3.1. Mix design
After obtaining the basic test results of the materials, M30 grade SCC was designed as per the IS 10262: 2019 [35] guidelines. The GGBS, IBMWA and LECA were considered for substitution with cement, fine aggregate, and coarse aggregate, respectively, in quantities ranging from 10% to 30% by weight, with a 10% increment. The superplasticizer (SP) (%) was pre-set for two distinct water to binder (W/B) ratios, specifically, the 0.4 W/B ratio had 1.4% SP and the 0.45 W/B ratio had 0.6% SP. Mix design codes, such as SCC 0-0-0 (which stands for self-compacting concrete with 0% LECA, 0% GGBS, and 0% IBMWA), have been coined for ease of identification. The code and mix design to produce 1m3 volume of SCC are both detailed in Annexure 1. In a concrete mixer, each SCC mix was thoroghly mixed until a consistent mixture was produced. The fresh mix was poured into a standard 150 mm cube molds without vibration. All casted moulds were cured for 28 days in a curing tank before being tested in a compression testing machine.
3.2. Experimental Testing
3.2.1. Fresh properties of concrete
The workability of self-compacting concrete was evaluated by four tests: Slump flow, J-Ring, L-Box, and V-Funnel.
Slump flow
It is one of the most widely used tests and provides an accurate assessment of the filling ability of SCC. In the absence of barriers, the slump flow is utilized to evaluate the horizontal free flow of SCC. The test technique is based on the approach used to determine slump. The diameter of the concentric spread of concrete serves as a measure of the concrete's filling capacity. However, it does not indicate the concrete's capacity to move between reinforcements without being blocked. The slump flowability of SCC is shown in Figure 5.
J-Ring
In this test, the SCC sample is allowed to flow/spread in all directions while being restrained by a circular arrangement of reinforcing bars. The flow is solely produced by gravity in the basic setup, such as that recommended by European standards. Due to the blocking effect of the reinforcement bars, the SCC's limited deformability is reflected by the J-Ring flow spread, and the flow time shows the rate of spread within a specified flow distance. The J-ring flowability of SCC is shown in Figure 6.
V-funnel
The V-Funnel test is conducted to evaluate the filling ability of SCC with a maximum aggregate size of 20mm. The V-Funnel is filled with 12 liters of concrete, and the time it takes for the concrete to flow through the V-Funnel is measured. The funnel may then be loaded with concrete and placed aside for 5 minutes to set. If the concrete segregates, the flow time, which is slightly correlated with the plastic viscosity of SCC, will be significantly increased. The V-Funnel passing ability is shown in Figure 7.
L-Box
An 'L'-shaped shaped box with vertical and horizontal chambers that are separated by a moveable gate, and in front of which vertical reinforcement bars are mounted was used in this experimental study. After pouring concrete into the vertical portion, the gate is raised to allow concrete to flow into the horizontal area. The fraction of the level of the concrete at the end of the horizontal portion and the level that remains near the vertical part after the flow ceases are taken into account. This reflects the resting slope of the concrete. This test measures the passing ability or the amount of concrete that can travel between the bars. The L-box passing ability is shown in Figure 8.
3.2.2. Hardened properties of concrete
The compressive strength of 28-days-cured cubes was determined. Concrete’s compressive strength is one of its primary mechanical properties. The compressive strength measured in megapascals is computed by dividing the ultimate load withstood by the cube's cross-sectional area.
3.3. Theoretical Overview
3.3.1. Artificial Neural Network (ANN)
Among a variety of machine learning techniques, ANN always stands out among others in estimating or approximating or predicting tasks owing to its capability of data mining from supervised learning using an empirical risk minimization framework. In the present study, a multilayer perceptron (MLP) network based on a backpropagation learning strategy was considered. The MLP network was comprised of 3 layers: the input layer (with 6 input parameters), the hidden layer and the output layer (with 1 parameter). Based on trial and error, the ideal number of nodes in the hidden layer was adjusted. Each node-to-node connection assigned with weights was fine-tuned to minimize the loss function using the optimization algorithm termed ‘Levenberg-Marquardt optimization’. Tansig, logsig, and purelin were the activation functions employed. The MLP network with the least RMSE and highest correlation coefficient during testing phase was considered to be the optimal network [36,37]. For further information on the ANN algorithm, its various structures, network optimization, and so forth, refer to the following literature [38,39].
3.3.2. Gradient Tree Boosting (GTB)
Friedman [40] introduced the gradient tree boosting (GTB) model, which is an iterative decision tree algorithm that takes into account the regression trees (weak/base learners) that are ensembled by gradient boosting. Weak learners are commonly employed in boosting since they get trained faster. To reduce generalisation error, weak learners work sequentially in GTB to optimize the bias-variance tradeoff. Each tree tries to improve on the predictive error from the previous tree and uses the computed pseudo-residual as the dependent variable to update the next tree and again determine the new pseudo-residual. By reiterating these steps, the final model is built sequentially by aggregating the activated outputs of each tree with the corresponding boosting weights following a gradient descent approach. While building the GTB regressor, the main parameters used are ‘loss' and ‘n_estimators’. In this context, ‘loss’ denotes the value of the loss function that needs to be optimized. The parameter ‘n_estimators’ determines the number of weak learners. The hyper-parameter labelled 'learning_rate' [in the range of (0 -1)] would avoid overfitting via shrinkage. For further information on the GTB algorithm, its stochastic gradient boosting, shrinkage, regularization, Tree constraints, and so forth, refer to the following literature [41,42].
3.3.3. CatBoost Regressor (CBR)
CatBoost does, in fact, belong to the family of gradient boosting decision trees. Cat Boost excels at machine learning tasks since it implements oblivious decision trees to handle heterogeneous data. CatBoost, unlike other boosting algorithms, includes symmetric trees (balanced trees). In each successive step, the leaves from the prior tree are split using the same criterion. In the whole balanced tree, the feature-split pair with the minimal loss (as determined by a penalty function) is picked and applied to all the level's nodes. The Catboost architecture promotes efficient CPU implementation, reduces prediction time, and regulates overfitting. To prevent overfitting, CatBoost employs the ordered boosting technique, a permutation-driven strategy, to train the model on a subset of the data while computing residuals on a different subset. CatBoost indeed has some common training parameters with GTB but provides a much more flexible interface for fine-tuning parameters. CatBoost's sensitivity to hyperparameters and their adjustments must be carefully considered in order to build a robust model. For further information on the CatBoost algorithm, its implementation, and so forth, refer to the following literature [43,44].
3.3.4 Model Development
Machine learning methods necessitate the division of dataset into training and testing subsets. During model development, the model is calibrated using the training dataset by optimizing its hyperparameters. Finally, the testing dataset is utilized to demonstrate the model's performance in estimation/prediction. The dataset for the current study included 384 samples with 6 input variables and 1 output variable. Multivariate regression analysis was considered to be implemented, and hence all the SCC constituents were considered as input parameters for the model building process. The input variables include SCC ingredients: LECA (%), GGBS (%), IBMWA (%), density of SCC (kg/m3), water to binder (W/B) ratio, superplasticizer (SP) (%) and the compressive strength (MPa) of SCC was considered as the output parameter. Knowing that GGBS, IBMWA, and LECA were replaced for cement, fine aggregate, and coarse aggregate, respectively, it is obvious that the cement (%) may be simply computed if the GGBS (%) is known, and vice versa. Hence, the SCC constituents, namely cement (%), fine aggregate (%) and coarse aggregate (%) were excluded as input parameters. The whole dataset was randomly divided into 70% samples as training dataset and 30% samples as testing dataset for the modeling of the CS of SCC. The descriptive statistics of the model parameters are as mentioned in Table 8. Three ML approaches, namely the ANN, GTB, and CBR were developed in this study to simulate the CS of SCC. The hyperparameters of all the models tuned by trial and error approach during the model development process are presented in Table 9.
3.4. Performance evaluation
The performance of developed ML models was evaluated using statistical indices such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Willmot Index (WI), Nash Sutcliffe Efficiency (NSE), Kling Gupta Efficiency (KGE), and Symmetric Mean Absolute Percentage Error (SMAPE). The mathematical formulations of these statistical metrics are presented below,
Mean Absolute Error (MAE):
![](data:image/png;base64,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)
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)