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Assessing the phenomenon of clogging of pervious concrete (Pc): Experimental test and model proposition
Journal of Building Engineering 29 (2020) 101203
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Journal of Building Engineering journal homepage: http://www.elsevier.com/locate/jobe
Assessing the phenomenon of clogging of pervious concrete (Pc): Experimental test and model proposition Gersson F.B. Sandoval a, *, Isaac Galobardes b, Andr�e Campos c, Berenice.M. Toralles a a
State University of Londrina, Civil Construction Department, State University of Londrina, Celso Garcia Road Pr 445 km 380, Londrina, Paran� a, Brazil School of Architecture, Planning and Design. Mohammed VI Polytechnic University. Lot 660, Hay Moulay Rachid, Ben Guerir, 43150, Morocco c State University of Londrina, Structures Department, State University of Londrina, Celso Garcia Road Pr 445 km 380, Londrina, Paran� a, Brazil b
A R T I C L E I N F O
A B S T R A C T
Keywords: Pervious concrete Permeability Porosity Clogging Sediments concentration Service life
During rainfall, different types of sediments are transported (organic and inorganic) that end up prejudicing the hydraulic efficiency of materials such as pervious concrete (PC). Sediments tend to accumulate on the surface of the material and its internal pore structure causing a decrease in permeability. This phenomenon is known as clogging. This study aims to identify and characterize the phenomenon of clogging of PC considering laboratory and field conditions. In that sense, a PC mix and three types of sediment (sand (S), clay (C), and mixture of both (S þ C)) were considered. Two types of permeability tests were adapted to induce the clogging and estimate the permeability reduction: the constant head test (laboratory), and the ASTM C1701 (field). Finally, different rates of clogging (low, medium and high) were considered. Considering the type of sediments, the finer they are the higher is the influence on the clogging phenomenon, reaching maximum permeability reductions greater than 95%. Considering the rate of clogging, the results show that low rate entails higher clogging. On the other hand, an analytical model to describe the clogging was proposed. This predicts efficiently the reduction of permeability considering the type and concentration of sediment. This model was proposed with experimental data obtained and validated with results from other investigations. The model aims to be used for engineers to design PC considering their life span and maintenance.
1. Introduction The urban development, added to the lack of planning, brings as a direct consequence the waterproofing of the soil. This is directly related to the pavement of streets and the civil construction industry [1,2]. This waterproofing and the inefficiency of the existing drainage systems in certain countries, increases the potential of floods during a precipitation event, creating numerous problems for the civilian population. In response to this type of problem, researchers focused on finding new materials that allow the passage of water through its structure without changing the mechanical properties. One of these materials is the pervious concrete (PC) [3–5]. PC is a special concrete with high porosity (15–30%), and therefore high permeability (>1 mm/s) [1,3,4,6–10] which is calculated by the permeability tests at falling head and at constant head permeability test. In that sense, the permeability of the material is related to the effective porosity, the grading of the coarse aggregate and the quantity of paste in
the mix proportions [7,11–15]. Due to the large voids, PC is subjected to the accumulation of different types of materials (sediments) inside the voids leading to a reduction of permeability. These sediments are generally transported during rainfall events. During a rainfall event, the flow of rainwater takes suspended ma terial (sediments), which are usually grains of sand, silt and clay, that by their small size facilitate their transport [16–18]. The transport of sed iments may be so critical that, for example, in winter season, the Yellow river (China) carries as much suspended material as water [19]. The accumulation of sediments and the reduction of permeability in the PC is known as clogging [20]. This phenomenon is the process in which the internal voids are partially or totally filled by sediments [20–27]. Then, sediments decrease the cross section and interconnection of voids causing an increase in tortuosity, decreasing the permeability and affecting the durability and functionality of PC over time [28–30]. There are few studies related to the phenomenon of PC clogging. This was studied recreating extreme cases where sediment concentrations
* Corresponding authors. E-mail addresses: [email protected] (G.F.B. Sandoval), [email protected] (I. Galobardes), [email protected] (A. Campos), [email protected] (Berenice.M. Toralles). https://doi.org/10.1016/j.jobe.2020.101203 Received 12 August 2019; Received in revised form 14 January 2020; Accepted 17 January 2020 Available online 21 January 2020 2352-7102/© 2020 Elsevier Ltd. All rights reserved.
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Journal of Building Engineering 29 (2020) 101203
immediately impair the permeability of the material or considering a final permeability equal to 0 [18,23,24,29,31–36]. Experimental results allowed that this never takes place, therefore there is a lack of knowl edge about how this phenomenon is presented when it occurs progressively. In general, the sediments commonly used to induce clogging in the PC are coarse-grains (sands) and fine grains (clays). These are the most susceptible soils to generate a reduction of the permeability of the ma terial [16–18]. One of the greatest difficulties to evaluate the clogging is that there is no test method to do it, therefore previous investigations adapted the existent permeability tests (both laboratory and field test) to induce the addition of sediment and measure the variation of perme ability [29]. However, the test methodology is not clear. The reliability in its repetition is not guaranteed since important data such as the area of the cross-section of the specimen and the total mass of sediment used are not considered. Then, it is important developing a standardized methodology that allows a recreation of the phenomenon of clogging and guarantees its reliable repetition. Moreover, as the clogging of the PC has been carried out in labora tory and field conditions independently [25], the possible correlation that may exist under these two conditions is unknown. That fact limits the conclusions to each case in particular. In the same way, empirical correlations using exponential equations were proposed to correlate the mass of sediment and the variation of permeability [18] derived from mathematical studies carried out in the area of infiltration in hydrology [37]. However, these correlations were not completely validated. In that context, to give an answer to the existing gaps in the knowledge of the phenomenon of the clogging in the PC an experimental program was defined aiming: a) Induce clogging in the PC with three clogging rates (slow, medium and fast) with three types of sediments (sand, clay, and mixture of both) in the laboratory, b) Induce clogging in the PC with slow rate with three types of sediments (sand, clay, and mixture of both) in the field, and c) Propose and validate an analytical clogging model for PC based on the results found and other researchers’ results.
the material. In that sense, investigations have been developed to un derstand more about the hydraulic degradation of the material, however there have been no proposed methodologies that can be standardized and models that can be used to predict the behavior of the material in a simple way. That is why this aims to contemplates the standardization of a simple test methodology that simulates the degradation of PC under different conditions and proposes a method of forecasting how these properties can vary depending on where the material is used. 2. Experimental program The experimental program was divided into three experimental phases as shown in Fig. 1. The first one aimed to characterize the PC hydraulically and to obtain the initial permeability of the PC; the second to adapt the constant head permeability test to evaluate the clogging phenomenon in laboratory conditions, and the third one to adapt the ASTM C1701 to study the phenomenon of clogging in the field. So with the results obtained in these three experimental programs (phases) a clogging model for PC can be proposed. In this section, the materials and test methods used to fulfill the objectives are explained. 2.1. Materials and mix proportions In this study a Portland Cement type II and one type of basalt aggregate (B) were used. The maximum size of the aggregate was 9.50 mm. This was obtained from a quarry in the city of Londrina (Brazil) and it is the most common aggregate size to make PC according to the literature review [38–40]. On the other hand, in order to determine the phenomenon of clogging of PC, three types of sediments were used (sand (S), clay (C) and the mixture of the two (S þ C)), the sand sediments were chosen because in existing structures of pervious concrete (pavers) have been the materials that have filled the internal pores of the material damaging its permeability [17], additionally the clay sediments for � [41]. being the most common type of soil in the city of Londrina, Parana The specific gravity; material finer than 75 μm (%); water absorption, and unit weight of the aggregate and sediments are presented in Table 1. Notice that these results will be used to establish the mix design of the PC. Fig. 2 shows the grading of B, S and C, respectively. B presents a uniform grading, since its grains are basically distributed in two sizes, 71% for 4.80 mm, and 29% for 2.40 mm. In that regard, this grading is recommended by the ACI 522R-10 to produce PC [44]. The uniformity
1.1. Research significance One of the great limitations that the diffusion and correct use of the PC has had is the ignorance of its hydraulic performance during its useful life, added to the fact that the current norms do not contemplate a methodology to evaluate the degradation of the hydraulic properties of
Fig. 1. Experimental program. 2
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To study the phenomenon of clogging in laboratory conditions, a total of 48 cylindrical specimens (100φ x 200 mm) was fabricated. The compacting process for the cylindrical specimens was performed with a standard Proctor hammer (steel cylinder with 50 mm in diameter and 305 mm tall) with 2.50 kg of mass on top of the fresh concrete. The PC was compacted with ten drops in a single layer. All specimens were compacted by the same operator to minimize the variability. The spec imens were demolded 24 h after and cured in water with 99% humidity and temperature of 21 � C until the age of test. To study the phenomenon of clogging in the field, an excavation of 2.50 m long, 1.00 m wide and 0.45 m deep (Fig. 3a), was dug outside the laboratory. A 0.30 m-layer of coarse aggregate with a diameter of 19 mm was placed above the subgrade (Fig. 3b). This layer was made to guar antee the water storage conditions of the PC [47]. Finally, on top of this the 10 square slabs of PC were molded (0.45 x 0.45 x 0.10 m) (Fig. 3c). The slabs were compacted with a metal roller of 13.20 kg of weight (Fig. 3d). The roller passed 10 times (round trip) in a single layer per slab since this number of passes guaranteed the same density obtained in the laboratory (1890 kg/m3). Finally, the curing of the slabs was carried out by irrigation for 7 days, and they were covered with wet tarps to reduce excessive water loss (Fig. 3e).
Table 1 Main features of the aggregates used in the study. Type of aggregate
B S C
Specific gravity
Materials finer than 75 μm (%)
Water Absorption (%)
Unit weight (g/cm3)
NBR NM 53/ 2003 [42]
NBR NM 46/ 2006 [43]
NBR NM 53/ 2003 [42]
NBR NM 53/2003 [42]
2.72 2.49 3.03
0.51 2.00 –
1.4 5.0 –
1.45 1.65 –
coefficient (Cu) for this grading is 2 confirming the uniformity of its grains (values lower than 5 are uniform [45,46]). On the other hand, S presented a continuous grading (Cu of 4.75) and grain size varying from 4.80 mm to 0.15 mm. It has an FMS (Fineness modulus of sand) of 2.7 being classified as coarse sand Finally, the grading of C presented 55% of particles of clay size, 23% of silty size and 21% of fine sand [26]. This sediment has a liquid limit (wL) of 52% and plastic limit (wP) of 38% being common values of clay [26,30]. In this study additions or additives were not incorporated into the mixture, and mix proportions were taken from the study conducted by Sandoval (2017) [6]. The mix proportion presents the following char acteristics per cubic meter: the mass of cement was designated to be 420 kg; the mass of B was 1369 kg, and the w/c ratio was selected to be 0.34.
3. Experimental phases The experimental work was divided into three experimental phases, the first phase with the objective to the hydraulic characterization of the PC, the second phase with the objective to evaluate the clogging in PC in laboratory conditions, and the third phases with the objective of eval uating the clogging of PCs at the field.
2.2. Fabrication and curing of specimens The materials were produced in a standard mixer with a capacity of 150 L. The mixing process was made according to Sandoval et al. (2017) recommendation. Therefore, the total mixing time was 5 min.
Fig. 2. Grading of B, S, C and S þ C.
Fig. 3. Experimental PC field construction: a) Excavation, b) Layer of coarse aggregate, c) Slab mold installation, d) Compacting roller and e) Slabs cure. 3
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3.1. First experimental phase
As shown in Fig. 5 it was necessary to standardize a setup in order to simulate the clogging process, taking into account the following items:
To obtain the hydraulic characterization (performance) of the ma terial two tests were performed: porosity and permeability, both in laboratory conditions, these tests is described below.
� Be practical and easy to perform; � Be able to reproduce an immediate and gradual clogging; � Be able to measure the variation of permeability coefficient as a function of sediment concentration; � Be easily reproduced.
3.1.1. Porosity (ɸ) To determine the porosity (ɸ) of the specimens the test described by the standard ASTM C1754 was conducted. This method consists in weighing the specimens dry and immersed in water. To determine the porosity Eq. (1) is used considering the water density (ρw), the di mensions of the specimen (diameter (D) and length (L), respectively), and the constant K (equal to 1,274,240 for SI). � � � � �� K⋅ðA BÞ Φ % ¼ 1 ⋅ 100 (1) 2 ρw ⋅D ⋅L
It was adapted the constant head permeability test to evaluate the clogging process in laboratory conditions was planned the second experimental phase as shown in Fig. 5.
In that regard, the constant head permeability test was adapted as shown in Fig. 6 a. The adaptation of the tests consisted in the addition of sediments to determine the permeability variation as a function of the type and concentration of sediment. Notice that the addition of the sediments consisted in dry method where sediments were introduced manually as shown in Fig. 6 b. Then the constant head permeability test was carried out again to study how the sediments affected the result (Fig. 6c). As Shown in Fig. 6a, the permeability of the PC was measured with the intention of determine the initial permeability of the material (Fig. 6a). Then the sediment (S, C and S þ C, respectively) was added homogeneously to the surface of the specimen (in dry form) and thus clog the material (Fig. 6b), and finally the permeability test with the clogged material was repeated to determine the variation of the permeability (Fig. 6c). It was used a total mass of sediment of 100g this amount with the intention of representing 20 years of material exposition (rain and wind) [16,48], this total mass of sediment was divided into the surface area of the specimen (100 mm of diameter) to generalize and get compare the results with other authors, this value was 1.27 g/cm2 and it was defined as sediment concentration. According to the literature review, clogging rate on the PC was evaluated fast (large amounts of sediment) and slow (small amounts of sediment) [16,28], however, it has not been determined which of the clogging rates would be more critical for permeability reduction. In that sense, the clogging test was divided into three phases: Firstly, the sediment was divided into 1/10 of the total concentration (low clogging rate), then it was divided into 1/5 (medium rate) and finally into ½ (fast rate), and thus determine the influence of clogging rate on PC permeability reduction, and verify which scenario would be most critical for the material. The three clogging rates chosen are associated with precipitation scenarios, low, medium and high, since depending on the intensity of the rain this can bring with it a greater amount of sediment due to soil erosion (more intense rains transport more sediment), and these sedi ments transported in the surface runoff would reach the surface of the of PC damaging its hydraulic properties in greater or lesser magnitude
Fig. 4. Constant head permeability test setup.
Fig. 5. Flowchart Second experimental phase.
3.1.2. Permeability The constant head permeability test was used to assess the perme ability of PC. This is supported by the American Concrete Institute (ACI) as it is a function of the porosity and void size [6,8–10]. This test measures the output flow at different times. Fig. 4 shows the per meameter (one-dimensional flow) used during the test suggested by Sandoval et al. (2017). This figure presents from the top to the bottom: the water inlet; the overflow drainpipe; the water column (that in this case it presented a height of 230 mm); the cylindrical specimen, and the flow measurement. Vaseline was used in the upper and lower perimeter of the specimen and in the internal part of the threaded adapters to facilitate the as sembly and disassembly of the test. During the test, the time (t) that the water column needs to go through the sample is measured. The average coefficient permeability (k) was determined using Eq. (2), based on the Darcy’s law. In this study, h was constant for all tests, being equal to 430 mm. k¼
q⋅L A⋅h⋅t
(2)
A total of 48 specimens at an age of 14 days (curing days) were used to determine the permeability coefficient. The results were averages calculated with four permeability measurements. 3.2. Second experimental phase
4
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Fig. 6. Adaptation of the laboratory clogging test: a) Unclogged permeability b) Addition of sediment, c) Clogged Permeability.
depending on its quantity [19,49]. A total of 27 specimens at an age of 14 days (curing days) were used to determine the clogging phenomenon in PC (3 specimens by sediment by clogging rate). The average of permeability coefficient was calculated by four permeability measurements.
cm. In the second phase, the sediment was added at the critical clogging rate, and then the permeability coefficient of the material was checked. This procedure was repeated until the addition of sediment concentra tion (1.27 g/cm2) or until the minimum permeability of standard was reached (1 mm/s). A total of 9 slabs at an age of 14 days (curing days) were used to determine the clogging phenomenon in PC (3 slabs by sediment). The average of permeability coefficient was calculated by four permeability measurements.
3.3. Third experimental phase In second experimental phase was established the critical clogging rate for PC, then this rate was reproduced in the field and assessed the variation in permeability. Analogously to the laboratory it was chosen a permeability test to be adapted to determine the variation of the permeability as a function of sediment concentration. The permeability test adapted was the ASTM C1701 [28,50,51]. To perform the test, a 300 mm diameter-PVC tube was fixed on the surface of each slab with silicone in its outer and inner perimeter to avoid the water loss during the execution [52] (Fig. 7). A Volume of 3.60 L (L) of water filled up the tube, maintaining a water height of approximately 10 mm [34]. Similarly, to the second experimental Phase, the adaptation of the ASTM C1701 permeability test was divided into two phases. The first was to verify the permeability coefficient, calculating the time necessary to infiltrate a volume of 3.6 l of water, maintaining a constant head of 1
4. Results and discussions 4.1. First experimental phase Table 2 presents the results obtained performing the porosity test (Φ) and the constant head permeability test (k). The average was the results of four estimations. The standard deviation (StDev) and variation of results in percentage (CV) are presented as well. Notice that the values of k are considered the initial permeability of the PC in the clogging study (second experimental Phase). The average Φ (26.57%) and k (10.27 mm/s) coincide with the values found in the literature [6,8,31,53–57]. Besides, k is higher than the minimum value recommended by ACI 522R-10 (1.0 mm/s). On the other hand, the variability of results obtained are not significantly important due to the high intrinsic scatter of the PC [6,8,11]. 4.2. Second experimental phase During the execution of the clogging permeability test, 3 min were established for the stabilization of the mixture (water and sediment) to minimize the dispersion of results, this time was determined since before 3 min there were large variations of the permeability measure, due to Table 2 Hydraulics properties.
Fig. 7. ASTM C1701 setup. 5
Property
Φ (%)
k (mm/s)
Average StDev CV
26.57 2.61 9.82
10.27 1.91 18.57
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Journal of Building Engineering 29 (2020) 101203
the initial rearrangement of the sediment particles. Fig. 8 shows the average results obtained for the clogging test in laboratory conditions, considering the three clogging rates (slow (2), medium (5) and fast (10)) and the three types of sediment (S, C and S þ C, respectively). The coefficients of variation were in the range of 0.86–7.79%, meaning low variability of the results during the execution of the test. Notice that for the S þ C sediment with a concentration of 0.64 g/cm2 there was a false recovery of permeability, this can be explained because during the execution of the test when the water flow was released some particles may have undergone a process of rear rangement which allowed the increase in permeability. Regarding the results there was a tendency between the rate of the sediment and the reduction of permeability, for all cases when the rate of clogging was lower, there was a greater reduction in permeability, this is explained because there was an accumulated effect on the loss of permeability for the slow rate of clogging, derived from the rearrange ment of sediment particles and the entry of them into the internal pores, thus further storing the PC. In that sense, the results with slow rate present a reduction of permeability of 70%, 81% and 100% for S, C and S þ C, respectively. For the medium rate this reduction was 60%, 91% and 100% for S, C and S þ C, respectively. Finally, the fast rate entailed a reduction of 56%, 71% and 73% for S, C and S þ C, respectively. Notice that in case of C and S þ C, the maximum reductions of permeability (94% and 73%, fasts rates) were reached for a concentration of 0.76 g/cm2, and hence it was not possible to reach the maximum concentration of 1.27 g/cm2. These re sults were explained, because with the slow rate of clogging the sedi ment grains have more time to rearrange and fill the internal voids of the PC justifying the highest reductions in permeability. The results are enlightening that C and S þ C were more damaging to the PC than S. This trend is in accordance to what was found in the literature [29,33,58–61]. In that sense, the small size of the grains fa cilitates their entry into the voids. At the same time and due to the plasticity of the sediment particles, when they contact with the water they tend to agglomerate due to the effect of van der Waals forces, forming clay flocs that impedes the rapid passage of water through the PC, and therefore reducing its permeability immediately. For the clogging rates evaluated in laboratory conditions, ANOVA indicated with a p-value lower than 0.5% that in all cases the slow clogging rate (1/10) have a significantly different from the other two rates evaluated, medium and fast respectively. It is also clear that the sand sediment presents significant differences with the other two sedi ments used for all the clogging rates evaluated as shown in Fig. 9. During the performance of this test, the following situations were noted:
1) The time to stabilize the outflow varied for each type of sediment being around 2 min for S and 3 for C and S þ C, respectively; 2) Part of the sediment (non-significant) was lost by the overflow drain when the water started to run; 3) As shown in Fig. 10 due to the direction of the water flow during the execution of the test, there was a tendency to accumulate hetero geneously part of the sediment on the surface of the specimen. These situations can be controlled during the test, first by gradually releasing the flow of water, to avoid sediment loss and thus control the dispersion of results, however, the sediment losses noted during the execution of the trial were not significant. The accumulation of sediment was given by the size of the grains that do not get through the material and these have to accumulate in a specific place because of the flow direction of the water during the test. 4.3. Third experimental phase Fig. 11 shows the results (averages) of the clogging test carried out in field conditions, considering a slow clogging rate (critical rate defined in the second experimental Phase) and the three types of sediment (S, C and S þ C, respectively). The results presents S as the less damaging sediment, reaching a
Fig. 9. Boxplot Maximum Loss of Permeability for all clogging rates evaluated.
Fig. 8. Clogging results for S (a); C (b), and S þ C (c), respectively. 6
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Fig. 10. Non-homogeneous-sediment accumulation after disassembly clogging test in laboratory conditions: a) S, b) C and c) S þ C.
maximum reduction of 80% at a maximum concentration (1.27 g/cm2). On the other hand, both C and S þ C reached a maximum reduction of permeability equal to 95% at a concentration of 0.635 g/cm2 (Fig. 10). These results were consistent with those found in the laboratory. The only differences were that in laboratory conditions the water flow was unidirectional, and in the field there was no such restriction. Further more, in the laboratory the PC was tested independently and in the field the material interacts with its lower layers, justifying its increased loss of permeability. 5. Proposal of analytical model for clogging 5.1. Analytical model According to the literature [22,24,29] and the experimental results presented, during the clogging process, the permeability (k) decreases in function of the sediment concentration (n) as shown in Fig. 12. In that sense, the initial permeability (k0) reaches a certain residual perme ability (k∞) as the concentration of sediment brought by water increases. Eq. (4) expresses the reduction of permeability as a differential of permeability as a function of the variation of the quantity of sediment (∂k/∂n), considering a coefficient that defines the clogging potential of sediment (c). Solving the differential equation (Eq. (4)) based on the boundary conditions shown in Fig. 12, an analytical model for the clogging process for PC is proposed (Eq. (5)). This equation estimates the permeability of the clogged PC according to the concentration of sediment (kclog) based on clogging potential of sediment (c). Notice that c depends on both the concentration and the type of sediment, it presents units of cm2/g (which means the area in cm2 that can be potentially clogged by 1 g of sediment), and controls the shape of the curve (Fig. 13). Then, c gen erates different curves, starting from the initial permeability (k0) to the endpoint of residual permeability (k∞). Furthermore, factors such as the
Fig. 12. Relationship between k and n based on experimental results (adapted from Refs. [22,24,29]).
∂k ¼ ∂n
(4)
ck
type of sediment and its concentration make the curve more or less asymptotic with its horizontal axis. When c is close to zero, a high concentration of sediment is necessary to clog the PC, whereas higher values of c entail the need of a lower concentration of sediments to clog the material. kclog ¼ k∞ þ ðk0
k∞ Þ*e
c*n
(5)
Eq. (5) serves as an aid to identify the potential for clogging to which the PC will be subjected, according to the type of sediment, and thus be able to establish if possible periodicity of maintenance. The coefficient c shows the clogging potential which a gram of sediment has to clogs 1
Fig. 11. Clogging results for the experimental field test.
Fig. 13. Shape curve depending on c (c1 > c2 > c3). 7
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cm2 of PC, therefore when the value of c is low (close to zero), the clogging gradually decreases the permeability and when it is higher, it occurs practically immediately. Using Eq. (5) and all the experimental data of the laboratory and field clogging Phases, the extreme permeability values were determined (k∞), the clogging potential (c) for each type of sediment with the use of the IBM SPSS software.
Table 3 Values of c, R2 and k∞ found with IBM SPSS Statistics 19 software. Conditions
Sediment
c (cm2/g)
k∞ (mm/s)
R2
Laboratory
S 10 C 10 S þ C 10 S5 C5 SþC5 S2 C2 SþC2 S C SþC
1.423 3.175 2.734 0.796 1.869 1.758 0.704 1.649 1.559 5.969 10.489 10.883
1.409E-01 5.000E-09 4.000E-04 2.084E-03 2.261E-03 4.000E-04 5.000E-06 2.786E-03 9.135E-04 1.960E-01 8.100E-02 8.800E-02
0.995 0.973 0.966 0.992 0.982 0.994 0.990 0.984 0.996 0.996 0.999 0.998
5.2. Validation Firstly, Eq. (5) was used with the experimental results of the second and third experimental Phases were used to validate the analytical model (permeability reduction and sediment concentration) as input data, leaving residual permeability and clogging potential as unique variables. This data was used in a non-linear regression using the IBM SPSS software, this determined the variables and with these results Fig. 14 was plotted. Fig. 14a presents the curves obtained from the experimental results of laboratory conditions considering the three types of sediment (S, C and S þ C) and the three clogging rates studied (slow, medium and fast). On the other hand, Fig. 14b shows the curves ob tained with the experimental results of the field. As presented in Fig. 14, the analytical model adjusts well to the experimental data obtained, independently of the condition of the test (laboratory or Field). The adjustment curve shows a gradative reduction of the permeability as a function of the sediment concentration for all sediments, thus facilitating the forecast of the clogging as a function of the sediment concentration. Table 3 presents the values of clogging potential (c), residual permeability (k∞) and coefficient of determination (R2) obtained with the IBM SPSS software. The results consider the condition of the test (laboratory and field, respectively), the type of sediment and the rate of clogging. Regarding the values obtained for the laboratory conditions, the values of coefficient c found are in the range between 0.704 and 3.176 cm2/g. The highest values obtained (3.176 and 2.734 cm2/g) are pre sented by C 10 and S þ C 10, respectively, whereas the lower values are presented by the sand (S 2 presents a c equal to 0.704 cm2/g). In that sense, the smaller is the particle of the sediment and the slow rate of clogging (laboratory conditions), generated a higher coefficient c. Then, the results obtained, verified that the coefficient c depends on the type of sediment and the concentration of it. Besides, regarding the values ob tained for the field conditions, although c values were around five times
Field
higher, and the same trend was observed. These results were in accor dance with what was found in the experimental programs. Regarding the values of residual permeability (k∞), these are higher than zero independently of the type of sediment, as expected [35]. Finally, in all cases, Eq. (5) is significantly well-adjusted to the results obtained in the laboratory, showing an R2 greater than 0.966. On the other hand, the analytical model was evaluated using experimental data (permeability reduction and sediment concentration) from an extended literature review [1,11,12,14,15,18,20,36,37]. The permeability reduction was evaluated as a function of the sediment concentration. Sediment mass and clogging cycles were used in these investigations, then to facilitate the interpretation of the data, the con centration of sediment g/cm2 (relative to the surface area of each specimen) was calculated and all permeability values were converted into a percentage, being 100% always the initial permeability. Fig. 15 presents the adapted results (points) and the regression curves (lines) obtained with the IBM SPSS software. As shown in Fig. 15 the regression curve with Eq. (5) fits well with the data obtained by the literature reviews, the values of c for each one of the sediments used are shown in Table 4. Table 4 shows the c varied from 0.63 to 31.10 cm2/g. For low values of c, the clogging process is gradual, that is, the clogging process was more delayed. This wide range of c is due to the different types of sediment used by the authors, which is their majority used fine sedi ments to clog the PC. The coefficient c decreases representative when the sediment used was sand c¼0.63 and increases when sediment used was fine c¼31.10. In all cases the use of fine sediment decreased the
Fig. 14. Validation of the analytical model: a) Laboratory conditions and b) Field conditions. 8
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layers in the permeable pavement leads to a reduction in the perme ability of the PC since it is adjusted with the permeabilities of the lower layers. This is vital for the implementation of a project and that average permeability of the entire system is to be contemplated for hydraulic operation. In both cases the sediment of sand has the lowest value and the fine sediments have the highest values, therefore, there is a corre lation between the two tests in order to carry out a quality control in the field. 7. Conclusions The main conclusion from this study is the identification and char acterization of the phenomenon of clogging of PC considering both laboratory and field by means of an analytical model presented in Eq. (5). The model estimates the permeability of the clogged PC based on a new parameter defined as potential of clogging of PC (c). This model may be used to predict when the clogging phenomenon reaches values higher than expected and therefore, when engineers need to maintain the PC structures. Apart from that, other specific conclusions are extracted from the document:
Fig. 15. Validation of the analytical model based on results from a litera ture review. Table 4 c values for literature review results. Reference
c (cm2/g)
k∞ (mm/s)
R2
(Deo et al., 2010) (Haselbach 2010) (Tong 2011) (Sriravindrarajah et al., 2011) (Coughlin et al., 2012) (Mata e Leming 2012) (Walsh et al., 2014) (Fwa et al., 2014)
0.63 7.49 2.04 4.12 1.15 1.55 12.47 31.10
5.140E-01 4.000E-03 1.000E-04 4.500E-04 4.400E-05 6.200E-04 1.400E-05 1.260E-01
0.990 1.000 0.970 1.000 0.881 1.000 0.998 0.918
� It was possible to adapt the two permeability test methods to simu late the clogging process in the PC (constant head permeability test and the test defined by ASTM C1701 for laboratory and field con ditions, respectively) efficiently to evaluate the variation of perme ability in function of the increase of the concentration of sediment; � Regarding the test procedures, the constant head permeability test shows sediment loss during its execution, while the test defined by ASTM C1701 does not. The loss of material is not significant and does not affect the results, which present same tendencies for any of the test carried out. Notice, that the sediment losses observed in the constant head permeability test are reduced by releasing the water flow gradually without causing large turbulence; � Results show that the fine sediment (clay and S þ C) is more damaging in terms of the reduction of permeability because of the smaller size of the grain. Besides, its plasticity affects the perme ability due to the Van der Waal forces when the clay particles come together to form clay flocks. That is to say, in the evaluated condi tions the clogging process was controlled by the type of sediment; � The proposed analytical model is adjusted to the behavior of the material and is a useful tool to identify the clogging potential (c) and the final permeability the PC can achieve (k∞), and predict with which concentration of sediment the PC stops to be a pervious material. � In this study, laboratory and field results maintain the proportion 1 to 4. This proportion is important to understand the decrease in permeability that the PC has in the field, which justifies that the clogging phenomenon is more accentuated, therefore the perme ability in the laboratory must be adjusted with the field values. � The phenomenon of clogging is something inevitable in the PC, therefore having knowledge that in which phase is the loss of permeability is vital to establish the possible corrective measures of hydraulic recovery. � The proposed model will allow to foresee the useful life of the PC in function of the permeability degradation, as well as to establish at what moment a cleaning methodology should be executed to main tain the permeable material.
permeability in an accelerated way as expected, and finally, the k∞ was different from zero, indicating that there will always be a remnant permeability of the material. The values of R2 were also verified, which were good (>0.98), representing a good curve fit to the data studied. 6. Comparison laboratory and field Since the PC is going to function as a permeable pavement system, it is important to identify the possible proportion that exists between the PC independently and when it interacts with the lower layers, in order to facilitate quality control in the field with laboratory tests at a lower scale. Then the results obtained in laboratory and field were compared in order to verify the proportion of the results and analyze how the interaction of the permeable pavement layers affects the clogging phe nomenon. The values of c and R2 obtained for a low rate of clogging in the laboratory and the experimental field are shown in Table 5. As shown in Table 5 the ratio of laboratory parameters c and experimental field was almost 1:4, this was explained, because in the laboratory conditions the permeability was measured independently in the PC, and in the experimental field it was measured in the pervious pavement system (PC, granular layer, subgrade). The interaction of the Table 5 Summary results of laboratory and Experimental Field for low clogging rate. Sediment
Laboratory
Experimental Field
clab
R2
k∞
cf
R2
k∞
Sand
1.423
0.995
5.969
0.996
Clay
3.175
0.987
10.489
0.999
SþC
2.734
0.999
1.409E01 5.000E09 4.000E04
10.883
0.998
1.960E01 8.100E02 8.800E02
Declaration of competing interest
cf/ clab
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
4.19 3.30 3.98
9
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Acknowledgments
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Hydrol 565 (2018) 589–598, https://doi. org/10.1016/j.jhydrol.2018.08.072. [37] A. Osman Akan, Horton Infiltration Equation Revisited, J. Irrigat. Drain. Eng. 118 (1992) 828–830. [38] A.I. Neptune, B.J. Putman, Effect of aggregate size and gradation on pervious concrete mixtures, ACI Mater. J. 107 (6) (2010) 627–633. [39] G.F.B. Sandoval, DESEMPENHO DO CONCRETO POROSO COM AGREGADOS � SUSTENTAVEIS, Universidade Estadual de Londrina, 2014. http://www.uel. br/pos/enges/portal/pages/arquivos/dissertacao/GERSSON BARRETO SANDOVAL 2013-1.pdf. � c, L. Korat, V. Ducman, I. Netinger, Influence of aggregate type and size on [40] K. Cosi� properties of pervious concrete, Construct. Build. Mater. 78 (2015) 69–76, https:// doi.org/10.1016/j.conbuildmat.2014.12.073. [41] F. Gonçalves, C.H.U. de Souza, F.S. Tahira, F. Fernandes, R.S. Teixeira, Incremento de lodo de ETA em barreiras ipermeabilizantes de aterro sanit?rio, Rev. DAE. 65 (2017) 5–14, https://doi.org/10.4322/dae.2016.018. 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The authors would like to thank postgraduate program of doctorate in civil engineering of the State University of Londrina and its laboratory of construction materials. Besides, the first author thanks the economic support provided by CAPES during the development of this research. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.jobe.2020.101203. Author statement The functions of each of the authors of this Paper are listed below: � Gersson F. B. Sandoval: lead author, experimental planning, executor of the experimental phases, data processing and clogging model. � Isaac Galobardes Reyes: Collaboration in the analysis of results and writing of the document. � Andr� e Campos de Moura: experimental logistics, experimental planning and clogging model. � Berenice Martins Toralles: experimental planning, review of stages, analysis of results.
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Journal of Building Engineering journal homepage: http://www.elsevier.com/locate/jobe
Assessing the phenomenon of clogging of pervious concrete (Pc): Experimental test and model proposition Gersson F.B. Sandoval a, *, Isaac Galobardes b, Andr�e Campos c, Berenice.M. Toralles a a
State University of Londrina, Civil Construction Department, State University of Londrina, Celso Garcia Road Pr 445 km 380, Londrina, Paran� a, Brazil School of Architecture, Planning and Design. Mohammed VI Polytechnic University. Lot 660, Hay Moulay Rachid, Ben Guerir, 43150, Morocco c State University of Londrina, Structures Department, State University of Londrina, Celso Garcia Road Pr 445 km 380, Londrina, Paran� a, Brazil b
A R T I C L E I N F O
A B S T R A C T
Keywords: Pervious concrete Permeability Porosity Clogging Sediments concentration Service life
During rainfall, different types of sediments are transported (organic and inorganic) that end up prejudicing the hydraulic efficiency of materials such as pervious concrete (PC). Sediments tend to accumulate on the surface of the material and its internal pore structure causing a decrease in permeability. This phenomenon is known as clogging. This study aims to identify and characterize the phenomenon of clogging of PC considering laboratory and field conditions. In that sense, a PC mix and three types of sediment (sand (S), clay (C), and mixture of both (S þ C)) were considered. Two types of permeability tests were adapted to induce the clogging and estimate the permeability reduction: the constant head test (laboratory), and the ASTM C1701 (field). Finally, different rates of clogging (low, medium and high) were considered. Considering the type of sediments, the finer they are the higher is the influence on the clogging phenomenon, reaching maximum permeability reductions greater than 95%. Considering the rate of clogging, the results show that low rate entails higher clogging. On the other hand, an analytical model to describe the clogging was proposed. This predicts efficiently the reduction of permeability considering the type and concentration of sediment. This model was proposed with experimental data obtained and validated with results from other investigations. The model aims to be used for engineers to design PC considering their life span and maintenance.
1. Introduction The urban development, added to the lack of planning, brings as a direct consequence the waterproofing of the soil. This is directly related to the pavement of streets and the civil construction industry [1,2]. This waterproofing and the inefficiency of the existing drainage systems in certain countries, increases the potential of floods during a precipitation event, creating numerous problems for the civilian population. In response to this type of problem, researchers focused on finding new materials that allow the passage of water through its structure without changing the mechanical properties. One of these materials is the pervious concrete (PC) [3–5]. PC is a special concrete with high porosity (15–30%), and therefore high permeability (>1 mm/s) [1,3,4,6–10] which is calculated by the permeability tests at falling head and at constant head permeability test. In that sense, the permeability of the material is related to the effective porosity, the grading of the coarse aggregate and the quantity of paste in
the mix proportions [7,11–15]. Due to the large voids, PC is subjected to the accumulation of different types of materials (sediments) inside the voids leading to a reduction of permeability. These sediments are generally transported during rainfall events. During a rainfall event, the flow of rainwater takes suspended ma terial (sediments), which are usually grains of sand, silt and clay, that by their small size facilitate their transport [16–18]. The transport of sed iments may be so critical that, for example, in winter season, the Yellow river (China) carries as much suspended material as water [19]. The accumulation of sediments and the reduction of permeability in the PC is known as clogging [20]. This phenomenon is the process in which the internal voids are partially or totally filled by sediments [20–27]. Then, sediments decrease the cross section and interconnection of voids causing an increase in tortuosity, decreasing the permeability and affecting the durability and functionality of PC over time [28–30]. There are few studies related to the phenomenon of PC clogging. This was studied recreating extreme cases where sediment concentrations
* Corresponding authors. E-mail addresses: [email protected] (G.F.B. Sandoval), [email protected] (I. Galobardes), [email protected] (A. Campos), [email protected] (Berenice.M. Toralles). https://doi.org/10.1016/j.jobe.2020.101203 Received 12 August 2019; Received in revised form 14 January 2020; Accepted 17 January 2020 Available online 21 January 2020 2352-7102/© 2020 Elsevier Ltd. All rights reserved.
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immediately impair the permeability of the material or considering a final permeability equal to 0 [18,23,24,29,31–36]. Experimental results allowed that this never takes place, therefore there is a lack of knowl edge about how this phenomenon is presented when it occurs progressively. In general, the sediments commonly used to induce clogging in the PC are coarse-grains (sands) and fine grains (clays). These are the most susceptible soils to generate a reduction of the permeability of the ma terial [16–18]. One of the greatest difficulties to evaluate the clogging is that there is no test method to do it, therefore previous investigations adapted the existent permeability tests (both laboratory and field test) to induce the addition of sediment and measure the variation of perme ability [29]. However, the test methodology is not clear. The reliability in its repetition is not guaranteed since important data such as the area of the cross-section of the specimen and the total mass of sediment used are not considered. Then, it is important developing a standardized methodology that allows a recreation of the phenomenon of clogging and guarantees its reliable repetition. Moreover, as the clogging of the PC has been carried out in labora tory and field conditions independently [25], the possible correlation that may exist under these two conditions is unknown. That fact limits the conclusions to each case in particular. In the same way, empirical correlations using exponential equations were proposed to correlate the mass of sediment and the variation of permeability [18] derived from mathematical studies carried out in the area of infiltration in hydrology [37]. However, these correlations were not completely validated. In that context, to give an answer to the existing gaps in the knowledge of the phenomenon of the clogging in the PC an experimental program was defined aiming: a) Induce clogging in the PC with three clogging rates (slow, medium and fast) with three types of sediments (sand, clay, and mixture of both) in the laboratory, b) Induce clogging in the PC with slow rate with three types of sediments (sand, clay, and mixture of both) in the field, and c) Propose and validate an analytical clogging model for PC based on the results found and other researchers’ results.
the material. In that sense, investigations have been developed to un derstand more about the hydraulic degradation of the material, however there have been no proposed methodologies that can be standardized and models that can be used to predict the behavior of the material in a simple way. That is why this aims to contemplates the standardization of a simple test methodology that simulates the degradation of PC under different conditions and proposes a method of forecasting how these properties can vary depending on where the material is used. 2. Experimental program The experimental program was divided into three experimental phases as shown in Fig. 1. The first one aimed to characterize the PC hydraulically and to obtain the initial permeability of the PC; the second to adapt the constant head permeability test to evaluate the clogging phenomenon in laboratory conditions, and the third one to adapt the ASTM C1701 to study the phenomenon of clogging in the field. So with the results obtained in these three experimental programs (phases) a clogging model for PC can be proposed. In this section, the materials and test methods used to fulfill the objectives are explained. 2.1. Materials and mix proportions In this study a Portland Cement type II and one type of basalt aggregate (B) were used. The maximum size of the aggregate was 9.50 mm. This was obtained from a quarry in the city of Londrina (Brazil) and it is the most common aggregate size to make PC according to the literature review [38–40]. On the other hand, in order to determine the phenomenon of clogging of PC, three types of sediments were used (sand (S), clay (C) and the mixture of the two (S þ C)), the sand sediments were chosen because in existing structures of pervious concrete (pavers) have been the materials that have filled the internal pores of the material damaging its permeability [17], additionally the clay sediments for � [41]. being the most common type of soil in the city of Londrina, Parana The specific gravity; material finer than 75 μm (%); water absorption, and unit weight of the aggregate and sediments are presented in Table 1. Notice that these results will be used to establish the mix design of the PC. Fig. 2 shows the grading of B, S and C, respectively. B presents a uniform grading, since its grains are basically distributed in two sizes, 71% for 4.80 mm, and 29% for 2.40 mm. In that regard, this grading is recommended by the ACI 522R-10 to produce PC [44]. The uniformity
1.1. Research significance One of the great limitations that the diffusion and correct use of the PC has had is the ignorance of its hydraulic performance during its useful life, added to the fact that the current norms do not contemplate a methodology to evaluate the degradation of the hydraulic properties of
Fig. 1. Experimental program. 2
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To study the phenomenon of clogging in laboratory conditions, a total of 48 cylindrical specimens (100φ x 200 mm) was fabricated. The compacting process for the cylindrical specimens was performed with a standard Proctor hammer (steel cylinder with 50 mm in diameter and 305 mm tall) with 2.50 kg of mass on top of the fresh concrete. The PC was compacted with ten drops in a single layer. All specimens were compacted by the same operator to minimize the variability. The spec imens were demolded 24 h after and cured in water with 99% humidity and temperature of 21 � C until the age of test. To study the phenomenon of clogging in the field, an excavation of 2.50 m long, 1.00 m wide and 0.45 m deep (Fig. 3a), was dug outside the laboratory. A 0.30 m-layer of coarse aggregate with a diameter of 19 mm was placed above the subgrade (Fig. 3b). This layer was made to guar antee the water storage conditions of the PC [47]. Finally, on top of this the 10 square slabs of PC were molded (0.45 x 0.45 x 0.10 m) (Fig. 3c). The slabs were compacted with a metal roller of 13.20 kg of weight (Fig. 3d). The roller passed 10 times (round trip) in a single layer per slab since this number of passes guaranteed the same density obtained in the laboratory (1890 kg/m3). Finally, the curing of the slabs was carried out by irrigation for 7 days, and they were covered with wet tarps to reduce excessive water loss (Fig. 3e).
Table 1 Main features of the aggregates used in the study. Type of aggregate
B S C
Specific gravity
Materials finer than 75 μm (%)
Water Absorption (%)
Unit weight (g/cm3)
NBR NM 53/ 2003 [42]
NBR NM 46/ 2006 [43]
NBR NM 53/ 2003 [42]
NBR NM 53/2003 [42]
2.72 2.49 3.03
0.51 2.00 –
1.4 5.0 –
1.45 1.65 –
coefficient (Cu) for this grading is 2 confirming the uniformity of its grains (values lower than 5 are uniform [45,46]). On the other hand, S presented a continuous grading (Cu of 4.75) and grain size varying from 4.80 mm to 0.15 mm. It has an FMS (Fineness modulus of sand) of 2.7 being classified as coarse sand Finally, the grading of C presented 55% of particles of clay size, 23% of silty size and 21% of fine sand [26]. This sediment has a liquid limit (wL) of 52% and plastic limit (wP) of 38% being common values of clay [26,30]. In this study additions or additives were not incorporated into the mixture, and mix proportions were taken from the study conducted by Sandoval (2017) [6]. The mix proportion presents the following char acteristics per cubic meter: the mass of cement was designated to be 420 kg; the mass of B was 1369 kg, and the w/c ratio was selected to be 0.34.
3. Experimental phases The experimental work was divided into three experimental phases, the first phase with the objective to the hydraulic characterization of the PC, the second phase with the objective to evaluate the clogging in PC in laboratory conditions, and the third phases with the objective of eval uating the clogging of PCs at the field.
2.2. Fabrication and curing of specimens The materials were produced in a standard mixer with a capacity of 150 L. The mixing process was made according to Sandoval et al. (2017) recommendation. Therefore, the total mixing time was 5 min.
Fig. 2. Grading of B, S, C and S þ C.
Fig. 3. Experimental PC field construction: a) Excavation, b) Layer of coarse aggregate, c) Slab mold installation, d) Compacting roller and e) Slabs cure. 3
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3.1. First experimental phase
As shown in Fig. 5 it was necessary to standardize a setup in order to simulate the clogging process, taking into account the following items:
To obtain the hydraulic characterization (performance) of the ma terial two tests were performed: porosity and permeability, both in laboratory conditions, these tests is described below.
� Be practical and easy to perform; � Be able to reproduce an immediate and gradual clogging; � Be able to measure the variation of permeability coefficient as a function of sediment concentration; � Be easily reproduced.
3.1.1. Porosity (ɸ) To determine the porosity (ɸ) of the specimens the test described by the standard ASTM C1754 was conducted. This method consists in weighing the specimens dry and immersed in water. To determine the porosity Eq. (1) is used considering the water density (ρw), the di mensions of the specimen (diameter (D) and length (L), respectively), and the constant K (equal to 1,274,240 for SI). � � � � �� K⋅ðA BÞ Φ % ¼ 1 ⋅ 100 (1) 2 ρw ⋅D ⋅L
It was adapted the constant head permeability test to evaluate the clogging process in laboratory conditions was planned the second experimental phase as shown in Fig. 5.
In that regard, the constant head permeability test was adapted as shown in Fig. 6 a. The adaptation of the tests consisted in the addition of sediments to determine the permeability variation as a function of the type and concentration of sediment. Notice that the addition of the sediments consisted in dry method where sediments were introduced manually as shown in Fig. 6 b. Then the constant head permeability test was carried out again to study how the sediments affected the result (Fig. 6c). As Shown in Fig. 6a, the permeability of the PC was measured with the intention of determine the initial permeability of the material (Fig. 6a). Then the sediment (S, C and S þ C, respectively) was added homogeneously to the surface of the specimen (in dry form) and thus clog the material (Fig. 6b), and finally the permeability test with the clogged material was repeated to determine the variation of the permeability (Fig. 6c). It was used a total mass of sediment of 100g this amount with the intention of representing 20 years of material exposition (rain and wind) [16,48], this total mass of sediment was divided into the surface area of the specimen (100 mm of diameter) to generalize and get compare the results with other authors, this value was 1.27 g/cm2 and it was defined as sediment concentration. According to the literature review, clogging rate on the PC was evaluated fast (large amounts of sediment) and slow (small amounts of sediment) [16,28], however, it has not been determined which of the clogging rates would be more critical for permeability reduction. In that sense, the clogging test was divided into three phases: Firstly, the sediment was divided into 1/10 of the total concentration (low clogging rate), then it was divided into 1/5 (medium rate) and finally into ½ (fast rate), and thus determine the influence of clogging rate on PC permeability reduction, and verify which scenario would be most critical for the material. The three clogging rates chosen are associated with precipitation scenarios, low, medium and high, since depending on the intensity of the rain this can bring with it a greater amount of sediment due to soil erosion (more intense rains transport more sediment), and these sedi ments transported in the surface runoff would reach the surface of the of PC damaging its hydraulic properties in greater or lesser magnitude
Fig. 4. Constant head permeability test setup.
Fig. 5. Flowchart Second experimental phase.
3.1.2. Permeability The constant head permeability test was used to assess the perme ability of PC. This is supported by the American Concrete Institute (ACI) as it is a function of the porosity and void size [6,8–10]. This test measures the output flow at different times. Fig. 4 shows the per meameter (one-dimensional flow) used during the test suggested by Sandoval et al. (2017). This figure presents from the top to the bottom: the water inlet; the overflow drainpipe; the water column (that in this case it presented a height of 230 mm); the cylindrical specimen, and the flow measurement. Vaseline was used in the upper and lower perimeter of the specimen and in the internal part of the threaded adapters to facilitate the as sembly and disassembly of the test. During the test, the time (t) that the water column needs to go through the sample is measured. The average coefficient permeability (k) was determined using Eq. (2), based on the Darcy’s law. In this study, h was constant for all tests, being equal to 430 mm. k¼
q⋅L A⋅h⋅t
(2)
A total of 48 specimens at an age of 14 days (curing days) were used to determine the permeability coefficient. The results were averages calculated with four permeability measurements. 3.2. Second experimental phase
4
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Fig. 6. Adaptation of the laboratory clogging test: a) Unclogged permeability b) Addition of sediment, c) Clogged Permeability.
depending on its quantity [19,49]. A total of 27 specimens at an age of 14 days (curing days) were used to determine the clogging phenomenon in PC (3 specimens by sediment by clogging rate). The average of permeability coefficient was calculated by four permeability measurements.
cm. In the second phase, the sediment was added at the critical clogging rate, and then the permeability coefficient of the material was checked. This procedure was repeated until the addition of sediment concentra tion (1.27 g/cm2) or until the minimum permeability of standard was reached (1 mm/s). A total of 9 slabs at an age of 14 days (curing days) were used to determine the clogging phenomenon in PC (3 slabs by sediment). The average of permeability coefficient was calculated by four permeability measurements.
3.3. Third experimental phase In second experimental phase was established the critical clogging rate for PC, then this rate was reproduced in the field and assessed the variation in permeability. Analogously to the laboratory it was chosen a permeability test to be adapted to determine the variation of the permeability as a function of sediment concentration. The permeability test adapted was the ASTM C1701 [28,50,51]. To perform the test, a 300 mm diameter-PVC tube was fixed on the surface of each slab with silicone in its outer and inner perimeter to avoid the water loss during the execution [52] (Fig. 7). A Volume of 3.60 L (L) of water filled up the tube, maintaining a water height of approximately 10 mm [34]. Similarly, to the second experimental Phase, the adaptation of the ASTM C1701 permeability test was divided into two phases. The first was to verify the permeability coefficient, calculating the time necessary to infiltrate a volume of 3.6 l of water, maintaining a constant head of 1
4. Results and discussions 4.1. First experimental phase Table 2 presents the results obtained performing the porosity test (Φ) and the constant head permeability test (k). The average was the results of four estimations. The standard deviation (StDev) and variation of results in percentage (CV) are presented as well. Notice that the values of k are considered the initial permeability of the PC in the clogging study (second experimental Phase). The average Φ (26.57%) and k (10.27 mm/s) coincide with the values found in the literature [6,8,31,53–57]. Besides, k is higher than the minimum value recommended by ACI 522R-10 (1.0 mm/s). On the other hand, the variability of results obtained are not significantly important due to the high intrinsic scatter of the PC [6,8,11]. 4.2. Second experimental phase During the execution of the clogging permeability test, 3 min were established for the stabilization of the mixture (water and sediment) to minimize the dispersion of results, this time was determined since before 3 min there were large variations of the permeability measure, due to Table 2 Hydraulics properties.
Fig. 7. ASTM C1701 setup. 5
Property
Φ (%)
k (mm/s)
Average StDev CV
26.57 2.61 9.82
10.27 1.91 18.57
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the initial rearrangement of the sediment particles. Fig. 8 shows the average results obtained for the clogging test in laboratory conditions, considering the three clogging rates (slow (2), medium (5) and fast (10)) and the three types of sediment (S, C and S þ C, respectively). The coefficients of variation were in the range of 0.86–7.79%, meaning low variability of the results during the execution of the test. Notice that for the S þ C sediment with a concentration of 0.64 g/cm2 there was a false recovery of permeability, this can be explained because during the execution of the test when the water flow was released some particles may have undergone a process of rear rangement which allowed the increase in permeability. Regarding the results there was a tendency between the rate of the sediment and the reduction of permeability, for all cases when the rate of clogging was lower, there was a greater reduction in permeability, this is explained because there was an accumulated effect on the loss of permeability for the slow rate of clogging, derived from the rearrange ment of sediment particles and the entry of them into the internal pores, thus further storing the PC. In that sense, the results with slow rate present a reduction of permeability of 70%, 81% and 100% for S, C and S þ C, respectively. For the medium rate this reduction was 60%, 91% and 100% for S, C and S þ C, respectively. Finally, the fast rate entailed a reduction of 56%, 71% and 73% for S, C and S þ C, respectively. Notice that in case of C and S þ C, the maximum reductions of permeability (94% and 73%, fasts rates) were reached for a concentration of 0.76 g/cm2, and hence it was not possible to reach the maximum concentration of 1.27 g/cm2. These re sults were explained, because with the slow rate of clogging the sedi ment grains have more time to rearrange and fill the internal voids of the PC justifying the highest reductions in permeability. The results are enlightening that C and S þ C were more damaging to the PC than S. This trend is in accordance to what was found in the literature [29,33,58–61]. In that sense, the small size of the grains fa cilitates their entry into the voids. At the same time and due to the plasticity of the sediment particles, when they contact with the water they tend to agglomerate due to the effect of van der Waals forces, forming clay flocs that impedes the rapid passage of water through the PC, and therefore reducing its permeability immediately. For the clogging rates evaluated in laboratory conditions, ANOVA indicated with a p-value lower than 0.5% that in all cases the slow clogging rate (1/10) have a significantly different from the other two rates evaluated, medium and fast respectively. It is also clear that the sand sediment presents significant differences with the other two sedi ments used for all the clogging rates evaluated as shown in Fig. 9. During the performance of this test, the following situations were noted:
1) The time to stabilize the outflow varied for each type of sediment being around 2 min for S and 3 for C and S þ C, respectively; 2) Part of the sediment (non-significant) was lost by the overflow drain when the water started to run; 3) As shown in Fig. 10 due to the direction of the water flow during the execution of the test, there was a tendency to accumulate hetero geneously part of the sediment on the surface of the specimen. These situations can be controlled during the test, first by gradually releasing the flow of water, to avoid sediment loss and thus control the dispersion of results, however, the sediment losses noted during the execution of the trial were not significant. The accumulation of sediment was given by the size of the grains that do not get through the material and these have to accumulate in a specific place because of the flow direction of the water during the test. 4.3. Third experimental phase Fig. 11 shows the results (averages) of the clogging test carried out in field conditions, considering a slow clogging rate (critical rate defined in the second experimental Phase) and the three types of sediment (S, C and S þ C, respectively). The results presents S as the less damaging sediment, reaching a
Fig. 9. Boxplot Maximum Loss of Permeability for all clogging rates evaluated.
Fig. 8. Clogging results for S (a); C (b), and S þ C (c), respectively. 6
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Fig. 10. Non-homogeneous-sediment accumulation after disassembly clogging test in laboratory conditions: a) S, b) C and c) S þ C.
maximum reduction of 80% at a maximum concentration (1.27 g/cm2). On the other hand, both C and S þ C reached a maximum reduction of permeability equal to 95% at a concentration of 0.635 g/cm2 (Fig. 10). These results were consistent with those found in the laboratory. The only differences were that in laboratory conditions the water flow was unidirectional, and in the field there was no such restriction. Further more, in the laboratory the PC was tested independently and in the field the material interacts with its lower layers, justifying its increased loss of permeability. 5. Proposal of analytical model for clogging 5.1. Analytical model According to the literature [22,24,29] and the experimental results presented, during the clogging process, the permeability (k) decreases in function of the sediment concentration (n) as shown in Fig. 12. In that sense, the initial permeability (k0) reaches a certain residual perme ability (k∞) as the concentration of sediment brought by water increases. Eq. (4) expresses the reduction of permeability as a differential of permeability as a function of the variation of the quantity of sediment (∂k/∂n), considering a coefficient that defines the clogging potential of sediment (c). Solving the differential equation (Eq. (4)) based on the boundary conditions shown in Fig. 12, an analytical model for the clogging process for PC is proposed (Eq. (5)). This equation estimates the permeability of the clogged PC according to the concentration of sediment (kclog) based on clogging potential of sediment (c). Notice that c depends on both the concentration and the type of sediment, it presents units of cm2/g (which means the area in cm2 that can be potentially clogged by 1 g of sediment), and controls the shape of the curve (Fig. 13). Then, c gen erates different curves, starting from the initial permeability (k0) to the endpoint of residual permeability (k∞). Furthermore, factors such as the
Fig. 12. Relationship between k and n based on experimental results (adapted from Refs. [22,24,29]).
∂k ¼ ∂n
(4)
ck
type of sediment and its concentration make the curve more or less asymptotic with its horizontal axis. When c is close to zero, a high concentration of sediment is necessary to clog the PC, whereas higher values of c entail the need of a lower concentration of sediments to clog the material. kclog ¼ k∞ þ ðk0
k∞ Þ*e
c*n
(5)
Eq. (5) serves as an aid to identify the potential for clogging to which the PC will be subjected, according to the type of sediment, and thus be able to establish if possible periodicity of maintenance. The coefficient c shows the clogging potential which a gram of sediment has to clogs 1
Fig. 11. Clogging results for the experimental field test.
Fig. 13. Shape curve depending on c (c1 > c2 > c3). 7
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cm2 of PC, therefore when the value of c is low (close to zero), the clogging gradually decreases the permeability and when it is higher, it occurs practically immediately. Using Eq. (5) and all the experimental data of the laboratory and field clogging Phases, the extreme permeability values were determined (k∞), the clogging potential (c) for each type of sediment with the use of the IBM SPSS software.
Table 3 Values of c, R2 and k∞ found with IBM SPSS Statistics 19 software. Conditions
Sediment
c (cm2/g)
k∞ (mm/s)
R2
Laboratory
S 10 C 10 S þ C 10 S5 C5 SþC5 S2 C2 SþC2 S C SþC
1.423 3.175 2.734 0.796 1.869 1.758 0.704 1.649 1.559 5.969 10.489 10.883
1.409E-01 5.000E-09 4.000E-04 2.084E-03 2.261E-03 4.000E-04 5.000E-06 2.786E-03 9.135E-04 1.960E-01 8.100E-02 8.800E-02
0.995 0.973 0.966 0.992 0.982 0.994 0.990 0.984 0.996 0.996 0.999 0.998
5.2. Validation Firstly, Eq. (5) was used with the experimental results of the second and third experimental Phases were used to validate the analytical model (permeability reduction and sediment concentration) as input data, leaving residual permeability and clogging potential as unique variables. This data was used in a non-linear regression using the IBM SPSS software, this determined the variables and with these results Fig. 14 was plotted. Fig. 14a presents the curves obtained from the experimental results of laboratory conditions considering the three types of sediment (S, C and S þ C) and the three clogging rates studied (slow, medium and fast). On the other hand, Fig. 14b shows the curves ob tained with the experimental results of the field. As presented in Fig. 14, the analytical model adjusts well to the experimental data obtained, independently of the condition of the test (laboratory or Field). The adjustment curve shows a gradative reduction of the permeability as a function of the sediment concentration for all sediments, thus facilitating the forecast of the clogging as a function of the sediment concentration. Table 3 presents the values of clogging potential (c), residual permeability (k∞) and coefficient of determination (R2) obtained with the IBM SPSS software. The results consider the condition of the test (laboratory and field, respectively), the type of sediment and the rate of clogging. Regarding the values obtained for the laboratory conditions, the values of coefficient c found are in the range between 0.704 and 3.176 cm2/g. The highest values obtained (3.176 and 2.734 cm2/g) are pre sented by C 10 and S þ C 10, respectively, whereas the lower values are presented by the sand (S 2 presents a c equal to 0.704 cm2/g). In that sense, the smaller is the particle of the sediment and the slow rate of clogging (laboratory conditions), generated a higher coefficient c. Then, the results obtained, verified that the coefficient c depends on the type of sediment and the concentration of it. Besides, regarding the values ob tained for the field conditions, although c values were around five times
Field
higher, and the same trend was observed. These results were in accor dance with what was found in the experimental programs. Regarding the values of residual permeability (k∞), these are higher than zero independently of the type of sediment, as expected [35]. Finally, in all cases, Eq. (5) is significantly well-adjusted to the results obtained in the laboratory, showing an R2 greater than 0.966. On the other hand, the analytical model was evaluated using experimental data (permeability reduction and sediment concentration) from an extended literature review [1,11,12,14,15,18,20,36,37]. The permeability reduction was evaluated as a function of the sediment concentration. Sediment mass and clogging cycles were used in these investigations, then to facilitate the interpretation of the data, the con centration of sediment g/cm2 (relative to the surface area of each specimen) was calculated and all permeability values were converted into a percentage, being 100% always the initial permeability. Fig. 15 presents the adapted results (points) and the regression curves (lines) obtained with the IBM SPSS software. As shown in Fig. 15 the regression curve with Eq. (5) fits well with the data obtained by the literature reviews, the values of c for each one of the sediments used are shown in Table 4. Table 4 shows the c varied from 0.63 to 31.10 cm2/g. For low values of c, the clogging process is gradual, that is, the clogging process was more delayed. This wide range of c is due to the different types of sediment used by the authors, which is their majority used fine sedi ments to clog the PC. The coefficient c decreases representative when the sediment used was sand c¼0.63 and increases when sediment used was fine c¼31.10. In all cases the use of fine sediment decreased the
Fig. 14. Validation of the analytical model: a) Laboratory conditions and b) Field conditions. 8
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G.F.B. Sandoval et al.
layers in the permeable pavement leads to a reduction in the perme ability of the PC since it is adjusted with the permeabilities of the lower layers. This is vital for the implementation of a project and that average permeability of the entire system is to be contemplated for hydraulic operation. In both cases the sediment of sand has the lowest value and the fine sediments have the highest values, therefore, there is a corre lation between the two tests in order to carry out a quality control in the field. 7. Conclusions The main conclusion from this study is the identification and char acterization of the phenomenon of clogging of PC considering both laboratory and field by means of an analytical model presented in Eq. (5). The model estimates the permeability of the clogged PC based on a new parameter defined as potential of clogging of PC (c). This model may be used to predict when the clogging phenomenon reaches values higher than expected and therefore, when engineers need to maintain the PC structures. Apart from that, other specific conclusions are extracted from the document:
Fig. 15. Validation of the analytical model based on results from a litera ture review. Table 4 c values for literature review results. Reference
c (cm2/g)
k∞ (mm/s)
R2
(Deo et al., 2010) (Haselbach 2010) (Tong 2011) (Sriravindrarajah et al., 2011) (Coughlin et al., 2012) (Mata e Leming 2012) (Walsh et al., 2014) (Fwa et al., 2014)
0.63 7.49 2.04 4.12 1.15 1.55 12.47 31.10
5.140E-01 4.000E-03 1.000E-04 4.500E-04 4.400E-05 6.200E-04 1.400E-05 1.260E-01
0.990 1.000 0.970 1.000 0.881 1.000 0.998 0.918
� It was possible to adapt the two permeability test methods to simu late the clogging process in the PC (constant head permeability test and the test defined by ASTM C1701 for laboratory and field con ditions, respectively) efficiently to evaluate the variation of perme ability in function of the increase of the concentration of sediment; � Regarding the test procedures, the constant head permeability test shows sediment loss during its execution, while the test defined by ASTM C1701 does not. The loss of material is not significant and does not affect the results, which present same tendencies for any of the test carried out. Notice, that the sediment losses observed in the constant head permeability test are reduced by releasing the water flow gradually without causing large turbulence; � Results show that the fine sediment (clay and S þ C) is more damaging in terms of the reduction of permeability because of the smaller size of the grain. Besides, its plasticity affects the perme ability due to the Van der Waal forces when the clay particles come together to form clay flocks. That is to say, in the evaluated condi tions the clogging process was controlled by the type of sediment; � The proposed analytical model is adjusted to the behavior of the material and is a useful tool to identify the clogging potential (c) and the final permeability the PC can achieve (k∞), and predict with which concentration of sediment the PC stops to be a pervious material. � In this study, laboratory and field results maintain the proportion 1 to 4. This proportion is important to understand the decrease in permeability that the PC has in the field, which justifies that the clogging phenomenon is more accentuated, therefore the perme ability in the laboratory must be adjusted with the field values. � The phenomenon of clogging is something inevitable in the PC, therefore having knowledge that in which phase is the loss of permeability is vital to establish the possible corrective measures of hydraulic recovery. � The proposed model will allow to foresee the useful life of the PC in function of the permeability degradation, as well as to establish at what moment a cleaning methodology should be executed to main tain the permeable material.
permeability in an accelerated way as expected, and finally, the k∞ was different from zero, indicating that there will always be a remnant permeability of the material. The values of R2 were also verified, which were good (>0.98), representing a good curve fit to the data studied. 6. Comparison laboratory and field Since the PC is going to function as a permeable pavement system, it is important to identify the possible proportion that exists between the PC independently and when it interacts with the lower layers, in order to facilitate quality control in the field with laboratory tests at a lower scale. Then the results obtained in laboratory and field were compared in order to verify the proportion of the results and analyze how the interaction of the permeable pavement layers affects the clogging phe nomenon. The values of c and R2 obtained for a low rate of clogging in the laboratory and the experimental field are shown in Table 5. As shown in Table 5 the ratio of laboratory parameters c and experimental field was almost 1:4, this was explained, because in the laboratory conditions the permeability was measured independently in the PC, and in the experimental field it was measured in the pervious pavement system (PC, granular layer, subgrade). The interaction of the Table 5 Summary results of laboratory and Experimental Field for low clogging rate. Sediment
Laboratory
Experimental Field
clab
R2
k∞
cf
R2
k∞
Sand
1.423
0.995
5.969
0.996
Clay
3.175
0.987
10.489
0.999
SþC
2.734
0.999
1.409E01 5.000E09 4.000E04
10.883
0.998
1.960E01 8.100E02 8.800E02
Declaration of competing interest
cf/ clab
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
4.19 3.30 3.98
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Acknowledgments
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The authors would like to thank postgraduate program of doctorate in civil engineering of the State University of Londrina and its laboratory of construction materials. Besides, the first author thanks the economic support provided by CAPES during the development of this research. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.jobe.2020.101203. Author statement The functions of each of the authors of this Paper are listed below: � Gersson F. B. Sandoval: lead author, experimental planning, executor of the experimental phases, data processing and clogging model. � Isaac Galobardes Reyes: Collaboration in the analysis of results and writing of the document. � Andr� e Campos de Moura: experimental logistics, experimental planning and clogging model. � Berenice Martins Toralles: experimental planning, review of stages, analysis of results.
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