**1. Introduction**

The world is making progress by leaps and bounds. New technologies and innovations are being introduced every day in every field. These advancements have altered the course of human history. One of the main aspects that has played a crucial role in shaping modern human civilization is infrastructure. From caves, mankind has started to live in strong and pleasing dwellings made by their own creative and innovative minds. Still, today infrastructure is considered to be the main element for progress in any country. The construction material that is used in abundance throughout the world for the construction of infrastructure is cement. However, along with the advantages of cement there are also certain adverse effects. Cement is said to be responsible for seven percent of the total carbon dioxide emissions worldwide [1]. It produces carbon dioxide while reacting when water is added to it. Secondly, a high temperature is required during the production of cement [2]. This high temperature is achieved by burning fossil fuels which increase the carbon footprint of cement. Our planet earth is suffering from problems of grave danger. Environmental deterioration and global warming are some of these alarming issues. If not

**Citation:** Iqtidar, A.; Bahadur Khan, N.; Kashif-ur-Rehman, S.; Faisal Javed, M.; Aslam, F.; Alyousef, R.; Alabduljabbar, H.; Mosavi, A. Prediction of Compressive Strength of Rice Husk Ash Concrete through Different Machine Learning Processes. *Crystals* **2021**, *11*, 352. https:// doi.org/10.3390/cryst11040352

Academic Editors: Shujun Zhang and Yifeng Ling

Received: 13 February 2021 Accepted: 22 March 2021 Published: 29 March 2021

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controlled in due time, these problems will push the earth to the brink of extinction. One of the major causes of environmental degradation and global warming is said to be the emission of carbon dioxide from different products and processes [3,4]. Since cement is a crucial contributor to the total carbon dioxide emissions of the world, the importance of infrastructure cannot be undermined. It must be replaced with some other material that has a smaller carbon footprint as well as possessing the same or better properties than cement.

The materials that replicate the properties of ordinary Portland cement (OPC) are known as secondary cementitious materials (SCMs). They have smaller carbon dioxide emission rates [5]. SCMs are generally waste materials and byproducts of different industries. These materials become sources of various types of pollution if not discarded or utilized properly. SCMs can be used in different proportions and combinations to replicate the desired properties of OPC. Some of the SCMs are fly ash (FA), corn cob ash (CCA), sugarcane bagasse ash (SCBA), rice husk ash (RHA), ground granulated blast furnace slag (GGBFS), etc [6–9]. RHA is one of the SCMs obtained from the agricultural waste of rice crop. Rice grains are covered in rice husks (RH) which are used as a fuel to boil paddy in rice mills. RHA is obtained after utilizing rice husks as fuel. It contains more than 90 percent silica and can be used successfully as an SCM to synthesize concrete [10]. An illustration of the chemical composition of RHA is shown in Figure 1 [11]. Ameri et al. [12] conducted a research on concrete containing RHA. It was found that concrete containing RHA showed a vigorous increase in early compressive strength. However, by increasing the RHA content by more than 15 percent, the compressive strength was decreased. This is attributed to the excess amount of silica present in RHA which remains unreacted. The compressive strength of concrete with RHA as an SCM was 9, 12, 13, and 16 percent higher than that of control mix. Similarly, Chao Lung et al. [13] incorporated RHA in concrete and concluded that concrete containing RHA showed a strength 1.2 to 1.5 times greater than that of the control mix. Chindaprasirt et al. [14] tested the concrete containing RHA for sulphate attack resistance and reported that concrete containing RHA proved to be highly effective against sulphate attack. It was reported by Thomas et al. [15] in a review paper that concrete containing RHA has a dense microstructure, so it can be used to reduce the water absorption of concrete by up to 30 percent. Rattanachu et al. [16] conducted research in which grounded RHA was used with steel reinforcements. It was observed that the use of RHA in the presence of steel resisted the corrosion of steel due to the fine structure of RHA. Thus, several studies have been made on environmental impact of RHA. They are reported in Table 1:


**Table 1.** Environmental impact of rice husk ash (RHA).

Hence, RHA can be utilized successfully as a cementitious material. RHA does not produce excessive amount of carbon dioxide. It can be used as a structural concrete. Not only does it contribute towards the strength of the concrete but also towards the long term durability properties of concrete [21].

**Material Used No. of Data** 

Recycled concrete aggregate

Fly ash (FA) and blast

**Points**

SCBA <sup>65</sup> Compressive

Silica fume (SF) and zeolite <sup>18</sup> Compressive

Recycled rubber concrete <sup>72</sup> Compressive

Cellular concrete <sup>99</sup> Compressive

furnace slag (BFS) <sup>135</sup> Compressive

**Figure 1.** Chemical composition of RHA.

**Figure 1.** Chemical composition of RHA*.* The rate of environmental deterioration does not allow one to spend an extensive amount of time on research and development of RHA blended concrete (RBC). Consequently, extensive lab works cannot be carried out on RBC. Along with that there is always an uncertainty regarding the mix design of RBC. This is due to the hygroscopic nature of RHA. Therefore, to predict the properties of different SCMs, artificial intelligence (AI) is being used throughout the globe. AI is used by different researchers to as-The rate of environmental deterioration does not allow one to spend an extensive amount of time on research and development of RHA blended concrete (RBC). Consequently, extensive lab works cannot be carried out on RBC. Along with that there is always an uncertainty regarding the mix design of RBC. This is due to the hygroscopic nature of RHA. Therefore, to predict the properties of different SCMs, artificial intelligence (AI) is being used throughout the globe. AI is used by different researchers to assess and predict the strength of concrete mixes. Table 2 lists the different previous studies conducted on SCMs to predict different properties. Different techniques such as artificial neural networks (ANN), LR, adaptive neuro-fuzzy inference system (ANFIS), and MNLR are used to successfully model and predict different properties of materials [22,23].

sess and predict the strength of concrete mixes. Table 2 lists the different previous studies conducted on SCMs to predict different properties. Different techniques such as artificial neural networks (ANN), LR, adaptive neuro-fuzzy inference system (ANFIS), and MNLR are used to successfully model and predict different properties of materials [22,23]. As AI research depends on mathematical modelling and parameters, it is a complex programming work and needs great optimization and care. Therefore, four program-As AI research depends on mathematical modelling and parameters, it is a complex programming work and needs great optimization and care. Therefore, four programming techniques are being used to predict the compressive strength of RHA-based concrete in this research. These techniques are ANFIS, ANN, MNLR, and LR. To achieve the targeted accuracy and to cater the complexity of programming these four techniques will be compared with each other. A vast database of peer reviewed literature is used to model the prediction of compressive strength.

ming techniques are being used to predict the compressive strength of RHA-based concrete in this research. These techniques are ANFIS, ANN, MNLR, and LR. To achieve the targeted accuracy and to cater the complexity of programming these four techniques will be compared with each other. A vast database of peer reviewed literature is used to

**dicted Modelling Technique Used Reference**

strength ANN, Response Surface Methodology (RSM) [24]

strength Backpropagation Neural Network (BPNN) [26]

strength ANN [27]

GEP, Multiple Linear Regression (MLR), Multiple

ANN, MNLR, ANFIS, Support vector machine

Nonlinear Regression (MNLR) [22]

ANN [23]

(SVM) [25]

Foamed concrete 91 Compressive Extreme Learning Machine (ELM) [28]

**Table 2.** Some recent studies using AI*.*

model the prediction of compressive strength.

**Property Pre-**

strength

Strength

strength

<sup>17</sup> Compressive


**Table 2.** Some recent studies using AI.
