*4.3. MNLR*

relation factor for MNLR predicted CS (R2 = 0.70) confirms the same. The correlation factor for training and validation is 0.75 and 0.69, respectively. The same can be confirmed by the dispersion of points in Figure 8 below. The results predicted by MNLR were not close to the experimental results. The correlation factor for MNLR predicted CS (R<sup>2</sup> = 0.70) confirms the same. The correlation factor for training and validation is 0.75 and 0.69, respectively. The same can be confirmed by the dispersion of points in Figure 8 below.

### *4.4. LR 40*

LR gave the results which were far away from the experimental results. A weak correlation (R<sup>2</sup> = 0.63) existed between the experimental and predicted results. The correlation factor for LR training and LR validation is 0.64 and 0.62, respectively. It can be seen in Figure 9 below that points are dispersed.

### *4.5. Sensitivity and Parametric Analysis*

Different variables are used to find the CS of RBC. Sensitivity analysis (SA) is used to determine the relative contribution of these variables to the result. SA is carried out mathematically by using the following Equations:

$$N\_{\dot{l}} = f\_{\text{max}}(\mathbf{x}\_{\dot{l}}) - f\_{\text{min}}(\mathbf{x}\_{\dot{l}}) \tag{12}$$

The results predicted by MNLR were not close to the experimental results. The cor-

$$SA \;= \; \frac{N\_{\text{\\_}}}{\sum\_{n}^{j=1} N\_{j}} \; \tag{13}$$

(**a**) (**b**) where *f*max(*xi*) is the maximum, and *f*min(*xi*) is the minimum output of the predictive models, respectively. Thus, *i* represents the input domain and other input variables that are kept constant. It is obvious from the graphical representation (shown in Figure 10) that the contribution of different input variables on the CS of RBC is same as that in real life.

(**f**)

(**g**) **Figure 7.** ANFIS modeling rules. (**a**) 1–29, (**b**) 30–60, (**c**) 61–91, (**d**) 92–122, (**e**) 123–153, (**f**) 154–184, (**g**) 185–192.

firmed by the dispersion of points in Figure 8 below.

The results predicted by MNLR were not close to the experimental results. The correlation factor for MNLR predicted CS (R2 = 0.70) confirms the same. The correlation factor for training and validation is 0.75 and 0.69, respectively. The same can be con-

*4.3. MNLR*

**Figure 8.** MNLR (**a**) training, (**b**), validation, (**c**) testing. **Figure 8.** MNLR (**a**) training, (**b**), validation, (**c**) testing.

seen in Figure 9 below that points are dispersed.

**Figure 9.** *Cont*.

(**a**) (**b**)

life.

*Crystals* **2021**, *11*, x FOR PEER REVIEW 14 of 24

(**c**)

**Figure 9.** LR (**a**) training, (**b**) validation, (**c**) testing*.* **Figure 9.** LR (**a**) training, (**b**) validation, (**c**) testing.

17.99 20 **Figure 10.** Contribution of inputs to the output. **Figure 10.** Contribution of inputs to the output.

10 15 Along with sensitivity analysis, a parametric analysis (PA) is also carried out. This helps in determining the influence of the input parameters on the output parameter. This shows the trend of CS when all the input variables are kept constant at their mean value except one input. The change in CS is recorded when one input variable is varied from its minimum value to its maximum value. All the results of PA are shown in Figure 11 below.

19.35 19.07 19.29

19.65

(13)

(13)

4.64 Age Cement RHA Water SP Agg 0 5 **Relative contribution to outcome (%)Parameters** The sublots in front of each graph in Figure 11 represent the constant parameters of parametric analysis for each input. The literature used for obtaining experimental values includes [12,35–39]. It can be observed from the results that when water is increased from a certain limit, a reduction in CS occurs. This is also obvious from previous studies. De Sensale [39] conducted research in which a water to cement ratio (w/c) of 0.4 gave more CS than w/c of 0.5. RHAP contributes towards the enhancement of strength, but when RHAP is increased by 30 percent, it results in decrease of compressive strength. This is due to the fact that, as discussed in Section 1, RHA contains 90 percent silica. By increasing the RHA percentage, the amount of silica is also increased. This silica remains unreacted by increment of RHA and results in reduced CS of RBC [37].

**Figure 10.** Contribution of inputs to the output. It can be seen from the above results that the regression models did not show satisfactory results as compared to the machine learning processes. This is due to certain

limitations in regression models, such as pre-defined equations that cannot learn the relationship between input variables and the function properly. Whereas, machine learning has efficiently predicted the relationship between input and output variables. The machine learning techniques gave results closer to the experimental values. shows the trend of CS when all the input variables are kept constant at their mean value except one input. The change in CS is recorded when one input variable is varied from its minimum value to its maximum value. All the results of PA are shown in Figure 11 below.

Along with sensitivity analysis, a parametric analysis (PA) is also carried out. This helps in determining the influence of the input parameters on the output parameter. This

*Crystals* **2021**, *11*, x FOR PEER REVIEW 15 of 24



**Figure 11.** *Cont*.




**Figure 11.** *Cont*.


**Figure 11.** Parametric analysis of inputs. (**a**) Age, (**b**) cement, (**c**) RHA, (**d**) water content, (**e**) superplasticizer, (**f**) aggregates*.* **Figure 11.** Parametric analysis of inputs. (**a**) Age, (**b**) cement, (**c**) RHA, (**d**) water content, (**e**) superplasticizer, (**f**) aggregates.

### The sublots in front of each graph in Figure 11 represent the constant parameters of **5. Conclusions**

parametric analysis for each input. The literature used for obtaining experimental values includes [12,35–39]. It can be observed from the results that when water is increased from a certain limit, a reduction in CS occurs. This is also obvious from previous studies. De Sensale [39] conducted research in which a water to cement ratio (w/c) of 0.4 gave more CS than w/c of 0.5. RHAP contributes towards the enhancement of strength, but when RHAP is increased by 30 percent, it results in decrease of compressive strength. This is due to the fact that, as discussed in Section 1, RHA contains 90 percent silica. By in-Different models for prediction of CS of RBC are developed in this study. The models developed in this study are based on wide range of data which consist of different parameters demonstrated by experimental studies that are available in the literature. The models considered the most influential parameters on CS as inputs. The results obtained in this research are closer to the experimental research. The following conclusions can be drawn from the obtained results:


Different models for prediction of CS of RBC are developed in this study. The models developed in this study are based on wide range of data which consist of different parameters demonstrated by experimental studies that are available in the literature. The models considered the most influential parameters on CS as inputs. The results obtained in this research are closer to the experimental research. The following conclusions can be Concrete containing RHA has a great potential to replace OPC concrete. It is recommended that extensive research be carried out by including more parameters. These parameters should include temperature, corrosion, and resistance to chlorine and acid attacks. Other advanced programming techniques such as an M5P tree and gene expression programming can be used to make further predictions.

drawn from the obtained results: 1. The PA has shown that the input parameters used in this research are effectively utilized by the model to predict the CS. Moreover, the statistical parameter R2 shows the accuracy of the data used for the training and validation of different models. 2. The R<sup>2</sup> for the predicted strengths of ANN, ANFIS, MNLR, and LR is 0.98, 0.89, 0.70, **Author Contributions:** A.I.: Conceptualization, data analysis, writing original draft and preparation, N.B.K.: Formal analysis and modelling, S.K.-u.-R.: Supervision, review and editing, M.F.J.: Investigation and review, F.A.: Methodology, review and editing, R.A.: Software, validation check and supervision, H.A.: Review, editing and supervision, A.M.: Validation, review, and supervision. All authors have read and agreed to the published version of the manuscript.

and 0.63,respectively. 3. It is evident by the comparison of ANN and ANFIS with the regression models that **Funding:** This research received no external funding.

both ANN and ANFIS have a high command on prediction of CS of RBC. Therefore, **Data Availability Statement:** Not applicable.

they are suitable for the predesign of RBC. **Acknowledgments:** Amir Mosavi would like to thank Alexander von Humboldt Foundation. **Conflicts of Interest:** The authors declare no conflict of interest.

### **Abbreviations**


### **Appendix A**

**Table A1.** Compressive strength (CS) (MPa) results obtained through different models.


**Table A1.** *Cont.*


**Table A1.** *Cont.*




### **References**

1. Chen, C.; Habert, G.; Bouzidi, Y.; Jullien, A. Environmental impact of cement production: Detail of the different processes and cement plant variability evaluation. *J. Clean. Prod.* **2010**, *18*, 478–485. [CrossRef]


**Yuqi Zhou 1,2, Jianwei Sun 2,3,\* and Zengqi Zhang <sup>4</sup>**


**Abstract:** Massive high-strength concrete structures tend to have a high risk of cracking. Ground slag powder (GSP), a sustainable and green industrial waste, is suitable for high-strength concrete. We carried out an experimental study of the effects of GSP with a specific surface area of 659 m2/kg on the hydration, pore structure, compressive strength and chloride ion penetrability resistance of high-strength concrete. Results show that adding 25% GSP increases the adiabatic temperature rise of high-strength concrete, whereas adding 45% GSP decreases the initial temperature rise. Incorporating GSP refines the pore structure to the greatest extent and improves the compressive strength and chloride ion penetrability resistance of high-strength concrete, which is more obvious under early temperature-matching curing conditions. Increasing curing temperature has a more obvious impact on the pozzolanic reaction of GSP than cement hydration. From a comprehensive perspective, GSP has potential applications in the cleaner production of green high-strength concrete.

**Keywords:** GSP; high strength; hydration; strength; penetrability

### **1. Introduction**

Currently, high-rise buildings have become increasingly widespread in China due to their advantages in space, stability and unique design [1,2]. They typically symbolize the landscape architecture and construction of a city, such as the Shanghai Tower (632 m), the Shenzhen Ping'an Finance Centre (599 m) and the China Zun in Beijing (528 m). The foundation structures of high-rise buildings with heavy loads are extremely deep and wide, which is typical for massive high-strength concrete structures [1]. During the hydration process, substantial hydration heat is generated in massive high-strength concrete, resulting in a high internal temperature rise because of its slow heat dissipation [3–6]. After hardening, large tensile stresses are formed due to restrained thermal and autogenous shrinkage deformations, which are the main driving forces of cracking in concrete [7].

Using supplementary cementitious materials (SCMs) to lower early heat and attendant volume changes is the most common preventative method [8–12]. The application of SCMs in concrete also has positive effects on workability, pumpability, strength and permeability resistance to chemical attacks [13–21]. Meanwhile, SCMs, as mineral admixtures to replace cement in high-strength concrete, reduce the carbon footprint in cement and concrete production and are conducive to sustainable development due to the conservation of natural resources [22–24]. Slag, as one of the most suitable SCMs, has been extensively identified and used to directly replace cement, minimizing cracking in massive concrete applications. Slag is a non-metallic residual generated from blast furnaces when iron is extracted from its ore [25,26]. Molten slag, comprising mostly silicates and alumina, is swiftly quenched with abundant water [27]. The rapid cooling method results in amorphous phases of slag (nearly

**Citation:** Zhou, Y.; Sun, J.; Zhang, Z. Effects of High-Volume Ground Slag Powder on the Properties of High-Strength Concrete under Different Curing Conditions. *Crystals* **2021**, *11*, 348. https://doi.org/ 10.3390/cryst11040348

Academic Editors: Yifeng Ling, Chuanqing Fu, Peng Zhang and Peter Taylor

Received: 7 March 2021 Accepted: 25 March 2021 Published: 29 March 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

80% content), which is responsible for its pozzolanic activity [28]. Compared to Portland cement, slag has a lower specific gravity [29]. The colour of slag varies from dark grey to white depending on the moisture inside, its chemical composition and its granulation efficiency [30]. The replacement rate for slag during the production of concrete varies from 30% to 85%, and 50% is typically used in most applications [10,31]. Incorporating as little as 30% slag can reduce the cumulative heat by 25% after the initial 48 h [31,32]. The cementitious activity of slag needs to be further improved for wider application. These inherent attributes are not easy to change, including the chemical composition, amorphous phase content and alkali concentration of the cement system [33,34]. However, the fineness of slag can be further enhanced by drying and subsequent grinding in a rotating ball mill to a finer powder with a specific surface area of 600 m2/kg−700 m2/kg, which is called "ground slag powder (GSP)" in this study.

Many studies have proven that the pozzolanic reaction rate is increased by improving the fineness of slag, which has a great impact on the development of strength and durability. He et al. [35] used two methods to prepare GSP, including the wet-milling method and the dry-separation method, to improve its early reactivity. They verified that the setting time gradually decreased with the dry-separation slag and increased with the wet-milling slag [35]. Moreover, a system containing wet-milling slag had higher electrical resistivity and better mechanical properties [35]. Liu et al. [36] investigated the contribution ratios of GSP to a GSP-cement-steel slag ternary cementitious material system and found that GSP caused an obvious improvement in the hydration and mechanical properties at every stage due to its close-packing effect [36]. Zhang et al. [37] found that the ultimate hydration heat initially increased and then decreased sharply with increasing proportions of GSP [37]. Meanwhile, adding GSP had a slight impact on chemical shrinkage but increased the chloride binding capacity [37]. Mohan et al. [38] conducted experiments on the influence of silica fume and GSP on the properties of self-compacting concrete. They found that incorporating GSP is a better way to decrease free shrinkage due to the diminished water withholding and improved sulphate and acid attack resistance of self-compacting concrete [38]. Pradeep Kumar et al. [39] reported a modification of the corrosion property of concrete with the use of GSP. Using GSP reduced the workability and water absorption of concrete, enhanced the bond strength of the steel rebar, and remarkably reduced rebar corrosion [39].

Based on the literature available, there is still a lack of research on the properties of GSP high-strength concrete. Hence, this study aims to investigate the feasibility of using GSP as a mineral admixture in high-strength concrete. In this paper, high-strength cement concrete with a design strength of C75 was prepared as the reference sample. Two substitution rates of GSP (25% and 45%) and two curing conditions (a standard curing condition and a temperature-matching curing condition) were selected. The adiabatic temperature rise, pore structure, hydration products, compressive strength and chloride ion penetrability resistance of high-strength concrete were measured. Effects of elevated early temperatures on the properties of plain cement concrete and GSP concrete were analyzed. The results of this study can provide considerable theoretical guidance for the use of GSP in massive high-strength concrete applications.

### **2. Materials and Methods**

### *2.1. Raw Materials*

Water-quenched blast-furnace slag powder was produced by Xingye Materials Co., Ltd., Xingtai, Hebei province, China. Portland cement with a strength grade of 42.5 was supplied by Jinyu Cement Co., Ltd., Beijing, China. The slag powder is further ground into GSP in the laboratory. The chemical compositions of raw materials are given in Table 1. The mass coefficient (K = w(CaO + MgO + Al2O3)/w(SiO<sup>2</sup> + MnO + TiO2)) of the GSP used was 1.97 according to the Chinese national standard (GB/T 203-2008). The specific surface areas of the cement and GSP were 341 m2/kg and 659 m2/kg, respectively. Coarse aggregates of concrete are crushed limestone of 5 to 20 mm in size. Fine aggregates of

concrete are river sand with a fineness modulus of 2.9. The workability of fresh concrete was adjusted using a polycarboxylate superplasticizer.

**Table 1.** Chemical compositions of the cement and ground slag powder (GSP)/%.


\* Na2Oeq = Na2O + 0.658K2O; LOI: loss on ignition.

### *2.2. Mix Proportions*

Table 2 shows the mix proportions of the high-strength concrete. The total amount of binder was 550 kg/m<sup>3</sup> . A water/binder ratio of 0.28 and a sand ratio of 0.43 were selected. Plain cement concrete was regarded as the reference sample (sample C). Two substitution rates of GSP (25% and 45% by mass) were used, corresponding to sample S25 and sample S45. Cubic concrete samples with side lengths of 100 mm were prepared. Each sample had a total of 36 square concrete specimens. The paste had the same water/binder ratio and substitution rates as the concrete. Fresh pastes were cast into plastic tubes and cured under the same curing conditions as the concrete.

**Table 2.** Mix proportions of high-strength concrete/kg·m−<sup>3</sup> .


### *2.3. Curing Conditions and Test Methods*

In this study, two curing conditions for the concretes and pastes were set—the standard curing condition (symbol S) and the temperature-matching curing condition (symbol M). Thus, sample SS25 represents S25 concrete cured under the standard curing condition, and sample MS45 represents S45 concrete cured under the temperature-matching curing condition. The standard curing condition required a constant temperature (20 ◦C ± 2 ◦C) and relative humidity (>95%). The temperature-matching curing condition needed to be adjusted according to the adiabatic temperature rise curve of the concrete. The adiabatic temperature rise curve of high-strength concrete for the initial 7 d was determined by a temperature measuring instrument (50 L) with an accuracy of ±0.1 ◦C.

The compressive strength and chloride ion penetrability resistance of concrete for each concrete mixture were obtained from an average of three specimens. The pastes were prepared for the tests of pore structure, Ca(OH)<sup>2</sup> (CH) content, and non-evaporable water content. First, the hardened paste was broken into small pieces (less than 5 mm). Then, the pieces were soaked in ethanol (Tongguang fine chemicals company, Beijing, China) for 24 h at test ages. Finally, all pieces were dried in the oven at 110 ◦C. For tests of CH content and non-evaporable content water content, dried pieces were further ground into ultrafine powder. The pore structure of hardened paste at ages of 28 d and 90 d was measured with a mercury intrusion porometer (MIP, AUTOPORE II 9220 manufactured by Micromeritics, America) with a maximum mercury intrusion pressure of 300 MPa. The CH content was determined by thermogravimetric (TG) and derivative thermogravimetric (DTG) analyses. TG and DTG curves were obtained using Instrument TGA 3+ (METTLER TOLEDO, Switzerland) in an N<sup>2</sup> atmosphere from 30 ◦C to 900 ◦C at 14 d, 28 d and 90 d. The non-evaporable water content W<sup>n</sup> was determined using the following Equation (1):

$$\mathbf{W\_n = (m\_1 - m\_2)/m\_1 - (1 - \alpha) \, LOL\_\mathbb{C} - \alpha LOL\_\mathbb{S}}\tag{1}$$

where m<sup>1</sup> is the dried mass of hardened paste at 110 ◦C, m<sup>2</sup> is the mass of hardened paste after heating at 1050 ◦C, α represents the replacement rate of GSP, and LOI<sup>C</sup> and LOI<sup>S</sup> represent the loss on ignition of cement and GSP, respectively. represent the loss on ignition of cement and GSP, respectively. **3. Results and Discussion** 

Wn = (m1 − m2)/m1 − (1 − α) LOIC − αLOIS, (1)

90 d. The non-evaporable water content Wn was determined using the following Equation

where m1 is the dried mass of hardened paste at 110 °C, m2 is the mass of hardened paste after heating at 1050 °C, α represents the replacement rate of GSP, and LOIC and LOIS

### **3. Results and Discussion** *3.1. Adiabatic Temperature Rise*  The adiabatic temperature rise curves of plain cement concrete and GSP concrete for

(1):

### *3.1. Adiabatic Temperature Rise* the initial 7 d are depicted in Figure 1. It is obvious that the growth trends of the three

*Crystals* **2021**, *11*, x FOR PEER REVIEW 4 of 18

The adiabatic temperature rise curves of plain cement concrete and GSP concrete for the initial 7 d are depicted in Figure 1. It is obvious that the growth trends of the three temperature curves are similar. The temperature rose sharply before 24 h and remained stable after 150 h. The final temperature rises of samples C, S25 and S45 were 56.56 ◦C, 60.58 ◦C and 54.07 ◦C, respectively. This illustrates that the incorporation of 25% GSP increases the adiabatic temperature rise by 4.02 ◦C and incorporating 45% GSP decreases the adiabatic temperature rise by 2.49 ◦C. Note that sample S25 exhibited the maximum temperature rise. This indicates that the promoting effect of GSP on cement hydration exceeds the negative effect due to cement reduction. The promoting effects derived from two main contributing factors [34]. One is the heterogenous nucleation effect [34]. Compared to cement particles, GSP has finer particles, of which the specific surface area is 659 m2/kg. Finer slag particles can serve as heterogeneous nucleation and crystallization sites of C–S–H gel and CH, thus improving the degree of cement hydration. The other is related to the higher reactivity of slag, which participates in pozzolanic reactions at an early stage, thus promoting cement hydration. The drop in the adiabatic temperature rise is attributed to a significant reduction in cement content. temperature curves are similar. The temperature rose sharply before 24 h and remained stable after 150 h. The final temperature rises of samples C, S25 and S45 were 56.56 °C, 60.58 °C and 54.07 °C, respectively. This illustrates that the incorporation of 25% GSP increases the adiabatic temperature rise by 4.02 °C and incorporating 45% GSP decreases the adiabatic temperature rise by 2.49 °C. Note that sample S25 exhibited the maximum temperature rise. This indicates that the promoting effect of GSP on cement hydration exceeds the negative effect due to cement reduction. The promoting effects derived from two main contributing factors [34]. One is the heterogenous nucleation effect [34]. Compared to cement particles, GSP has finer particles, of which the specific surface area is 659 m2/kg. Finer slag particles can serve as heterogeneous nucleation and crystallization sites of C–S–H gel and CH, thus improving the degree of cement hydration. The other is related to the higher reactivity of slag, which participates in pozzolanic reactions at an early stage, thus promoting cement hydration. The drop in the adiabatic temperature rise is attributed to a significant reduction in cement content.

**Figure 1.** Adiabatic temperature rise curves of concrete. **Figure 1.** Adiabatic temperature rise curves of concrete.
