCl) and 1.2% sodium hydroxide (NaOH) (Table7). Following that, 60 volts of electricity were applied for 6 hours in accordance with the ASTM C 1202–91 standard, and the results for each blend were averaged.Table 7
The usage rate of solutions.
NaOHNaClSample size (cm)Caustic soda (%)Water (%)Salt (%)Water (%)10 (diameter) × 5 (height)1.298.8397


## 2.3.6. Bulk Specific Gravity Test

Because of the difference in specific gravity between ceramics, carbon fibers, and natural aggregates, the specific gravity of the specimens was calculated using the ASTM C 642 standard. After measuring the dry weight of the samples, the weight of water in each sample was then determined using the Archimedes scale, and the specific gravity of the samples was estimated.


## 2.3.7. Skid Resistance Test

The skid resistance test was carried out in accordance with ASTM E 303–93 [26]. Skid resistance was determined using pendulum friction testing equipment. The pendulum and pointer were released from the horizontal position, and the pendulum arm was measured on its return swing. The scale’s pointer location was recorded (pendulum test value).


## 2.3.8. Freeze Thaw Exposure

RCC cylindrical samples of 150 × 300 mm were submitted to quick freeze-thaw testing in accordance with ASTM C666 procedure B for freeze-thaw resistance [27]. The samples were placed in a freeze-thaw chamber configured to freeze in the air at −18°C for 1 hr and thaw in water at +4°C for 1 hr. All RCC samples were treated to a total of 300 freeze-thaw cycles. At 150 and 300 cycles, the compressive strength, indirect tensile strength, electrical resistance, rapid chloride permeability, specific gravity, and pendulum tests were performed.


## 2.4. Background of Prediction Methods

### 2.4.1. Kernelized Support Vector Regression

The support vector regression (SVR) model is a supervised learning method which is applied to predict discrete values. This method uses the same principle of the support vector machines (SVMs) and is only used for the prediction of linear data. Kernelized support vector regression (KSVR) is an extension of the SVR model that uses some kernel functions [19]. This model can be used for nonlinear data [28]. The kernel function transfers the data to a higher dimension space for allowing linear separation by equation (2) [29]:(2)predictx=∑i=1Nαi−αi∗Kxi,x−b,in which x is the model input, K is the kernel function, b is a bias b∈R, and αi and αi∗ are Lagrangian multipliers that fulfill the equality αiαi∗=0,αiandαi∗≥0.Some of the most important of the kernel functions are polynomials and Gaussian that are obtained by equations (3) and (4), respectively.(3)Kxi,xj=xi.xj+1d,(4)Kxi,xj=exp−xi−xj22r2,where r is the width of the kernel function and d is an integer value greater than one.


### 2.4.2. Radial Basis Function Neural Networks

Radial-bias function (RBF) neural networks have significant advantages in comparison with back propagation neural networks, including the ownership of a simple structure, fast learning algorithms, and no local minimum problems [30, 31]. A three-layer feed-forward design is typical of RBFS: an input layer, a hidden layer, and an output layer. The hidden layer nodes are centered at specific positions with a given radius, and the distance between the input vectors and their centers is calculated [16].Usually, the Gaussian functionφ is applied as a transfer function of the RBF model that is calculated by the following equation [2]:(5)φjx=exp−x−cj2rj2,where cj and rj are the center and width of the jth RBF node, respectively. The output of the RBF ANN ym is calculated by the equation as follows:(6)ym=∑i=0nφjwj.Here,wj is the weight value between the output layer node and the hidden layer node.


## 2.4.1. Kernelized Support Vector Regression

The support vector regression (SVR) model is a supervised learning method which is applied to predict discrete values. This method uses the same principle of the support vector machines (SVMs) and is only used for the prediction of linear data. Kernelized support vector regression (KSVR) is an extension of the SVR model that uses some kernel functions [19]. This model can be used for nonlinear data [28]. The kernel function transfers the data to a higher dimension space for allowing linear separation by equation (2) [29]:(2)predictx=∑i=1Nαi−αi∗Kxi,x−b,in which x is the model input, K is the kernel function, b is a bias b∈R, and αi and αi∗ are Lagrangian multipliers that fulfill the equality αiαi∗=0,αiandαi∗≥0.Some of the most important of the kernel functions are polynomials and Gaussian that are obtained by equations (3) and (4), respectively.(3)Kxi,xj=xi.xj+1d,(4)Kxi,xj=exp−xi−xj22r2,where r is the width of the kernel function and d is an integer value greater than one.


## 2.4.2. Radial Basis Function Neural Networks

Radial-bias function (RBF) neural networks have significant advantages in comparison with back propagation neural networks, including the ownership of a simple structure, fast learning algorithms, and no local minimum problems [30, 31]. A three-layer feed-forward design is typical of RBFS: an input layer, a hidden layer, and an output layer. The hidden layer nodes are centered at specific positions with a given radius, and the distance between the input vectors and their centers is calculated [16].Usually, the Gaussian functionφ is applied as a transfer function of the RBF model that is calculated by the following equation [2]:(5)φjx=exp−x−cj2rj2,where cj and rj are the center and width of the jth RBF node, respectively. The output of the RBF ANN ym is calculated by the equation as follows:(6)ym=∑i=0nφjwj.Here,wj is the weight value between the output layer node and the hidden layer node.


## 3. Test Results and Analyses

### 3.1. Workability

The influence of carbon fibers on the workability of several produced RCCPs is depicted in Figure2. As a result, at a constant w/c ratio, increasing the fiber content increased the Vebe time, showing that fiber-reinforced specimens are less workable. The reduced workability has been attributed to the friction between the fiber and the coarse aggregates, as well as the fibers’ mobility limitations in the presence of these particles [32]. Other investigations found that the introduction of fibers had a detrimental impact on workability due to the friction between the fibers and the cement paste [31]. Figure 2 demonstrates that adding waste ceramic aggregate significantly increased the Vebe time, which can be attributed to the waste ceramic aggregate having a rougher surface than the natural aggregate. As a result, in order to achieve the same workability, the w/c ratio in the fiber-reinforced RCCP should be greater than that in the conventional RCCP. The ASTM C1170 standard requires a vibration time of 30–45 seconds for RCCP specimens compressed by a 22.7 kg surcharge. As a result, only the RCC-C0-CF0 and RCC-C0-CF1.5 RCCP specimens displayed satisfactory workability.Figure 2
Vebe time of the mixtures.


### 3.2. Compressive Strength Test Results

The compressive strength of the mix designs cured for 28 days before and after freeze-thaw conditionings is illustrated in Figure3. As can be seen, for the mix designs, the compressive strength was reduced with freeze-thaw exposure relative to unconditioned ones. The samples containing waste ceramic fine-grained aggregate showed slightly higher strength compared with the control mixture both before and after freeze-thaw exposure, which might be due to the surface texture of the ceramic aggregate, creating a bond with cement paste and the pozzolanic reaction of the smaller particles of this aggregate (finer than 0.075 mm) with the paste [29, 33]. For specimens containing carbon fiber, no noticeable change was attained before exposure to freeze-thaw cycles, but then clearly, it can be seen that carbon fiber reduced the reduction rate slightly for the samples experiencing freeze-thaw conditioning, thereby more durability can be expected for mixtures containing this fiber [10, 34].Figure 3
Results of 28-day compressive strength.


### 3.3. Indirect Tensile Test Results

Figure4 displays the indirect tensile strength test results of the mixtures. For unconditioned samples, the waste ceramic aggregate substitution led to similar strength, while with carbon fiber incorporation, there was a slight increase in splitting tensile strength for both natural and waste ceramic aggregates, and the highest strength was attained for WC-CF3. After freeze-thaw conditioning, the tensile strength of all samples was reduced by the exposition regardless of the aggregate type and carbon fiber incorporation. Similarly, WC-CF3 experienced the highest tensile strength in comparison with the other samples, making it more durable than the control mixture with natural aggregates [28].Figure 4
Results of 28-day indirect tensile strength.


### 3.4. Electrical Resistance Test Results

Figure5 illustrates the electrical resistance of the mixtures containing natural and waste ceramic fine-grained aggregates reinforced with carbon fiber both before and after 150 and 300 freeze-thaw cycles of exposure. As shown in the figure, the electrical resistance of the specimens containing waste ceramic aggregate is higher than that of the plain concrete, which might be attributed to the higher insulation power and electrical resistance of the ceramic aggregate in comparison with the natural aggregate [30]. This trend continued after freeze-thaw conditioning, and the resistivity of the specimen reduced with higher freeze-thaw cycling conditions, which might be attributed to the weight loss of the sample with the exposition and consequently the creation of more air pores, making it less resistant against freeze-cycles. This resistance decreased when carbon fiber was used, and with higher carbon fiber incorporation, the lowest electrical resistance was achieved which is for N-FC3. This results from the fact that carbon fiber can act as an electrical conductor and thereby increase the conductivity of the sample. For the samples containing waste ceramic aggregate, similar trends were attained; however, more resistance against electricity was gained relative to the control mixture, which can be attributed to the chemical composition of the ceramic aggregate, leading to a more durable mixture.Figure 5
Results of a 28-day electrical resistance test.


### 3.5. Rapid Chloride Permeability Test Results

Figure6 depicts the RCPT test results for cylindrical specimens with a diameter of 5 and a height of 10 cm before and after freeze-thaw exposure. As can be seen, the sample containing waste ceramic aggregate had a lower passing charge in comparison with the control one, and the result is similar to the result obtained in the electrical resistivity test. So, the waste ceramic incorporation in the RCC mixture creates a more durable mixture in the long term, and according to Table 8, this aggregate type can result in very low chloride ion permeability, which makes it less susceptible to the damage caused by the environment. In addition, carbon fiber addition led to a higher passing charge, and with increasing the amount of fiber, more charge would be passed regardless of the used aggregate type, and the highest charge was attained for N-CF3. When the mixtures were exposed to freeze-thaw cycling, the passing charge increased, which can be related to the weight loss of the samples after exposure to these cycles, creating more air pores in the cement paste.Figure 6
Results of 28 days of the rapid chloride permeability test.Table 8
Relationship between chloride ion penetrability and charge passed in accordance with ASTM C 1202.
Chloride ion permeabilityCharge passed (coulombs)High>4000Moderate2000–4000Low1000–2000Very low100–1000Negligible<100The difference in the sample temperature before and after conducting the RCPT test is depicted in Figure7. From the figure, the waste ceramic incorporation resulted in a significant decrease in the difference which is based on the fact that this aggregate reduced the conductivity of the RCC mixture, so lower heat was resulted. In addition, carbon fiber incorporation resulted in higher temperature differences, creating an electrically conductive mixture which can be used to melt snow and ice on roadways.Figure 7
Results of temperature changes of samples.


### 3.6. Specific Gravity Test Results

Figure8 demonstrates the bulk specific gravity of the samples before and after freeze-thaw conditioning. As shown in the figure, for the unconditioned mixtures, waste ceramic incorporation led to a decrease in the specific gravity. This can be due to the lower aggregate’s specific gravity being compared to the natural one. In addition, carbon fiber introduction reduced the specific gravity negligibly resulting from the lower specific gravity of the fiber relative to the natural aggregate and slightly reduced the compactability of the mixture containing this fiber. After freeze-thaw exposure, the bulk specific gravity of all the samples reduced, and for higher cycles, the lower specific gravity was attained. Furthermore, carbon fiber usage reduced the rate of specific gravity reduction, and using a higher fiber percent resulted in better performance as it reduces the weight loss that can be expected in the samples without the fiber.Figure 8
Specific gravity test results.


### 3.7. Skid Resistance Test Results

Figure9 depicts the skid resistance test results of the samples before and after freeze-thaw cycling. From the figure, it can be clearly noted that before conditioning, the samples without carbon fiber resulted in similar British pendulum numbers regardless of the aggregate type, while carbon fiber incorporation negligibly reduced the skid resistance of the mixture, and by using a higher content of the fiber, more reduction was achieved. However, after freeze-thaw conditioning, different trends were attained. For mixtures containing ceramic aggregates and carbon fibers, a reduction in the pendulum number was recorded in comparison with the control mixture, thereby having lower skid resistance after freeze-thaw exposure, but they are still in the range of 48–59 pendulum numbers under the wet condition [35].Figure 9
British pendulum number under wet conditions.


### 3.8. Results of RBF and KSVR Models

In this research, the RBF and KSVR methods are used to estimate the indirect tensile strength (ITS) and compressive strength (CS). In order to predict these values, carbon fiber, natural fine waste, ceramic fine aggregate, and moisture conditioning are considered as the inputs of these models. Some criteria including the root-mean-square error (RMSE) and mean absolute error (MAE) are applied for assessing the prediction models. The data are randomly divided into training data (80%) and test data (20%).(7)RMSE=∑i=1nyi−yi^2n,MAE=1n∑i=1nyi−yi^.Here,yi is the actual value, yi^ is the predicted value of data, and n stands for the number of observations.Tables9 and 10 show the performance of the KSVR model for the estimation of CS and ITS variables for polynomial and Gaussian kernels. As shown in these tables, for both variables, the polynomial performs better.Table 9
Performance of the KSVR model for the estimation of CS.
Test dataTotal dataPolynomialGaussianPolynomialGaussianRMSE0.40640.91230.26710.8089MAE0.24630.73040.21760.5514Table 10
Performance of the KSVR model for the estimation of ITS.
Test dataTotal dataPolynomialGaussianPolynomialGaussianRMSE0.10050.18980.06050.1596MAE0.08480.14040.05120.1079Actual and predicted values of CS by the RBF and KSVR models and their corresponding R-squared values for the test and total data are shown in Figures10 and 11. According to these figures, for both models, R-squared values are large which show the high performance of these models.Figure 10
Actual and predicted values of CS by the RBF and KSVR models for test data.Figure 11
Actual and predicted values of CS by the RBF and KSVR models for total data.Similarly, Figures12 and 13 show the actual and predicted values of ITS by the RBF and KSVR models. As shown in these figures, both models have high performance.Figure 12
Actual and predicted values of ITS by the RBF and KSVR models for test data.Figure 13
Actual and predicted values of ITS by the RBF and KSVR models for total data.Finally, the comparison of RBF and KSVR models for the estimation of CS and ITS is summarized in Tables11 and 12. According to these tables and as shown in the previous figures, both the RBF and KSVR models have high performance in estimating the CS and ITS values. But the error values for the RBF method are lower than those for KSVR ones, and the R-squared values are also higher. Therefore, RBF has more accuracy than the KSVR model in estimating CS and ITS values.Table 11
Comparison of RBF and KSVR models for estimating CS.
Test dataTotal dataRBFKSVRRBFKSVRRMSE0.25750.40640.12140.2671MAE0.19720.24630.04380.2176Table 12
Comparison of RBF and KSVR models for estimating ITS.
Test dataTotal dataRBFKSVRRBFKSVRRMSE0.08110.10050.04290.0605MAE0.06650.08480.02890.0512


## 3.1. Workability

The influence of carbon fibers on the workability of several produced RCCPs is depicted in Figure2. As a result, at a constant w/c ratio, increasing the fiber content increased the Vebe time, showing that fiber-reinforced specimens are less workable. The reduced workability has been attributed to the friction between the fiber and the coarse aggregates, as well as the fibers’ mobility limitations in the presence of these particles [32]. Other investigations found that the introduction of fibers had a detrimental impact on workability due to the friction between the fibers and the cement paste [31]. Figure 2 demonstrates that adding waste ceramic aggregate significantly increased the Vebe time, which can be attributed to the waste ceramic aggregate having a rougher surface than the natural aggregate. As a result, in order to achieve the same workability, the w/c ratio in the fiber-reinforced RCCP should be greater than that in the conventional RCCP. The ASTM C1170 standard requires a vibration time of 30–45 seconds for RCCP specimens compressed by a 22.7 kg surcharge. As a result, only the RCC-C0-CF0 and RCC-C0-CF1.5 RCCP specimens displayed satisfactory workability.Figure 2
Vebe time of the mixtures.


## 3.2. Compressive Strength Test Results

The compressive strength of the mix designs cured for 28 days before and after freeze-thaw conditionings is illustrated in Figure3. As can be seen, for the mix designs, the compressive strength was reduced with freeze-thaw exposure relative to unconditioned ones. The samples containing waste ceramic fine-grained aggregate showed slightly higher strength compared with the control mixture both before and after freeze-thaw exposure, which might be due to the surface texture of the ceramic aggregate, creating a bond with cement paste and the pozzolanic reaction of the smaller particles of this aggregate (finer than 0.075 mm) with the paste [29, 33]. For specimens containing carbon fiber, no noticeable change was attained before exposure to freeze-thaw cycles, but then clearly, it can be seen that carbon fiber reduced the reduction rate slightly for the samples experiencing freeze-thaw conditioning, thereby more durability can be expected for mixtures containing this fiber [10, 34].Figure 3
Results of 28-day compressive strength.


## 3.3. Indirect Tensile Test Results

Figure4 displays the indirect tensile strength test results of the mixtures. For unconditioned samples, the waste ceramic aggregate substitution led to similar strength, while with carbon fiber incorporation, there was a slight increase in splitting tensile strength for both natural and waste ceramic aggregates, and the highest strength was attained for WC-CF3. After freeze-thaw conditioning, the tensile strength of all samples was reduced by the exposition regardless of the aggregate type and carbon fiber incorporation. Similarly, WC-CF3 experienced the highest tensile strength in comparison with the other samples, making it more durable than the control mixture with natural aggregates [28].Figure 4
Results of 28-day indirect tensile strength.


## 3.4. Electrical Resistance Test Results

Figure5 illustrates the electrical resistance of the mixtures containing natural and waste ceramic fine-grained aggregates reinforced with carbon fiber both before and after 150 and 300 freeze-thaw cycles of exposure. As shown in the figure, the electrical resistance of the specimens containing waste ceramic aggregate is higher than that of the plain concrete, which might be attributed to the higher insulation power and electrical resistance of the ceramic aggregate in comparison with the natural aggregate [30]. This trend continued after freeze-thaw conditioning, and the resistivity of the specimen reduced with higher freeze-thaw cycling conditions, which might be attributed to the weight loss of the sample with the exposition and consequently the creation of more air pores, making it less resistant against freeze-cycles. This resistance decreased when carbon fiber was used, and with higher carbon fiber incorporation, the lowest electrical resistance was achieved which is for N-FC3. This results from the fact that carbon fiber can act as an electrical conductor and thereby increase the conductivity of the sample. For the samples containing waste ceramic aggregate, similar trends were attained; however, more resistance against electricity was gained relative to the control mixture, which can be attributed to the chemical composition of the ceramic aggregate, leading to a more durable mixture.Figure 5
Results of a 28-day electrical resistance test.


## 3.5. Rapid Chloride Permeability Test Results

Figure6 depicts the RCPT test results for cylindrical specimens with a diameter of 5 and a height of 10 cm before and after freeze-thaw exposure. As can be seen, the sample containing waste ceramic aggregate had a lower passing charge in comparison with the control one, and the result is similar to the result obtained in the electrical resistivity test. So, the waste ceramic incorporation in the RCC mixture creates a more durable mixture in the long term, and according to Table 8, this aggregate type can result in very low chloride ion permeability, which makes it less susceptible to the damage caused by the environment. In addition, carbon fiber addition led to a higher passing charge, and with increasing the amount of fiber, more charge would be passed regardless of the used aggregate type, and the highest charge was attained for N-CF3. When the mixtures were exposed to freeze-thaw cycling, the passing charge increased, which can be related to the weight loss of the samples after exposure to these cycles, creating more air pores in the cement paste.Figure 6
Results of 28 days of the rapid chloride permeability test.Table 8
Relationship between chloride ion penetrability and charge passed in accordance with ASTM C 1202.
Chloride ion permeabilityCharge passed (coulombs)High>4000Moderate2000–4000Low1000–2000Very low100–1000Negligible<100The difference in the sample temperature before and after conducting the RCPT test is depicted in Figure7. From the figure, the waste ceramic incorporation resulted in a significant decrease in the difference which is based on the fact that this aggregate reduced the conductivity of the RCC mixture, so lower heat was resulted. In addition, carbon fiber incorporation resulted in higher temperature differences, creating an electrically conductive mixture which can be used to melt snow and ice on roadways.Figure 7
Results of temperature changes of samples.


## 3.6. Specific Gravity Test Results

Figure8 demonstrates the bulk specific gravity of the samples before and after freeze-thaw conditioning. As shown in the figure, for the unconditioned mixtures, waste ceramic incorporation led to a decrease in the specific gravity. This can be due to the lower aggregate’s specific gravity being compared to the natural one. In addition, carbon fiber introduction reduced the specific gravity negligibly resulting from the lower specific gravity of the fiber relative to the natural aggregate and slightly reduced the compactability of the mixture containing this fiber. After freeze-thaw exposure, the bulk specific gravity of all the samples reduced, and for higher cycles, the lower specific gravity was attained. Furthermore, carbon fiber usage reduced the rate of specific gravity reduction, and using a higher fiber percent resulted in better performance as it reduces the weight loss that can be expected in the samples without the fiber.Figure 8
Specific gravity test results.


## 3.7. Skid Resistance Test Results

Figure9 depicts the skid resistance test results of the samples before and after freeze-thaw cycling. From the figure, it can be clearly noted that before conditioning, the samples without carbon fiber resulted in similar British pendulum numbers regardless of the aggregate type, while carbon fiber incorporation negligibly reduced the skid resistance of the mixture, and by using a higher content of the fiber, more reduction was achieved. However, after freeze-thaw conditioning, different trends were attained. For mixtures containing ceramic aggregates and carbon fibers, a reduction in the pendulum number was recorded in comparison with the control mixture, thereby having lower skid resistance after freeze-thaw exposure, but they are still in the range of 48–59 pendulum numbers under the wet condition [35].Figure 9
British pendulum number under wet conditions.


## 3.8. Results of RBF and KSVR Models

In this research, the RBF and KSVR methods are used to estimate the indirect tensile strength (ITS) and compressive strength (CS). In order to predict these values, carbon fiber, natural fine waste, ceramic fine aggregate, and moisture conditioning are considered as the inputs of these models. Some criteria including the root-mean-square error (RMSE) and mean absolute error (MAE) are applied for assessing the prediction models. The data are randomly divided into training data (80%) and test data (20%).(7)RMSE=∑i=1nyi−yi^2n,MAE=1n∑i=1nyi−yi^.Here,yi is the actual value, yi^ is the predicted value of data, and n stands for the number of observations.Tables9 and 10 show the performance of the KSVR model for the estimation of CS and ITS variables for polynomial and Gaussian kernels. As shown in these tables, for both variables, the polynomial performs better.Table 9
Performance of the KSVR model for the estimation of CS.
Test dataTotal dataPolynomialGaussianPolynomialGaussianRMSE0.40640.91230.26710.8089MAE0.24630.73040.21760.5514Table 10
Performance of the KSVR model for the estimation of ITS.
Test dataTotal dataPolynomialGaussianPolynomialGaussianRMSE0.10050.18980.06050.1596MAE0.08480.14040.05120.1079Actual and predicted values of CS by the RBF and KSVR models and their corresponding R-squared values for the test and total data are shown in Figures10 and 11. According to these figures, for both models, R-squared values are large which show the high performance of these models.Figure 10
Actual and predicted values of CS by the RBF and KSVR models for test data.Figure 11
Actual and predicted values of CS by the RBF and KSVR models for total data.Similarly, Figures12 and 13 show the actual and predicted values of ITS by the RBF and KSVR models. As shown in these figures, both models have high performance.Figure 12
Actual and predicted values of ITS by the RBF and KSVR models for test data.Figure 13
Actual and predicted values of ITS by the RBF and KSVR models for total data.Finally, the comparison of RBF and KSVR models for the estimation of CS and ITS is summarized in Tables11 and 12. According to these tables and as shown in the previous figures, both the RBF and KSVR models have high performance in estimating the CS and ITS values. But the error values for the RBF method are lower than those for KSVR ones, and the R-squared values are also higher. Therefore, RBF has more accuracy than the KSVR model in estimating CS and ITS values.Table 11
Comparison of RBF and KSVR models for estimating CS.
Test dataTotal dataRBFKSVRRBFKSVRRMSE0.25750.40640.12140.2671MAE0.19720.24630.04380.2176Table 12
Comparison of RBF and KSVR models for estimating ITS.
Test dataTotal dataRBFKSVRRBFKSVRRMSE0.08110.10050.04290.0605MAE0.06650.08480.02890.0512


## 4. Conclusion

The study aimed to investigate the effect of using waste ceramic as a replacement for fine aggregate in roller compacted concrete (RCC) and the application of carbon fiber to improve the mechanical properties and electrical conductivity of RCC. To achieve this goal, several tests such as compressive strength, indirect tensile strength, electrical resistance, chloride ion penetration, specific gravity, and skid resistance tests were carried out on the fabricated samples before and after freeze-thaw cycling exposure. According to the tests’ results, the key findings can be as follows:(1)
The use of waste ceramics in roller concrete increased the compressive strength, and the addition of carbon fiber had less effect on the compressive strength. Freeze-thaw exposure resulted in a decrease in the strength, but carbon fiber incorporation reduced the rate of reduction.(2)
The use of waste ceramics in roller concrete increased the tensile strength. Moreover, with the addition of carbon fibers, the tensile strength increased and the highest strength was obtained for the sample containing waste ceramic aggregate and 1% of carbon fiber. In addition, carbon fiber reduced the rate of reduction after exposure to freeze-thaw cycles.(3)
Waste ceramics increased the electrical resistance, thereby increasing the durability of the mixture in the long term. Meanwhile, carbon fiber incorporation reduced the resistance, which created an electrically conductive concrete to melt snow and ice on the road surface in colder regions.(4)
According to the RCPT test results, the waste ceramic reduced significantly the passing charge, while with the addition of carbon fiber, the passing charge increased. Moreover, by comparing the results of the samples, the lower the electrical resistance of the sample, the higher the passing charge would be achieved, and with increasing the passing charge, the temperature of the sample also increased. Moreover, freeze-thaw exposure resulted in an increase in the passing charge in all samples regardless of the aggregate type.(5)
Waste ceramics reduced the specific gravity of the samples as a result of lower specific gravity of the ceramic aggregate relative to the natural one. Moreover, by adding carbon fibers to the concrete, the specific gravity is slightly reduced, which can be attributed to the lower compactability of the fiber-reinforced mixture and the lower specific gravity of the carbon fiber in comparison with the natural aggregate.(6)
Waste ceramic had less effect on the skid resistance of the mixture, but carbon fiber incorporation slightly reduced the skid resistance of the RCC mixture. After freeze-thaw exposure, the skid resistance of all mixtures increased while carbon fiber usage reduced the rate of increase.(7)
The results of the prediction model showed that both the KSVR and RBF models have high performance in the estimation of ITS and CS values. But the accuracy of the RBF model was higher than that of the KSVR model.



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*Source: 1308387-2023-01-25.xml*