1. Introduction
Self-compacting concrete has been used in a variety of civil engineering applications because of its superior flowability, mechanical strength, and durability [
1,
2]. It has greater application in the areas of underwater concreting; structural members involving heavy reinforcement; solving unskilled labor issues in the construction sector; eliminating the need for vibration; and reducing construction time, labor costs, and noise pollution [
3,
4]. Generally, SCC consists of a high volume of cement with a low volume of coarse aggregate, and a superplasticizer is used to reduce the water–binder ratio. Environmental and working conditions have substantially improved because of the development of SCC, since there is lower energy consumption, less vibration, higher productivity, less noise, reduced health hazards, and so on [
1,
5].
SCC with low density can be produced by partially or completely replacing coarse aggregates with natural or synthetic lightweight aggregates. Lightweight expanded clay aggregate (LECA) is a versatile porous ceramic material manufactured by firing the natural mining clay in a rotary kiln. LECA is utilized in a vast array of applications, including hydroponics, geotechnical fillers, lightweight concrete, and thermal and acoustic insulation. A plethora of microscopic air-filled voids accounts for LECA’s lightweight, thermal, and acoustic isolation properties [
6,
7]. Compared with other kinds of lightweight aggregates, such as pumice, scoria, perlite, etc., LECA can provide a smoother concrete surface. LECA pretreatment with cement and silica fume paste provides it with the requisite qualities for SCC since aggregate strength has the greatest influence on the compressive strength of concrete and LECA has less aggregate strength [
8,
9].
Currently, several cement substitutes are employed, notably including fly ash, ground granulated blast-furnace slag (GGBS), and metakaolin. The properties of waste materials can vary widely, which can make it difficult to design and produce SCC with consistent properties [
10,
11]. GGBS is a non-metallic byproduct consisting of aluminosilicates and calcium silicates that are acquired in a molten state with iron in a blast furnace [
12,
13,
14]. GGBS has the potential to improve both the fresh and hardened qualities of SCC while also promoting sustainability. GGBS delays the setting time of concrete, resulting in an increase in strength over time. Concrete-containing GGBS can reduce heat generation during the hydration process while also improving resistance to sulphate attacks, making it suitable for marine construction [
15,
16]. Oner et al. (2007) [
17], upon investigation of 32 distinct SCC mixes containing GGBS, discovered that when GGBS content increases, the water-to-binder ratio tends to decrease for the same consistency, demonstrating that GGBS has a positive effect on consistency. The CS of concrete mixtures containing GGBS likewise increased substantially as the quantity of GGBS replacement increased. Research by Leelavathi et al. (2021) [
18] explored the potential of GGBS as a cement substitute in the production of SCC at 10, 20, 30, 40, and 50% by weight of cement. SCC substituted with 20% GGBS was accepted as an optimal concrete mix compared with other proportions. The addition of GGBS increases the paste volume, which reduces the friction between the paste and the aggregate particles, thus improving the fluidity of the mix.
By replacing concrete constituents with less expensive, recycled, or more sustainable materials, environmental impacts can be minimized. The use of waste materials in SCC is a promising way to reduce the environmental impact of concrete production [
19,
20]. Biomedical waste poses a significant environmental problem among many other types of waste simply because it is potentially hazardous. The waste produced by hospitals, diagnostic centers, and biological laboratories is collectively called biomedical waste. Incineration is the ideal treatment option for biomedical waste, despite the fact that alternative techniques such as carbon filtration, chemical coagulation, biological oxidation, membrane filtering, and others have been utilized to address this hazardous waste [
21,
22]. The type of ash emitted and stockpiled in such waste incineration facilities is known as incinerator biomedical waste ash (IBMWA). IBMWA is typically disposed of in landfill to prevent it from spreading into the environment. However, several studies demonstrate the potential for using it to fabricate building materials like bricks, mortar, concrete, asphalt, etc. [
23,
24,
25]. Aubert et al. (2004) [
23] explored the application of IBMWA as a substitute for fine aggregate in concrete production. The durability and CS of hardened concrete were unaffected by the utilization of IBMWA as a supplementary material in concrete [
26].
In general, the CS of SCC is determined through physical experimentation, which is expensive and time-consuming. Recent technological innovations have made it possible to handle such engineering problems using alternate methods, which include numerical simulation, empirical regression, and the use of machine-learning (ML) techniques. The prediction of the CS of SCC, in particular, could be addressed by the development of regression models based on machine learning, using specific paradigms that can learn from the input data and offer extremely precise results. Several ML approaches, including neural networks, ensemble methods, and generalized additive models are used to forecast the compressive strength of SCC [
27,
28]. Research on the use of neural networks to forecast the compressive strength of various types of concrete mixes began at the end of the 1990s, and neural networks have proven to be quite successful in doing so [
29,
30]. Since a substantial body of research on the application of several machine-learning paradigms to estimate/predict the compressive strength of different types of concrete exists, it was thought prudent to present a review of some recent literature related to SCC. In a study by Asteris et al. (2016) [
31], multilayer feed-forward neural networks predicted the 28-day CS of admixture-based SCC with low significant error rates and computational cost. Likewise, Yaman et al. (2017) [
32] applied multi-input–multi-output neural network models for predicting SCC’s ingredients (model outputs) based on its hardened and fresh properties (model inputs). A hybrid model that includes a beetle antennae search (BAS) algorithm with a random forest (RF) was developed by Zhang et al. (2019) [
33] to predict the uniaxial compressive strength of lightweight SCC. The predictive capabilities of genetic programming (GP) and artificial neural networks (ANN) were comparatively evaluated by Awoyera et al. (2020) [
34] to estimate the strength properties of geopolymer SCC with mineral admixtures. Similarly, Farooq et al. (2021) [
35] modeled the CS of SCC modified with fly ash by implementing support-vector-machine (SVM), ANN, and gene-expression-programming (GEP) methods. Based on experimental datasets gathered from prior studies, recent research by Hoang (2022) [
36] employed Levenberg–Marquardt artificial neural networks (LM-ANN), genetic programming (GEP), deep neural network regression (DNNR), support vector regression (SVR), extreme gradient boosting machines (XGBoost), adaptive boosting machines (AdaBoost), and gradient boosting machines (GBM) to predict the CS of SCC. The DNNR model outperformed the other models in his tests for predicting the CS of SCC. In recent times, novel ensemble methods and hybrid ML approaches have been intensively investigated for predicting the compressive strength of SCC [
28,
37] and in other domains of structural and transportation engineering [
38,
39].
The current research was conducted in two phases. In the experimental phase, GGBS, IBMWA, and LECA were substituted for cement, fine aggregate, and coarse aggregate, respectively, in quantities ranging from 10% to 30% by weight, with a 10% increment. To produce SCC, two different water-to-binder ratios—0.4 and 0.45—were adopted. The mix designs comprise permutations and combinations of various material substitutions. In the second phase, machine-learning models were developed for simulating the CS of the SCC produced. Extensive effort was expended in compiling experimental data (384 samples), which was then used for modeling. To the best of the authors’ knowledge, only limited research has been undertaken on ML modeling of the compressive strength of SCC. For the first time, the CS of SCC produced by blending GGBS, IBMWA, and LECA is modeled utilizing the most recent ML techniques, such as gradient tree boosting and CatBoost Regression.