Pore and strength characteristics of cemented paste backfill using sulphide tailings: Effect of sulphur content

Construction and Building Materials 237 (2020) 117452

Contents lists available at ScienceDirect

Construction and Building Materials journal homepage: www.elsevier.com/locate/conbuildmat

Pore and strength characteristics of cemented paste backfill using sulphide tailings: Effect of sulphur content Lang Liu a,b,⇑, Jie Xin a, Chao Huan a, Chongchong Qi c,*, Wenwu Zhou d, KI-IL Song e a

Energy School, Xi’an University of Science and Technology, Xi’an 710054, China Key Laboratory of Western Mines and Hazards Prevention, Ministry of Education of China, Xi’an 710054, China c School of Resources and Safety Engineering, Central South University, Changsha 410083, China d School of Chemistry and Chemical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China e Dept. of Civil Engineering, Inha University, Incheon 402-751, South Korea b

h i g h l i g h t s

g r a p h i c a l a b s t r a c t


ACCNN and PCAS were efficient for

the quantitative analysis of SEM images.
Sulphur content had a significant influence on the pore and strength characteristics of CPB.
UCS of CPB with increasing sulphur content was increased first and then decreased.
12% sulphur content was found to be an inflexion point for pore characteristics of CPB.
Porosity and the number of pores were found to be strongly correlated to the UCS of CPB.

a r t i c l e

i n f o

Article history: Received 24 June 2019 Received in revised form 13 October 2019 Accepted 2 November 2019

Keywords: Cemented paste backfill Sulphide tailings Sulphur content Pore and strength

a b s t r a c t Cemented paste backfill (CPB) is a special composite prepared from recycled tailings, cement, and water. To ensure safe and efficient application of CPB for sulphide tailings, its pore and strength characteristics should be investigated. Although the strength of CPB has been determined well, identification of pore characteristics often requires manually adjusted threshold during the pre-treatment of microscopic images. In this study, an annealed chaotic competitive neural network was used for the pre-treatment of microscopic images and analysis of particles (pores) and cracks for quantitative pore analysis. The effect of sulphur content on the pore and strength characteristics of CPB specimens was evaluated in this study. Microscopic parameters (porosity, fractal dimension, etc.) were used to represent the pore characteristics, and unconfined compressive strength (UCS) was used to represent the CPB strength. The results show that the sulphur content plays a critical role in the pore and strength characteristics of CPB. For example, the porosity first decreased from 9.5% to 8.2% and then increased from 8.2% to 12.89% when the sulphur content was increased from 6.1% to 25%. Instead, the UCS of CPB specimens showed a slight increase from 2.51 MPa to 2.90 MPa before a clear decrease from 2.90 MPa to 0.571 MPa. The results indicate that it is necessary to evaluate the effect of sulphur content before the sulphide mineral tailings are recycled in CPB. Ó 2019 Elsevier Ltd. All rights reserved.

⇑ Corresponding authors at: Energy School, Xi’an University of Science and Technology, Xi’an 710054, China (L. Liu) and School of Resources and Safety Engineering, Central South University, Changsha 410083, China (C. Qi). E-mail addresses: [email protected] (L. Liu), [email protected] (J. Xin), [email protected] (C. Huan), [email protected] (C. Qi), [email protected]. kr (KI-IL Song). https://doi.org/10.1016/j.conbuildmat.2019.117452 0950-0618/Ó 2019 Elsevier Ltd. All rights reserved.

2

L. Liu et al. / Construction and Building Materials 237 (2020) 117452

1. Introduction

2. Materials and methods

A large amount of tailings created by the mining industry pose a severe threat to the environment [1–4,52,53]. The environmental concerns become more pressing when sulphide-rich tailings are encountered. These tailings, often containing sulphidic minerals such as pyrrhotite (Fe(1-x)S), are produced during mineral excavation from sulphur-rich ore deposits. If the surface is disposed, these sulphide-rich tailings become unstable in the presence of water and air [5]. In such cases, sulphide-rich tailings are oxidized, resulting in acid mine drainages that promote the release of toxic heavy metals (i.e., zinc, and arsenic) to surface stream and underground water [6–8]. Furthermore, highly acidic surrounding conditions are caused by the weathering of oxidation layers where carbonate neutralization is not available [9]. Thus, how to dispose mine tailings, especially sulphide-rich tailings, plays an important role in both controlling environmental pollution and promoting cleaner production of mineral resources. Cemented paste backfill (CPB) has long been used as an efficient and eco-friendly method for the disposal of tailings, which can recycle 60–75% of tailings to underground voids [10,11,54,55]. Apart from environmental benefits, CPB can also help to recover the pillar ores, ensure the safe exploitation, and avoid/minimize surface subsidence once being used together with mining geomechanics. Also, the costs of rehabilitation and filling are reduced owing to the recycling of mine tailings [12,13,56–59]. All these benefits have promoted the CPB technique as an important part during mineral excavation [14–17]. Generally, CPB should have sufficient strength to remain stable during mining operations, and its strength is mainly provided by the hydration of ordinary Portland cement. However, strength and stability problems may be encountered when sulphide-rich tailings are used in CPB. These CPB materials are often associated with strength loss and change in pore characteristics caused by internal sulfate attack, which has been extensively studied [7,10,18–21]. For example, Benzaazoua et al. evaluated the effects of CPB chemical composition and mineral types on the strength of CPB, indicating that sulfate erosion decreases the strength of CPB [19]. Ercikdi et al. showed that the oxidation products of sulphide minerals such as acid and sulfate led to undesired chemical reactions with the constituents of backfill, weakening its long-term stability [20]. Yin et al. studied the relationship between sulphur content and free expansion rate (FER) of CPB samples. The results show that with the increase of sulphur content, FER did not clearly grow during the pre-curing period (28 d), while FER increased from 4.856% (S: 3.64 wt%) to 9.613% (S: 20 wt%) during the post-curing period (120 d) [21]. However, the effect of sulphur content on the pore characteristics of CPB has not been studied quantitatively due to lack of data. Also, although the pore characteristics of CPB strongly correlate with strength [22–24], the correlation between pore characteristics and strength with respect to sulphur content has not been investigated thoroughly. The objective of this study was (a) to apply annealed chaotic competitive neural network (ACCNN) to process scanning electron microscopy (SEM) images for the determination of pore characteristics that has been difficult to quantify through visual comparison, (b) to advance the fundamental understanding of the effect of sulphur content on CPB pore characteristics, and (c) to study the correlation between pore characteristics and strength with respect to sulphur content. No quantitative analysis has yet been reported for this important topic, which has great significance during the recycling of mine tailings and production of mineral resources.

2.1. Materials 2.1.1. Sulphide-rich tailings The sulphide-rich tailings were obtained from copper mines located in Central China. The sulphide concentrate used in the experiment was obtained from the same copper mine. The important physical parameters of sulphide-rich tailings are shown in Table 1. The main chemical elements of sulphide-rich tailings and sulphide concentrate are shown in Table 2. Fig. 1 shows the particle size distribution (PSD) determined using a laser diffraction particle size analyser (Winner, 2000). The d10, d60, d90, and uniformity coefficient of sulphide-rich tailings were 4.96 lm, 32.02 lm, 71.30 lm, and 6.46, respectively. The optimum gradation of tailings particles was determined using the Talbot equation [25], and the uniformity coefficient of tailings particles is generally between 4 and 6. According to the curve shown in Fig. 1, the uniformity coefficient was 6.46; thus, the tailings relatively lacked in coarse particles. 2.1.2. Water and binder Common tap water was used to mix the water. Considering the economic cost of the mine, the actual surrounding rock stability of the mine, and the test and recommendations provided in the literature [26], composite Portland cement (P.C32.5) was selected as the binder. The main physic-chemical properties of P.C32.5 are shown in Tables 3 and 4. 2.2. Methods 2.2.1. Experimental procedure Table 5 shows the experimental methods used in this study. As the aim of this study was to evaluate the effect of sulphur content, the concentration of solids and ratio of tailings/cement (T/C) were determined and kept constant throughout the study based on trial tests and engineering experience. The sulphur content was changed from 6.1 wt% of solids (tailings and sulphide concentrate) to 25 wt%. The determination of sulphur content was based on suggestions provided in the literature [14,19,26]. Specifically, the amounts of each component of the filling material (tailing, Sulphide concentrate, composite Portland cement (PC.32.5), and Water) are shown in Table 5. The proportion of each CPB ingredient was determined and the CPB materials were homogenized using an NJ-160Avessel. After about 10 min mixing, the CPB slurry was poured into a cylindrical test mold (cast iron, 50 mm  100 mm) coated with lubricating oil. After 48 h, the CPB was demolded and added to a curing box (temperature: 20 ± 1 °C and humidity: 95 ± 1%). Three CPB samples existed in each series of experiment, and the average results are shown for further analysis. 2.2.2. Experimental instruments The CPB specimens were removed from the curing box after the corresponding curing times; the corresponding heights and diameters were recorded using a Vernier calliper. UCS tests were carried out using an electronic universal testing machine (MTS C43.504) with a 1 mm/minloading rate and 1–50 kN strength range. Moreover, one cylindrical sample (D*H = 4*5 mm) was cut from the CPB specimen for SEM (JSM-6460LV SEM analyser). The CPB specimens were then coated with gold for 180 s. More than 10 SEM images (2000 amplification) were obtained for each CPB specimen and simulated using ACCNN for image processing to determine the pore characteristics. The results show average pore

3

L. Liu et al. / Construction and Building Materials 237 (2020) 117452 Table 1 Basic physical properties of sulphide-rich tailings. Item

Specific gravity (t/m3)

Volume-weight (t/m3)

Bulk density (t/m3)

Porosity (%)

Tailings

2.852

1.387

0.487

51.370

Table 2 Primary chemical elements of the sulphide-rich tailings and sulphide concentrate (unit wt%). Item

S

Ca

Si

Al

TFe

Mg

Cu

Zn

Tailings Sulphide concentrate

6.10 39.82

2.60 0.57

9.06 8.36

5.34 1.26

24.60 40.12

0.18 0.06

0.045 0.01

0.01 0.01

effect, SEM has been widely used in the literature to determine the microstructure of CPB. As discussed before, each CPB specimen was taken and treated with spay-gold prior to the SEM analysis. 3.2. Image pre-treatment using ACCNN In this study, the SEM images were pre-treated using a novel image clustering method, ACCNN, to obtain binary images. The ACCNN incorporated the simulated annealing mechanism into the competitive neural network to improve the network training. The binary pore images were finally obtained after self-adaption. Although the implementation of ACCNN for SEM analysis was limited, some studies used similar methods in the literature, such as in Ref. [27]. Fig. 1. PSD of sulphide-rich tailings. Table 3 Main physical properties of P.C32.5 composite Portland cement. Items

Value

Reference

Fineness (<10 lm), % Initial setting time, min Final setting time, min 28 d flexural strength, MPa 28 d uniaxial compressive strength, MPa

8.00 162.00 403.00 6.60 31.50

10 45 600 5.5 32.5

Note: reference of P.C 32.5 composite Portland cement is from China’s cement strength classification (GB12958-1999 – composite Portland cement). Table 4 Main chemical compositions of P.C32.5 composite Portland cement. Element (wt%)

C3S

C2S

C3A

C4AF

Binder

39.7

31.33

11.12

7.53

characteristics. Fig. 2 shows the experimental instruments used in UCS and SEM tests. 3. Digital image processing and data analysis 3.1. Specimen preparation for SEM analysis With numerous advantages, i.e., a high resolution, high enlargement factor, wide field of view, and strong three-dimensional

3.2.1. Methodology Although the traditional competitive neural network has been widely used in material science [28], it tends to converge to a local minimum during the training [29]. Therefore, the simulated annealing mechanism was incorporated into the competitive neural network in this study to determine the network architecture, thus effectively addressing the local minimum issue and allowing the algorithm to reach the optimal solution efficiently. Fig. 3 shows the topological structure diagram of a two-layer simulated annealing competitive learning neural network. In this study, ACCNN with two layers was selected because it might be superior to the other layers in training [29]. N nodes in the input layer were classified into c categories in the output layer, i.e., c clustering centres exist in the output layer. During training, the transient state ux;y and internal weight state vx;y gradually reach the stable states by inserting the simulated annealing functions. The output states gradually decrease to the global minimum with a small learning factor. To shorten the fractal processes, parallel computing is used to update the parameters. In ACCNN, the neural states can be updated via function vx;y. The simulated annealing algorithm is applied to train the network using Eqs. (1)–(6). This network model contains n and c nodes in the input and output layers, respectively, along with n  c connection weights. The model can be expressed as follows:

E¼

n X c  2 1X ux;j Z x  W j  2 x¼1 j¼1

ð1Þ

Table 5 Experimental scenarios. Items

Solid concentration (wt%)

Binder type

T/C ratio

Sulphide concentrate (wt%)

Cement (g)

Water (g)

Tailings (g)

Sulphur content (g)

A1 A2 A3 A4

72

P.C32.5

8

6.1 12.0 18.0 25.0

98.4

344.4

787.2 649.4 509.4 346.0

0 137.8 277.8 441.2

4

L. Liu et al. / Construction and Building Materials 237 (2020) 117452

Fig. 2. Experimental instruments: MTS C43.504 UCS testing machine and JEOL JSM-6460LV SEM analyser.

Fig. 3. Topological structure of simulated annealing neural network.

ux;j ðtÞ ¼

1 1 þ ev x;jðtÞ=e

ð2Þ

  V x;y ðt þ 1Þ ¼ kV x;y ðtÞ þ E  TðtÞ V x;y ðtÞ  I0

ð3Þ

  Dwj ¼ g Z x  W j ux;y

ð4Þ

TðtÞ ¼

1 bþ1

  b þ tanhðaÞt Tðt  1Þ

ð5Þ

W j ðt þ 1Þ ¼ wj ðtÞ þ Dwj ðtÞ

ð6Þ

where E is the energy function of networks with n and c nodes; ux;y and vx;y are the transient state and internal state connection weights, respectively. 3.2.2. Pre-treatment process During the simulation, the image size was set to 510 mm  410 mm, leading to a total number of pixels of 209,100. These pixels were regarded as the input node. Also, four

L. Liu et al. / Construction and Building Materials 237 (2020) 117452

output nodes (brightest, moderately bright, moderately dark, and darkest, respectively) are present. The transient state ux;y and internal weight state vx;y have arrays with 209,100  4 elements. The input vector was populated with the pixel’s gray scale at each point (209,100 pixels in total). The flow chart of ACCNN algorithm is shown in Fig. 4. 3.2.3. Pre-treatment results SEM images (510 mm  410 mm) were used in the pretreatment process; the ACCNN method was used for clustering analysis. A pre-treatment example is shown in Fig. 5. Specifically, Fig. 5(a) shows the original image; Fig. 5(b)–(e) show the brightest, moderately bright, moderately dark, and darkest images, respectively; Fig. 5(f) shows the inverted image of Fig. 5(e). Finally, the darkest image, i.e., Fig. 5(e), was selected in this study as it best represents the pore image. In Fig. 5(f), the dark area represents the pore region, based on which the pore characteristics can be calculated. 3.3. Quantitative analysis system SEM images are shown by the matrices of pixels. For each series of experimental samples, at least three SEM images were recorded for each sample, whose mean value was taken as the value for the quantitative processing of digital images. A total of 48 images were recorded for quantitative analysis. To quantify the pore characteristics of CPB specimen, ACCNN-based clustering analysis was used to extract the information from the images, leading to the identification of accurate boundaries between skeleton particles and pores. Then, the particle (pore) and crack analysis system (PCAS)

5

was used to quantitatively evaluate the SEM image using geometric theory [30]. Several studies have been published about the quantitative information output by SEM using PCAS [30,31]. For example, Liu et al. described in detail the principle of quantitative information output by SEM using PCAS. They also explained the advantages and disadvantages of the application of quantitative information output by SEM using PCAS [30]. The dark and bright regions in the binary SEM images represent the particles and pores, respectively. By analysing the dark regions, the microscopic parameters of CPB (porosity, fractal dimension, etc.) were obtained. 3.4. Quantitative analysis of pore characteristics According to experience and suggestions provided in the literature, the following metrics were used to characterise the pore characteristics [32]: a) Porosity: Porosity is the ratio of pore volume to particle volume in SEM images. Porosity ranges between 0 and 100%, representing the integrity of pore in a CPB. b) The number of pores reflects the extent of pore failure of CPB. The more the number of pores, the more severe the damage. c) Average pore width: The ratio of total pore area to the total pore length; this is used to represent the stretching of a pore. d) Fractal dimension of a pore is used to show the size distribution of a backfill, specifically reflecting the change in pore size. The fractal dimension of a pore is usually described by the distribution characteristics of N(r) that

Fig. 4. The flow chart of ACCNN algorithm.

6

L. Liu et al. / Construction and Building Materials 237 (2020) 117452

Fig. 5. Set of clustering images: (a) original image, (b) brightest image in the set, (c) moderately bright image in the set, (d) moderately dark image in the set, (e) darkest image in the set, and (f) inverted image of (e).

is the number of pores with diameter not greater than r (a pre-set value of pore diameter). Dc is defined as the fractal dimension of a pore. The pore diameter r serves as thex axis, and N(r) corresponds to the y axis. A series of N(r) values can be obtained by changing ther values. Dc can be calculated as follows:

DC ¼ LimlnNðrÞ=lnr

ð7Þ

A larger Dc value indicates less homogeneity of pore size distribution [33]. e) Probability entropy represents the order of structure cell, describing the arrangement of pores in the sulphurcontaining CPB. Hm can be calculated as follows:

Hm ¼ 

n X

Pi ðaÞlogn Pi ðaÞ

ð8Þ

i¼1

where a denotes the directional angle corresponding to the longest chord of CPB particles, pi is the frequency at which the structural elements appear at a specific region, n is the number of directional segments along the direction where the structural elements are distributed. Hm ranges between 0 and 1. A larger Hm indicates that pores are arranged more disorderly, whereas a smaller Hm indicates an improved order of pore distribution [34]. f) Roundness can be calculated as follows:

Ri ¼ 4pS=L2

ð9Þ

L. Liu et al. / Construction and Building Materials 237 (2020) 117452

where S and L are the area and perimeter of a region, respectively. Only considering the circularity of a single pore leads to a large error. Therefore, the average roundness is used to describe the pore geometry characteristics. The average roundness can be expressed as follows:

R¼

n X

Ri =n

ð10Þ

i¼1

where n is the statistically determined number of particles and pores. R ranges between 0 and 1; a larger R value indicates a more circular pore.

7

of CPB. The variation in the integral area of T2 spectrum distribution reflects the change in pore volume of CPB [38]. The variation characteristics of T2 spectral area of CPB with different T/C ratios and sulphur content of 2.5% are shown in Table 6. The porosity of CPB can be inversely calculated from the NMR spectral area. Specific calculation steps are provided in the literature [39,40]. The calculation error for CPB porosity was less than 7.8% between NMR and SEM, indicating that SEM is reliable enough for the quantitative analysis of CPB. In addition, the quantitative analysis of digital images provided a method to determine the microstructure of materials. 4.2.2. Validation of fractal dimension (SEM and NMR) Eq. (11) can be used to calculate the fractal dimension (D) of the NMR method

4. Results and discussion 4.1. Sulfate attack

lgSV ¼ ð3  DÞlgðT 2 Þ þ ðD  3ÞlgðT 2Max Þ

The expansion of sulphur-containing CPB was caused by stress imbalance inside the backfill body. According to the mechanism of internal sulfate attack, the internal sulfate attack can be divided into three different stages [35–37], as shown in Fig. 6. First, the cement hydration reaction, i.e., C3S, C2S, C3A, and C4AF reacted with water to form hydration products such as C-S-H gel, CH, AFt (small amount), C3AH6, and other hydration products. The hydration products of C-S-H gel were insoluble in water, precipitated as colloidal particles, and formed gel groups with cementation ability. CH rapidly saturated in the liquid phase and precipitated in the form of crystals. Second, the sulfate ions underwent complex chemical reactions with CH, C3A, and C-S-H gels, forming products of insoluble salts (AFt and gypsum) with a larger crystal volume than the original solid-phase components. To be more specific, AFt and gypsum were 2.5 times and 1.24 times of the original reactant [36], respectively. The internal stress of backfill was formed, causing expansion corrosion. On the other hand, the cementing components such as CH and C-S-H, the hydration products of CPB, constantly dissolved or decomposed, resulting in the loss of binding performance between aggregated and a decrease in backfill strength. Finally, when free water was consumed or anhydrous ethanol was used in backfill (because of the presence of hydrogen bonds in anhydrous ethanol, free waterwas quickly absorbed from the CPB), hydration was terminated, and sulfate erosion stopped. 4.2. Feasibility of digital image technology 4.2.1. Validation of porosity (SEM and NMR) The total T2 spectral area of NMR represents the NMR porosity, which is a key feature reflecting the change in pore characteristic

ð11Þ

where SV is the cumulative pore volume fraction with the transverse relaxation time less than T2; T2Max is the maximum transverse relaxation time; D is the fractal dimension. In the case of pore structure of CPB, lg Sv and lg T2 should be linearly correlated. Therefore, the T2 spectrum data can be calculated by regression analysis; then, the fractal dimension D is obtained (as shown in Fig. 7). The fractal dimension is used to describe the pore structure of CPB [40–42]. Table 7 shows the fractal dimension error of CPB calculated from NMR and SEM. Specifically, the sulphur content of CPB was 2.5%. The characteristics of specific tailings are described in the literature [39]. The maximum error was 7.7%, indicating that SEM is reliable enough for the quantitative analysis of CPB. A comparison of different test methods (NMR and SEM) based on the porosity and fractal dimension of CPB showed that the digital image quantitative analysis technique is reliable. However, it can be used as an alternative experimental method to study the microstructure problems of materials. 4.3. Pore characteristics with different sulphur contents Fig. 8 shows the effect of sulphur content on the pore characteristics of CPB. The pore characteristics of CPB with different sulphur contents were studied at curing times of 3 d, 7 d, 14 d, and 28 d. Fig. 8(a) shows the relationship between the sulphur content and porosity of CPB. It was found that the porosity first decreased and then increased with the increase in sulphur content. By taking CPB samples after 28 d of curing as an example, when the sulphur content was increased from 6.1% to 12%, the porosity decreased slightly (9.5% at 6.1% and 8.2% at 12%). One possible reason is that a low sulphur content promotes the formation of expansive ettringites. These expansive ettringites fill the macropores inside the

Fig. 6. Schematic of internal sulfate attack of individual CPB particle.

8

L. Liu et al. / Construction and Building Materials 237 (2020) 117452

Table 6 Porosity results of CPB specimens with different T/C ratios and sulphur content of 2.5%. Curing time (d)

Concentration (wt%)

T/C ratio

Total peak area

3

72

7

72

14

72

28

72

4 6 8 10 12 4 6 8 10 12 4 6 8 10 12 4 6 8 10 12

102372.65 106505.88 108247.18 116268.49 118755.13 44682.32 47148.78 82983.8 91655.17 91177.84 42900.26 33313.79 46984.20 73800.98 84331.54 19637.09 29718.70 37776.46 38249.38 46642.45

Fig. 7. Fitting curve of fractal dimension.

CPB, thus decreasing the porosity [36,43]. However, with further increase in sulphur content from 12% to 25%, excess ettringite is formed. The expansion of ettringite results in the extension of microcracks and thus an increase in porosity [44–47]. Similar results on the relationship between sulphur content and porosity were obtained for other curing times. These results indicate that the sulphur content plays a critical role in the formation and development of the pore structure of backfill. With a certain amount of sulphur, the porosity decreases (compared with a smaller sulphur content); further increase in sulphur content increases the porosity. Considering the correlation between porosity and strength, the CPB strength increases when a certain amount of sulphur is added, whereas the CPB strength decreases with excessive sulphur content. These results are consistent with the findings of Fall And Pokharel [44]. Fig. 8(b) shows the variation in the number of pores with sulphur content. Compared with the porosity, a similar trend was observed. The number of pores first decreased and then increased with the increase in sulphur content. Taking the CPB samples after 28 d of curing as an example, the number of pores was 69 when the

Porosity (%)

Error

NMR

SEM

13.39 13.92 14.14 15.15 15.46 6.07 6.38 9.79 10.88 10.82 5.89 4.68 6.40 8.61 9.93 2.94 4.21 5.22 5.28 6.34

14.65 14.03 13.77 16.06 16.48 6.09 6.92 9.03 11.26 11.09 6.04 5.68 7.49 9.24 9.89 3.24 4.69 6.23 5.62 6.99

6.42% 0.79% 2.62% 6.01% 6.60% 0.33% 2.19% 7.76% 3.49% 2.50% 2.55% 7.05% 3.28% 7.32% 0.40% 5.10% 4.28% 5.56% 6.44% 3.94%

sulphur content was 6.1%; the number of pores was 60 when the sulphur content was 12%. When the sulphur content was increased from 6.1% to 12%, the number of pores showed a downward trend. This is probably because the sulphur caused mutual adsorption and interweaved between the particles. The voids between particles decreased, thus enriching the pores and decreasing the number of pores [43]. When the sulphur content was increased from 12% to 25%, the number of pores increased sharply (from 60 to 108). The sharp increase in the number of pores is probably caused by the excessive sulphur that prevents the formation of hydration products [36]. Also, the expansive ettringite decomposes the macropores within CPB into micropores. In addition, the sulphur content and number of pores of backfill at other curing times exhibited similar trends. Fig. 8(c) shows the relationship between sulphur content and average pore width of CPB. In general, the pore width increased with increasing sulphur content. Taking the CPB sample after curing for 28 d as an example, when the sulphur content was increased from 6.1% to 12%, the average pore width increased slightly (16.77 compared with 17.05). When the sulphur content was greater than 12%, the average pore width increased rapidly as the sulphur content increased. In addition, the sulphur content and average pore width of backfill at other curing times had a similar trend. Further, the rate of increase was accelerated when the sulphur content was increased. These results indicate that an increase in sulphur content aggravates the fragmentation of pores, especially at a higher sulphur content. The effect of sulphur content on the fractal dimension of pores is shown in Fig. 8(d). As discussed before, a large fractal dimension indicates a worse uniformity of pores, further representing a large difference in pore sizes. Taking the CPB sample obtained after curing for 28 d as an example, when the sulphur content increased from 6.1% to 12%, the fractal dimension decreased from 1.27 to 1.25. This is because a low sulphur content is beneficial for the formation of expanded ettringite, filling the effective spacing between the pores of backfill [43] and thus reducing the fractal dimension. The fractal dimension became stabilized when the sulphur content was higher than 12%, indicating that the pore-breaking effect produced with sulfate attack remained similar to increasing sulphur content after 12%. In addition, the sulphur content and fractal dimension of backfill at other curing times had similar regularities. On the microscopic level, the pores were gradually filled and disap-

9

L. Liu et al. / Construction and Building Materials 237 (2020) 117452 Table 7 Fractal dimension results of CPB specimens with different T/C ratios and sulphur content of 2.5%. Curing time (d)

Concentration (wt%)

T/C ratio

3

72

7

72

14

72

28

72

4 6 8 10 12 4 6 8 10 12 4 6 8 10 12 4 6 8 10 12

peared within a certain size range, and the fractal dimension almost remained unchanged. Fig. 8(e) shows the relationship between sulphur content and probability entropy of CPB. With the increase in sulphur content, the probability entropy first increased and then decreased. Taking the CPB samples obtained after 28 d of curing as an example, the maximum probability entropy (0.961) was obtained at 12% sulphur content. In addition. The probability entropy was greater than 0.92, indicating that the overall pore distribution was chaotic and lacked directionality [32]. During cement hydration, the redistribution and imbalance of internal stress in CPB cause the translation and rotation of particles, thus changing the direction of particles. The translation, rotation, and rearrangement of particles have been described in detail in another paper [48]. However, under the action of sulphur attack, diverse complex compounds (i.e., ettringite) are produced, inhibiting the movement of particles and making the particles more disorderly. Fig. 8(f) shows the effect of sulphur content on the average roundness of CPB. The average roundness is highly related to the sulphur content. In the case of 28 d curing, an evident increase in the average roundness was observed (0.47 compared with 0.42) when the sulphur content was increased from 6.1% to 12%. Then, the average roundness stabilized with further increase in sulphur content. In addition, the sulphur content and roundness of backfill at other curing times exhibited similar rules. These results indicate the shape change of pores with sulphur content. At a low sulphur content (6.1–12%), the pore shape gradually became smooth, and the average roundness increased. This is probably because of the expansion caused by expansive phases (i.e., gypsum and ettringite), effectively making the pore shape of CPB more smooth and thus increasing the average roundness [49]. When a certain value was reached, further increase in the average roundness was not achieved, which would result in a stabilized average pore roundness even with further increase in sulphur content.

4.4. Strength characteristics with different sulphur content UCS is one of the most important mechanical properties of CPB [50]. In this section the strength characteristics of CPB with different sulphur contents were studied after 3 d, 7 d, 14 d, and 28 d of curing (as shown in Fig. 9). The UCS of all CPB showed an upward trend when the curing time was increased from 3 d to 14 d.

Fractal dimension (D)

Error 2

SEM

NMR

R

1.51 1.52 1.53 1.44 1.59 1.36 1.39 1.32 1.40 1.32 1.30 1.25 1.24 1.25 1.36 1.14 1.16 1.23 1.25 1.26

1.42 1.47 1.46 1.49 1.52 1.29 1.35 1.38 1.37 1.39 1.20 1.23 1.25 1.26 1.28 1.12 1.13 1.17 1.18 1.20

0.74 0.75 0.73 0.73 0.74 0.68 0.68 0.70 0.72 0.70 0.60 0.74 0.60 0.62 0.62 0.62 0.62 0.61 0.63 0.62

5.96% 3.29% 4.58% 3.47% 4.40% 5.15% 2.88% 4.55% 2.14% 5.30% 7.69% 1.60% 0.81% 0.80% 5.88% 1.75% 2.59% 4.88% 5.60% 4.76%

However, when the curing time was increased from 14 d to 28 d, the UCS of CPB with a sulphur content of 6.1% and 12% showed a downward trend. For CPB with sulphur contents of 18% and 25%, it continued to increase. Taking 28 d of curing as an example, the UCS of CPB increased from 2.51 MPa to 2.95 MPa when the sulphur content was increased from 6.1% to 12%. As explained before, a low sulphur content would decrease the porosity, thus increasing the UCS. As the sulphur content further increased from 12% to 25%, the UCS decreased slightly. This is probably because of excess expansive hydrates (i.e., gypsum and ettringite), which would induce internal stress and promote the crack growth inside CPB [44–47].The relationship between UCS and sulphur content could be explained in a similar manner as the relationship between pore characteristics and sulphur content.

4.5. Relationship between pore and strength characteristics with respect to sulphur content The relationship between pore characteristics and UCS was analysed by taking the sulphur-containing CPB sample obtained after curing for 28 d as an example, Fig. 10 shows the relationship between three pore characteristics, porosity, average pore width, and pore fractal dimension, with strength at different sulphur contents. The lowest porosity (8.2%) was achieved at 12% sulphur content when the highest UCS (2.95 MPa) was obtained. Further, an increase/decrease in UCS was observed when the porosity was decreased/increased. The results on negative correlation between porosity and UCS are consistent with the findings reported in the literature [49]. The relationship between average pore width with UCS showed that the average pore width increased even though an inflexion point was observed in UCS at 12%. This indicates that the average pore width is not consistent with the UCS, at least from this study. Fig. 10 also shows that the pore fractal dimension reached its minimum at 12% sulphur content, indicating that the uniformity of pores was better, and the size difference between pores was smaller. Therefore, the fractal dimension of pore was consistent with the UCS of CPB. The evolution between number of pores and UCS with different sulphur contentsis shown in Fig. 11. This shows that the number of pores was the smallest (60) at 12% sulphur content when the highest UCS was obtained. Further, the variation trend of number of

10

L. Liu et al. / Construction and Building Materials 237 (2020) 117452

15

300

14

Porosity, %

12 11

Curing time 3d 7d 14d 28d

10 9

Number of Pores

250 13

Curing time 3d 7d 14d 28d

200

150

100

8 50 6

12

18

24

6

12

Sulphur content, %

18

(a)

(b)

28

24

Curing time 3d 7d 14d 28d

1.45

Fractal dimension (DC)

Average pore width, pixel

26

24

Sulphur content, %

22

20

Curing time 3d 14d

7d 28d

1.40

1.35

1.30

18

1.25 16 6

12

18

6

24

12

18

(c)

(d)

0.48

0.96

0.46

Average pore roundness

Probability entropy

0.95 0.94 0.93 0.92 0.91 0.90

24

Sulphur content, %

Sulphur content, %

Curing time 3d 14d

0.89

0.44

0.42

Curing time 3d 14d

0.40

7d 28d

7d 28d

0.38

6

12

18

Suphur content, %

(e)

24

6

12

18

24

Sulphur content, %

(f)

Fig. 8. Effect of sulphur content on the pore characteristics of CPB: (a) porosity, (b) number of pores, (c) average pore width, (d) fractal dimension, (e) probability entropy, and (f) average pore roundness.

L. Liu et al. / Construction and Building Materials 237 (2020) 117452

11

3.5 3.0

UCS, MPa

2.5

Sulphur content 6.1% 12% 18% 25%

2.0 1.5 1.0 0.5 0.0 3d

7d

14d

Fig. 11. Relationship between the number of pores and UCS with different sulphur contents.

28d

Curing time

4.0

Average width/10, pixels Fractal dimension Dc

Porosity/4, % UCS

3.5

4.0 3.5 3.0 2.5

3.0

2.0

2.5

UCS, MPa

Pore size distribution and characteristics

Fig. 9. Effect of sulphur content on the UCS of CPB.

1.5 2.0 1.0 1.5

0.5

1.0

0.0 6

12

18

24

Sulfur content, % Fig. 10. Relationship between three pore characteristics, porosity, average pore width, and pore fractal dimension, with UCS at different sulphur contents.

pores had a negative relationship with that of UCS, indicating that the number of pores and UCS has a strong correlation. Fig. 12 shows the variation in strength and probability entropy of CPB with different sulphur contents. The variation in probability entropy corresponds with the variation in UCS at low sulphur contents (6.1–12%). However, the relationship between probability entropy with UCS was unclear at high sulphur contents (18– 25%). This is probably because the effect of sulphur element swelling dominated when a high sulphur content was present in CPB [51]. Fig. 13 shows the variations in particle roundness and UCS with different sulphur contents. The higher the particle roundness, the more the particle shape approaches the circle, and the larger the UCS of CPB. As shown in Fig. 12, the UCS and roundness reached their maximum values (2.95 MPa for UCS and 0.47 for roundness) simultaneously at 12% sulphur content. However, as the sulphur content of CPB continued to increase, the roundness remained stable, whereas the UCS significantly decreased. This is probably because of the swelling of CPB specimens caused by excess sulphur content. In summary, two pore characteristics, porosity and the number of pores, exhibited a negative correlation with the UCS of CPB. The correlation efficient between porosity and UCS was found to be

Fig. 12. Relationship between probability entropy and UCS with different sulphur contents.

0.796, and the correlation efficient between the number of pores and UCS was 0.997. Other pore characteristics, including pore width, pore fractal dimension, probability entropy, and roundness, might have a good correlation with UCS at a low sulphur content (less than 12%). The relationship between these pore characteris-

Fig. 13. Relationship between roundness and UCS with different sulphur contents.

12

L. Liu et al. / Construction and Building Materials 237 (2020) 117452

tics with UCS at a high sulphur content was not clear from this study, which requires further research on this topic. 5. Conclusions Recycling of sulphide-rich tailings as CPB plays an important role in the cleaner production of mineral resources. In this study, the effect of sulphur content on the pore and strength characteristics of CPB was evaluated. To obtain quantitative results of pore characteristics, the ACCNN was used for SEM image pretreatment, and the PCAS was used for quantitative analysis. The following conclusions can be drawn based on this study: 1) The ACCNN and PCAS are efficient for the quantitative analysis of SEM images. 2) Sulphur content plays a critical role in the pore and strength characteristics of CPB. 3) 12% sulphur content was found to be an important inflexion point for the pore characteristics of CPB in this study. Sulphur content has a significant effect on the pore characteristics of CPB. 4) The UCS of CPB with an increasing sulphur content first increased first (until 12% sulphur content with the maximum UCS of 2.95 MPa) and then decreased. 5) The porosity and number of pores are negatively correlated to the UCS of CPB. The correlation between UCS and other pore characteristics is not clear, especially at high sulphur contents, which needs further research in the future. In the future, the effect of sulphur content can be studied together with other influencing variables such as curing time (90 d, 180 d), which will provide more insights into the sulfate attack. Also, the mechanism of sulphur attack can be investigated using atomic simulations. Finally, software development based on the ACCNN and PCAS will contribute to the quantitative analysis of SEM images, thus promoting a wider application of SEM technology in CPB analysis. Declaration of Competing Interest 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. Acknowledgments This research was supported by the National Natural Science Foundation of China (Nos. 51674188, 51874229, 51504182, 51974225, 51904224, 51904225, 51704229), Shaanxi Innovative Talents Cultivate Program-New-star Plan of Science and Technology (No. 2018KJXX-083), Natural Science Basic Research Plan of Shaanxi Province of China (Nos. 2018JM5161, 2018JQ5183, 2015JQ5187, 2019JM-074), Scientific Research Program funded by the Shaanxi Provincial Education Department (Nos. 15JK1466, 19JK0543), China Postdoctoral Science Foundation (No. 2015M582685), and Outstanding Youth Science Fund of Xi’an University of Science and Technology (No. 2018YQ2-01). This research was also supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2017R1E1A1A01075118). References [1] A.T. Cross, H. Lambers, Young calcareous soil chronosequences as a model for ecological restoration on alkaline mine tailings, Sci. Total Environ. 607–608 (2017) 168–175.

[2] C. Qi, A. Fourie, Cemented paste backfill for mineral tailings management: review and future perspectives, Miner. Eng. 144 (2019) 106025. [3] L. Liu, C. Zhu, C. Qi, M. Wang, C. Huan, B. Zhang, K.-I. Song, Effects of curing time and ice-to-water ratio on performance of cemented paste backfill containing ice slag, Constr. Build. Mater. 228 (2019) 116639. [4] C. Qi, L. Liu, J. He, Q. Chen, L.-J. Yu, P. Liu, Understanding cement hydration of cemented paste backfill: DFT study of water adsorption on tricalcium silicate (111) surface, Minerals 9 (4) (2019) 202. [5] S. Ahmari, L. Zhang, Durability and leaching behavior of mine tailings-based geopolymer bricks, Constr. Build. Mater. 44 (2013) 743–750. [6] M. Bagheri, A. Bazvand, M. Ehteshami, Application of artificial intelligence for the management of landfill leachate penetration into groundwater, and assessment of its environmental impacts, J. Cleaner Prod. 149 (2017) 784–796. [7] A. Kesimal, E. Yilmaz, B. Ercikdi, Evaluation of paste backfill mixtures consisting of sulphide-rich mill tailings and varying cement contents, Cem. Concr. Res. 34 (10) (2004) 1817–1822. [8] E. Yilmaz, A. Kesimal, B. Ercikdi, Evaluation of acid producing sulphidic mine tailings as a paste backfill, Turkish J. Earthences 17 (1) (2004) 11–20. [9] A. Valentín-Vargas, J.W. Neilson, R.A. Root, J. Chorover, R.M. Maier, Treatment impacts on temporal microbial community dynamics during phytostabilization of acid-generating mine tailings in semiarid regions, Sci. Total Environ. 618 (2018) 357–368. [10] B. Ercikdi, G. Külekci, T. Yılmaz, Utilization of granulated marble wastes and waste bricks as mineral admixture in cemented paste backfill of sulphide-rich tailings, Constr. Build. Mater. 93 (2015) 573–583. [11] H. Lu, C. Qi, Q. Chen, D. Gan, Z. Xue, Y. Hu, A new procedure for recycling waste tailings as cemented paste backfill to underground stopes and open pits, J. Cleaner Prod. 188 (2018) 601–612. [12] M. Fall, J.C. Célestin, M. Pokharel, M. Touré, A contribution to understanding the effects of curing temperature on the mechanical properties of mine cemented tailings backfill, Eng. Geol. 114 (3) (2010) 397–413. [13] S. Yin, A. Wu, K. Hu, Y. Wang, Y. Zhang, The effect of solid components on the rheological and mechanical properties of cemented paste backfill, Miner. Eng. 35 (2012) 61–66. [14] Q. Chen, Q. Zhang, C. Qi, A. Fourie, C. Xiao, Recycling phosphogypsum and construction demolition waste for cemented paste backfill and its environmental impact, J. Cleaner Prod. 186 (2018) 418–429. [15] C. Qi, Q. Chen, A. Fourie, X. Tang, Q. Zhang, X. Dong, Y. Feng, Constitutive modelling of cemented paste backfill: a data-mining approach, Constr. Build. Mater. 197 (2019) 262–270. [16] W. Sun, H. Wang, K. Hou, Control of waste rock-tailings paste backfill for active mining subsidence areas, J. Cleaner Prod. 171 (2018) 567–579. [17] T. Yılmaz, B. Ercikdi, Predicting the uniaxial compressive strength of cemented paste backfill from ultrasonic pulse velocity test, Nondestruct. Test. Eval. 31 (3) (2015) 1–20. [18] M. Benzaazoua, T. Belem, B. Bussière, Chemical factors that influence the performance of mine sulphidic paste backfill, Cem. Concr. Res. 32 (7) (2002) 1133–1144. [19] M. Benzaazoua, J. Ouellet, S. Servant, P. Newman, R. Verburg, Cementitious backfill with high sulfur content Physical, chemical, and mineralogical characterization, Cem. Concr. Res. 29 (5) (1999) 719–725. _ Effect of desliming of sulphide-rich mill tailings on [20] B. Ercikdi, H. Baki, M. Izki, the long-term strength of cemented paste backfill, J. Environ. Manage. 115 (2013) 5–13. [21] S. Yin, Y. Shao, A. Wu, Y. Wang, X. Chen, Expansion and strength properties of cemented backfill using sulphidic mill tailings, Constr. Build. Mater. 165 (2018) 138–148. [22] M. Fall, S.S. Samb, Pore structure of cemented tailings materials under natural or accidental thermal loads, Mater. Charact. 59 (5) (2008) 598–605. [23] B. Gaël, T. Christelle, E. Gilles, G. Sandrine, S.-F. Tristan, Determination of the proportion of anhydrous cement using SEM image analysis, Constr. Build. Mater. 126 (2016) 157–164. [24] J. Kiventerä, L. Golek, J. Yliniemi, V. Ferreira, J. Deja, M. Illikainen, Utilization of sulphidic tailings from gold mine as a raw material in geopolymerization, Int. J. Miner. Process. 149 (2016) 104–110. [25] M.P.A. Jackson, C.J. Talbot, External shapes, strain rates, and dynamics of salt structures, Geol. Soc. Am. Bull. 97 (3) (2015) 305–323. [26] H. Li, A. Wu, H. Wang, Evaluation of short-term strength development of cemented backfill with varying sulphide contents and the use of additives, J. Environ. Manage. 239 (2019) 279–286. [27] X. Qin, P. Wang, L. Lang, W. Mei, X. Jie, Sensitivity analysis of microstructure parameters and mechanical strength during consolidation of cemented paste backfill, Math. Prob. Eng. 2018 (2) (2018) 1–9. [28] S.T. Bararpour, M.R. Feylizadeh, A. Delparish, M. Qanbarzadeh, M. Raeiszadeh, M. Feilizadeh, Investigation of 2-nitrophenol solar degradation in the simultaneous presence of K2S2O8 and H2O2: using experimental design and artificial neural network, J. Cleaner Prod. 176 (2018) 1154–1162. [29] J.-S. Lin, Annealed chaotic neural network with nonlinear self-feedback and its application to clustering problem, Pattern Recogn. 34 (5) (2001) 1093–1104. [30] C. Liu, B. Shi, J. Zhou, C. Tang, Quantification and characterization of microporosity by image processing, geometric measurement and statistical methods: application on SEM images of clay materials, Appl. Clay Sci. 54 (1) (2011) 97–106. [31] C. Liu, C.-S. Tang, B. Shi, W.-B. Suo, Automatic quantification of crack patterns by image processing, Comput. Geosci. 57 (2013) 77–80.

L. Liu et al. / Construction and Building Materials 237 (2020) 117452 [32] J. Zhou, Y. Deng, Y. Cao, J. Yan, Experimental study of microstructure of Hangzhou saturated soft soil during consolidation process, J. Central South Univ. 45 (6) (2014) 1998–2005. [33] L.T. Mao, R. Xue, A.N. Li-Qian, L.I. Huai-Qi, Fractal approach on soft soil porosity microstructure, J. China Univ. Min. Technol. 34 (5) (2005) 600–604. [34] B. Shi, Quantitative research on the orientation of microstructures of clayey soil, Acta Geol. Sin. 52 (2) (1997) 83–84. [35] X. Jin, Y. Wang, Y. Tian, N. Jin, Research on kinetics model of cement hydration based on microstructure information, J. Build. Mater. 17 (5) (2014) 862–867. [36] L. Liu, C. Zhu, C. Qi, B. Zhang, K.-I. Song, A microstructural hydration model for cemented paste backfill considering internal sulfate attacks, Constr. Build. Mater. 211 (2019) 99–108. [37] Y. Tian, N. Jin, X. Jin, Coupling effect of temperature and relative humidity diffusion in concrete under ambient conditions, Constr. Build. Mater. 159 (2018) 673–689. [38] H. Shen, W.U. Aixiang, L. Jiang, Y. Wang, H. Jiao, X. Liu, 2016. Small cylindrical slump test for unclassified tailings paste, J. Central South Univ. [39] L. Liu, Z.Y. Fang, Y.P. Wu, X.P. Lai, P. Wang, K.-I. Song, Experimental investigation of solid-liquid two-phase flow in cemented rock-tailings backfill using Electrical Resistance Tomography, Constr. Build. Mater. 175 (2018) 267–276. [40] L. Qin, C. Zhai, S. Liu, J. Xu, S. Wu, R. Dong, Fractal dimensions of low rank coal subjected to liquid nitrogen freeze-thaw based on nuclear magnetic resonance applied for coalbed methane recovery, Powder Technol. 325 (2018) 11–20. [41] Y. Zhao, G. Zhu, Y. Dong, N.N. Danesh, Z. Chen, T. Zhang, Comparison of lowfield NMR and microfocus X-ray computed tomography in fractal characterization of pores in artificial cores, Fuel 210 (2017) 217–226. [42] S. Zhou, D. Liu, Y. Cai, Y. Yao, Fractal characterization of pore–fracture in lowrank coals using a low-field NMR relaxation method, Fuel 181 (2016) 218–226. [43] L. Liu, J. Xin, Y. Feng, B. Zhang, K.-I. Song, Effect of the cement-tailing ratio on the hydration products and microstructure characteristics of cemented paste backfill, Arabian J. Sci. Eng. 44 (7) (2019) 6547–6556. [44] M. Fall, M. Pokharel, Coupled effects of sulphate and temperature on the strength development of cemented tailings backfills: Portland cement-paste backfill, Cem. Concr. Compos. 32 (10) (2010) 819–828. [45] W. Li, M. Fall, Sulphate effect on the early age strength and self-desiccation of cemented paste backfill, Constr. Build. Mater. 106 (2016) 296–304. [46] W. Li, M. Fall, Strength and self-desiccation of slag-cemented paste backfill at early ages: link to initial sulphate concentration, Cem. Concr. Compos. 89 (2018) 160–168.

13

[47] M. Pokharel, M. Fall, Combined influence of sulphate and temperature on the saturated hydraulic conductivity of hardened cemented paste backfill, Cem. Concr. Compos. 38 (2013) 21–28. [48] J.X. Bo Zhang, Lang Liu, Lijie Guo, Ki-Il Song, An experimental study on the microstructures of cemented paste backfill during its developing process, Adv. Civ. Eng. 2018 (2018,) 1–10. [49] P.K.M.P.J.M. Monteiro, Concrete: microstructure, properties, and materials, 2008. [50] C. Qi, A. Fourie, Q. Chen, Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill, Constr. Build. Mater. 159 (2018) 473–478. [51] D. Chen, X. Chen, Y. Liu, Y. He, 2015. Expansion of circulating fluidized bed fly ash and control measures, Sichuan Daxue Xuebao 47(2) 198–204,208. [52] X. Feng, N. Zhang, Position-optimization on retained entry and backfilling wall in gob-side entry retaining techniques, International Journal of Coal Science & Technology 2 (3) (2015) 186–195. [53] J. Xu, D. Xuan, C. He, Innovative backfilling longwall panel layout for better subsidence control effect—separating adjacent subcritical panels with pillars, International Journal of Coal Science & Technology 1 (3) (2014) 297–305. [54] D. Ma, J. Wang, X. Cai, X. Ma, J. Zhang, Z. Zhou, M. Tao, Effects of height/diameter ratio on failure and damage properties of granite under coupled bending and splitting deformation, Engineering Fracture Mechanics 220 (2019) 106640. [55] K. Peng, J. Zhou, Q. Zou, J. Zhang, F. Wu, Effects of stress lower limit during cyclic loading and unloading on deformation characteristics of sandstones, Construction and Building Materials 217 (2019) 202–215. [56] L. Liu, Z. Fang, C. Qi, B. Zhang, L. Guo, K.I. Song, Experimental investigation on the relationship between pore characteristics and unconfined compressive strength of cemented paste backfill, Construction and Building Materials 179 (2018) 254–264. [57] L. Liu, P. Yang, C. Qi, B. Zhang, L. Guo, K.I. Song, An experimental study on the early-age hydration kinetics of cemented paste backfill, Construction and Building Materials 212 (2019) 283–294. [58] C. Huan, F. Wang, S. Li, Y. Zhao, L. Liu, Z. Wang, C. Ji, A performance comparison of serial and parallel solar-assisted heat pump heating systems in Xi’an, China, Energy Science & Engineering 7 (2019) 1379–1393. [59] X. Zhang, Y. Jia, M. Wang, L. Liu, Experimental Research on Heat Transfer and Strength Analysis of Backfill with Ice Grains in Deep Mines, Sustainability 11 (9) (2019) 2486.