Equation-driven strength prediction of GGBS concrete: a symbolic machine learning approach for sustainable development

This study employs a structured machine learning framework to address the challenge of accurately predicting the compressive strength (CS) of GGBS-based concrete. Given the nonlinear, multivariable nature of strength development in such sustainable mix designs, conventional empirical models often fail to deliver reliable results across different mix conditions. Therefore, the proposed framework integrates diverse algorithmic strategies to enable data-driven prediction with improved accuracy, generalizability, and practical utility.

The framework is centred around three core machine learning paradigms, such as MLP, a type of deep feedforward neural network capable of modelling highly nonlinear relationships. AdaBoost is an ensemble learning method that enhances the performance of weak learners through sequential reweighting. GEP is an evolutionary algorithm that produces symbolic models offering mathematical interpretability. The objective is to not only compare the predictive capabilities of these models using a real-world dataset of GGBS-based concrete but also to explore their strengths in terms of accuracy, consistency, and model transparency. The research workflow includes data acquisition, preprocessing, model development and validation, performance evaluation using multiple statistical metrics, and practical deployment of the most effective model. By implementing this framework, the study aims to contribute a replicable, scalable methodology for ML-based prediction of concrete performance and to support the design of more sustainable and optimised cementitious composites.

The final dataset comprises 787 experimental records collected from published studies [65], [66], [67]. Each record represents a unique mix design, incorporating Ground Granulated Blast Furnace Slag as a partial cement replacement, with the CS (in MPa) measured at different curing ages. The dataset includes a wide range of mix compositions and curing durations, offering a robust basis for machine learning model development and generalisation. Key input features extracted across the sources include cement content, GGBS content, water content, fine and coarse aggregates, water-to-binder ratio (W/B), and curing age. These variables are widely recognised as critical determinants of concrete strength, particularly when incorporating SCMs such as GGBS. The dataset reflects real-world practices in sustainable concrete mix design, particularly in contexts where reduced carbon emissions and improved durability are desired. All values were cross-checked for consistency and formatted for use in Python-based modelling workflows. Full references to the data sources are provided in the appropriate section to ensure transparency and reproducibility.

To explore the distributional characteristics of the input features, box plots were generated for all variables used in predicting CS (Figure 2). Most parameters, including cement, GGBS, and aggregate content, exhibit wide ranges with several outliers, particularly in superplasticizer and W/C ratios. These variations reflect the heterogeneity of mix designs compiled from literature and emphasise the need for robust model training to accommodate such diversity. Moreover, the pairplot of the input parameters used in the study is shown in Figure 3, which shows the distribution and pairwise relationships among the variables and CS. However, the descriptive statistics for the same parameters are depicted in Table 1.

Figure 2:

Box plots showing the distribution and variability of input parameters used for predicting CS of GGBS-based concrete.

Figure 3:

Pairplot of the GGBS concrete dataset showing the distribution and pairwise relationships among input variables and CS.

The compiled records encompass a broad range of mix compositions and curing conditions reported across the selected studies, including GGBS replacement levels between 0 % and 80 %, curing ages of 3–365 days, and water-to-binder ratios ranging from 0.24 to 0.75. Cement, aggregate, and admixture quantities also show high variability, with coefficients of variation exceeding 20 % for most parameters. Such dispersion confirms the dataset’s suitability for capturing diverse mix behaviours and realistic material variability required for machine-learning-based modelling.

The raw dataset, retrieved from multiple published literature sources, was initially compiled and formatted for compatibility with Python-based ML workflows. Basic preprocessing steps were conducted to ensure consistency in units and variable representation across all records. No imputation or categorical encoding was required since all features were numerical and complete. It is essential to distinguish between the two water ratios used in this study: the water-to-binder ratio (W/B) includes cement and GGBS in the denominator, whereas the water-to-cement ratio (W/C) considers only cement. Including both ratios enables the models to capture how GGBS replacement modifies effective water demand and hydration behaviour compared with conventional mixes.

Exploratory data analysis (EDA) was performed using histograms and pair plots to assess each feature’s distribution and identify potential correlations and outliers visually. Histograms revealed the spread and skewness of key input variables such as cement, GGBS, water content, and curing age. Pair plots enabled the identification of linear and nonlinear trends between predictors and the target variable (compressive strength), aiding in understanding variable importance and potential multicollinearity. Feature scaling or normalisation was not applied at this stage, as the selected machine learning models are either inherently robust to unscaled inputs (e.g., decision-tree-based AdaBoost) or can tolerate varied feature magnitudes with proper regularisation. No additional derived features were introduced beyond those provided in the dataset. However, attention was given to preserving meaningful predictors such as the water-to-binder ratio (W/B), which was explicitly included as an input feature. The dataset was randomly partitioned into training (70 %) and testing (30 %) subsets, ensuring representative distribution across the target variable. This split was used consistently across all machine learning models to allow fair and comparable evaluation of predictive performance. A 70/30 train test split was selected due to its proven balance between providing adequate data for training and retaining sufficient unseen data for reliable generalisation. Alternative ratios of 80/20 and 75/25 were also examined and yielded consistent performance trends, confirming the stability of the developed models.

The ML pipeline implemented in this study was designed to systematically develop, train, and evaluate three distinct algorithms: MLP, AdaBoost, and GEP. All modelling work was performed using Python 3.9, executed within the Spyder IDE as part of the Anaconda Navigator distribution. This environment offered robust package management and reproducibility, with core libraries including scikit-learn (for MLP and AdaBoost), NumPy, Pandas, Matplotlib, and gplearn (for GEP implementation). After data preprocessing, the dataset was split into training (70 %) and testing (30 %) subsets using randomised sampling to avoid sampling bias. Each model was trained exclusively on the training set, with hyperparameter tuning performed via grid search and cross-validation where applicable. MLP was configured as a feedforward neural network with multiple hidden layers. The number of neurons, activation functions (e.g., ReLU), learning rate, and epochs were optimised to prevent underfitting or overfitting. AdaBoost was implemented using a series of decision tree regressors as weak learners. The number of estimators and the learning rate were tuned to balance bias and variance. GEP, a symbolic regression approach, was employed to derive an interpretable mathematical expression representing the relationship between input features and CS. Parameters such as the number of genes, head size, and function set were configured to optimise model expressiveness and convergence.

All models were evaluated on the same testing set using consistent metrics for fair comparison. The modular pipeline structure allowed for easy replication, experimentation, and adaptation of models, ensuring flexibility for further research or real-world deployment. The graphical representation of the model pipeline for the presented research work is shown in Figure 4.

Figure 4:

Graphical representation of the model’s pipeline adopted for the presented research work.

Multiple evaluation metrics were used to assess the predictive accuracy and robustness of the ML models developed in this research. These metrics were selected to capture different aspects of model performance, ranging from overall fit to error magnitude, and to facilitate meaningful comparison between the Multilayer Perceptron (MLP), AdaBoost, and Gene Expression Programming (GEP) models. The primary performance indicators used include:

The model’s predictive performance was evaluated using three key metrics. The Coefficient of Determination (R²) quantifies how well the model explains variability in the target variable, with values approaching one indicating a strong fit. The Root Mean Square Error (RMSE) assesses prediction accuracy by emphasising larger deviations through the squared error component. Meanwhile, the Mean Absolute Error (MAE) offers a straightforward, scale-dependent measure by averaging the absolute differences between predicted and observed values. Table 2 presents a detailed overview of the performance indicators employed to evaluate the proposed ML models’ predictive efficiency comprehensively. These metrics were computed using the test dataset to ensure unbiased evaluation, with all values rounded to three decimal places for clarity. In addition to quantitative metrics, residual plots, prediction versus actual scatter plots, and histograms of error distribution were generated to assess model behaviour and error patterns visually. Such plots help in identifying overfitting, heteroscedasticity, or systematic under- or over-prediction. Comparative analysis has been done to determine which model offered the most balanced performance across all metrics. Emphasis was placed not only on high R² values but also on minimising RMSE and MAE, thereby ensuring that the selected model was both accurate and practically reliable for real-world strength prediction tasks.

Table 2:

Metric	Formula	Description
Mean Absolute Error (MAE)	MAE = 1 n ∑ i = 0 n y i − ŷ i $\frac{1}{\mathrm{n}}\sum _{\mathrm{i}=0}^{\mathrm{n}}\left\vert {\mathrm{y}}_{\mathrm{i}}-{\hat{\mathrm{y}}}_{\mathrm{i}}\right\vert $	Average magnitude of prediction errors;measures absolute deviation between predicted and experimental values.
Mean Squared Error (MSE)	MSE = 1 n ∑ i = 0 n y i − ŷ i 2 $\frac{1}{\mathrm{n}}\sum _{\mathrm{i}=0}^{\mathrm{n}}{\left({\mathrm{y}}_{\mathrm{i}}-{\hat{\mathrm{y}}}_{\mathrm{i}}\right)}^{2}$	Penalises larger deviations more strongly by squaring error terms.
Root Mean Squared Error (RMSE)	RMSE= 1 n ∑ i = 0 n y i − ŷ i 2 $\sqrt{\frac{1}{\mathrm{n}}\sum _{\mathrm{i}=0}^{\mathrm{n}}{\left({\mathrm{y}}_{\mathrm{i}}-{\hat{\mathrm{y}}}_{\mathrm{i}}\right)}^{2}}$	Square root of MSE;interpretable in the same units as the target variable.
Adjusted R2	Adj.R2 = 1 − 1 − R 2 n − 1 n − p − 1 $-\frac{\left(1-{\mathrm{R}}^{2}\right)\left(\mathrm{n}-1\right)}{\mathrm{n}-\mathrm{p}-1}$	Adjust R2 for the number of predictors (ρ) to account for model complexity and avoid overfitting.

where y _i  = actual value, y ˆ i ${\hat{y}}_{i}$  = predicted value, y ˉ $\bar{y}$  = mean of actual values, n = number of samples, and p = number of predictors.

Table 2 presents a detailed overview of the performance indicators employed to evaluate the proposed ML models’ predictive efficiency comprehensively [68].

Moreover, the execution process adopted in the study of the 5-fold validation approach is shown in Figure 5.

Figure 5:

The adopted 5-fold validation approach for the research work indicates the process.

Interpretability analyses were performed post-model training to improve model transparency and provide engineering-relevant insights. Local Interpretable Model-Agnostic Explanations (LIME) were used to assess the contribution of input variables to individual predictions. At the same time, permutation-based sensitivity analysis quantified the global impact of each feature across the dataset. These complementary techniques offered both localised and generalised understanding of model decisions, reinforcing the reliability of ML-driven strength predictions for GGBS concrete.

GEP is a nature-inspired evolutionary algorithm that evolves computer programs or mathematical expressions capable of modelling intricate relationships within a dataset. In the context of this research, GEP was implemented to derive symbolic expressions linking the mix design variables to the target output, compressive strength. The model operates by generating a population of chromosomes composed of genes, which subsequently evolve through selection, mutation, and crossover operations. The final model not only enables accurate prediction but also offers a transparent mathematical formulation, facilitating interpretability and integration into design frameworks. The prediction process by GEP is shown in Figure 6 [69].

Figure 6:

Flowchart indicating the execution process of the GEP model [69].

AdaBoost enhances predictive accuracy by iteratively combining multiple weak learners, such as decision stumps, into a single strong model. In this work, AdaBoost was employed to iteratively improve prediction accuracy by minimising the residual error of each subsequent learner. The model assigns higher weights to poorly predicted samples in each iteration, enabling the ensemble to focus on challenging data points. Its strength lies in enhancing generalisation while maintaining computational efficiency, making it suitable for structured datasets such as those derived from concrete mix designs. The steps involved in the execution process for the predicted outcomes by AdaBoost is shown in Figure 7 [70].

Figure 7:

Steps involved for the final prediction by AdaBoost model [70].

MLP is a class of feedforward artificial neural networks designed to approximate complex nonlinear functions. As illustrated in Figure 8, the MLP model consists of an input layer corresponding to the selected mix parameters, followed by two hidden layers with non-linear activation functions that capture the complex relationships between variables [71]. The model is trained using backpropagation, where weights are iteratively updated to minimise prediction error. The final single-neuron output layer produces the estimated compressive strength. This workflow ensures that both direct and interactive effects of input parameters are effectively learned by the network. The model’s design includes an input layer for the input variables, followed by one or more hidden layers using ReLU activations, and a single-node output layer for compressive strength estimation. MLP was trained using the backpropagation algorithm with mean squared error as the loss function. Input features were normalised to improve convergence. Its data-driven nature and capability to learn hidden patterns without requiring predefined equations make MLP an effective tool for modelling material behaviour.

Figure 8:

Schematic workflow of the MLP model showing the flow of data from input parameters through hidden layers to the output node responsible for compressive strength prediction [71].

Together, these models offer complementary strengths: GEP provides symbolic interpretability, AdaBoost enhances predictive robustness through boosting, and MLP captures deep nonlinear associations in the data. Their collective application offers a multi-perspective approach to modelling the CS of GGBS-based concrete. The flowchart of the MLP model for predicting the outputs is depicted in Figure 8.

The GEP model demonstrated a strong predictive capability for estimating the CS of GGBS-based concrete, as evidenced by the correlation between the experimental and forecasted results shown in Figure 9. The model yielded a high R² value of 0.8636, indicating that approximately 86.4 % of the variance in the actual CS values can be explained by the GEP model’s predictions. The fitted regression line, defined by the equation y = 0.8156x + 7.223, further confirms the strong linear relationship, although a slight underestimation trend is noticeable at higher strength ranges.

Figure 9:

Relationship of the forecasted and actual CS of the GGBS-based concrete using the GEP model.

To further assess the model’s performance, the absolute differences among the predicted and actual CS values are illustrated in Figure 10. This residual error distribution provides deeper insight into the precision and generalisation ability of the GEP model. The analysis shows that the prediction error ranges from a minimum of 0.010 MPa to a maximum of 20.56 MPa. Notably, a substantial portion of the predictions fall within acceptable error bounds: 60.37 % of the data points exhibit an absolute error between 0 and 6 MPa, indicating a high degree of precision in the majority of the cases. An additional 27.67 % of predictions lie within an error range of six–12 MPa, reflecting moderate deviation. Only 11.32 % of the test set displayed errors exceeding 12 MPa, which could be attributed to a combination of data outliers, complex material interactions, or limitations in the model’s sensitivity to specific input features.

Figure 10:

Evaluation of the GEP model performance through actual versus predicted CS of GGBS concrete.

These findings confirm that the GEP model can be considered a robust and reliable approach for modelling the CS of sustainable concrete mixes incorporating GGBS as a partial cement replacement. The results support its suitability for preliminary strength prediction in practical engineering applications, particularly when interpretability and transparency of the underlying equation are valued.

The AdaBoost regression model also demonstrated strong predictive capability in estimating the CS of concrete incorporating GGBS. As illustrated in Figure 11, the relationship between the experimental and predicted CS values shows a solid linear trend with a high R² = 0.8853. This represent that the model can explain approximately 88.5 % of the variation in the actual strength values. The regression equation, y = 0.7629x + 9.9382, reveals a slight underestimation in the slope, implying that while the trend is accurately captured, the model tends to compress higher values toward the mean slightly.

Figure 11:

Relationship of the forecasted and actual CS of the GGBS-based concrete using the AdaBoost model.

A deeper examination of prediction reliability is presented in Figure 12, which visualizes the error distribution across the test dataset. The residual analysis reveals a maximum error of 16.32 MPa and a minimum of 0.064 MPa, with the majority of predictions demonstrating high accuracy: Approximately 52.0 % of the data points fall within an absolute error range of 0–6 MPa, indicating high prediction precision for over half of the dataset. An additional 40.25 % of predictions fall within the six–12 MPa error range, suggesting moderate but acceptable deviations in complex cases. Only 6.91 % of the predictions exceeded a 12 MPa error, reflecting a relatively low frequency of significant outliers.

Figure 12:

Evaluation of the AdaBoost model performance through actual versus predicted CS of GGBS concrete.

Compared to the GEP model, AdaBoost demonstrates slightly improved accuracy in terms of R² and a tighter error distribution, making it a promising candidate for practical applications where generalisation is critical. Its performance further underscores the utility of ensemble learning approaches in capturing nonlinear interactions in concrete mix constituents and their effect on compressive strength.

The MLP was the top-performing model in forecasting the CS of GGBS concrete mixes. As presented in Figure 13, a strong linear relationship is observed between the actual and predicted values, characterised by a high coefficient of determination (R² = 0.8949). The corresponding regression equation, y = 0.8973x + 4.1088, suggests that the MLP model effectively captures the underlying trends in the data with minimal bias, as evidenced by the slope approaching unity and a relatively low intercept.

Figure 13:

Relationship of the forecasted and actual CS of the GGBS-based concrete using the MLP model.

The prediction residuals, detailed in Figure 14, offer a detailed view of the model’s error distribution across the test set. The analysis reveals a maximum error of 21.74 MPa and a minimum of just 0.014 MPa, indicating that while outliers exist, the majority of the predictions are highly accurate. The distribution of the absolute errors is as follows: 62.26 % of the predictions have an absolute error between 0 and 6 MPa, demonstrating excellent predictive precision for nearly two-thirds of the dataset. 26.55 % of the predictions fall within the six–12 MPa range, still representing acceptable performance given the variability inherent in concrete materials. Only 6.28 % of the predictions exhibit an error greater than 12 MPa, suggesting that significant deviations are relatively rare.

Figure 14:

Evaluation of the MLP model performance through actual versus predicted CS of GGBS concrete.

This level of accuracy indicates that the MLP model is particularly well-suited for capturing the nonlinear interactions between the mix constituents and their influence on the resulting CS. Compared to GEP and AdaBoost, MLP shows the best R² and the lowest percentage of high-error predictions, reinforcing its robustness and adaptability for predictive modelling in sustainable concrete solutions.

To further validate and interpret the nonlinear trends captured by the machine learning models, both a 3D surface plot and a 2D contour plot were generated to explore the interactive effect of GGBS content and curing age on the CS of concrete. These plots provide visual support to the results obtained by the MLP, AdaBoost, and GEP models, particularly in highlighting the dominance of age and GGBS in strength development, which is further explained in Section 7 (Model Interpretability and Feature Analysis).

The 3D surface plot (Figure 15) reveals a distinct nonlinear increase in CS as both GGBS content and age increase. Notably, strength improvement becomes more significant beyond 150 kg/m³ of GGBS and 90 days of curing, indicating a synergistic effect between GGBS hydration and prolonged curing. This trend aligns with the MLP model’s superior predictive performance (R² = 0.8949), which effectively captured such high-order interactions. The surface topography, with curved ridges and valleys, visually represents the complex relationships that the GEP model approximated symbolically and the AdaBoost model generalised through boosting iterations. Complementarily, the 2D contour plot (Figure 16) projects these interactions onto a planar format, where contour bands delineate regions of equal strength. This format simplifies the identification of strength thresholds and can aid practical mix design. The plot confirms that higher strengths are concentrated in the upper-right region of the graph, where both GGBS dosage and age are at elevated levels. These visual findings corroborate the importance rankings from permutation-based sensitivity analysis, which identified age and water-related ratios as the most influential features. Together, these plots not only validate the predictive insights from the ML models but also provide engineers with an interpretable, data-driven method for fine-tuning concrete mix compositions to achieve optimal performance and sustainability. They bridge the gap between black-box predictions and practical understanding, supporting transparent decision-making in GGBS-based concrete design.

Figure 15:

3D surface plot illustrating the nonlinear relationship between GGBS content, curing age, and compressive strength.

Figure 16:

2D contour plot showing compressive strength zones as a function of GGBS content and age.

The final expression derived from the GEP model represents a significant analytical outcome of this study. The explicit functional form, as illustrated in Equation (4), establishes a robust empirical relationship between the concrete mix constituents and the resulting CS. The symbolic regression capability of GEP has enabled the extraction of a transparent, human-interpretable formula that encapsulates the complex nonlinear interactions among the input variables. This derived equation is not merely a regression surrogate, but a predictive model grounded in data-driven optimisation. Each term within the equation reflects meaningful interactions, such as the ratio of activator to binder and composite logarithmic transformations involving the GGBS-to-binder and activator concentration parameters. These terms highlight the multivariate dependencies and synergistic effects that govern the strength development in GGBS-based concrete. (4) f X = A + B + C + D $$f\left(X\right)=A+B+C+D$$ A = X 4 X 2 $$A=\frac{\sqrt{{X}_{4}}}{{X}_{2}}$$ B = 3 . log 0.195 X 7 2 X 0 + log X 1 − X 2 2 + log X 5 X 0 − X 2 X 1 − X 2 $$B=3. \mathit{log}\left(0.195\,\frac{{X}_{7}^{2}}{{X}_{0}}\right)+\mathit{log}\left[{\left({X}_{1}-{X}_{2}\right)}^{2}\right]+\mathit{log}\left[\left(\frac{{X}_{5}}{{X}_{0}}-{X}_{2}\right)\left({X}_{1}-{X}_{2}\right)\right]$$ C = X 5 X 6 + log X 7 + log X 1 − X 5 X 0 + log X 7 X 6 $$C=\sqrt{\frac{{X}_{5}}{{X}_{6}}}+\mathrm{log}\left({X}_{7}\right)+\mathrm{log}\left({X}_{1}-\frac{{X}_{5}}{{X}_{0}}\right)+\mathrm{log}\left(\frac{{X}_{7}}{{X}_{6}}\right)$$ D = log X 7 − 0.329 X 1 − X 5 X 0 2 + log x 7 − 0.329 X 5 X 0 $$D=\mathit{log}\left[\left({X}_{7}\,\left(-0.329\right)\,{\left({X}_{1}-\frac{{X}_{5}}{{X}_{0}}\right)}^{2}\right)\right]+\mathit{log}\left[{x}_{7}\,\left(-0.329\right)\,\frac{{X}_{5}}{{X}_{0}}\right]$$ Where,

X0 = Cement.

X1 = GGBS.

X2 = Water.

X3 = Aggregate.

X4 = Sand.

X5 = W/B Ratio.

X6 = Superplasticizers.

X7 = Age.

From a practical standpoint, this equation serves as a compact and deployable tool for engineers and material scientists, enabling rapid estimation of CS with no need for costly or time-consuming laboratory experiments. The predictive precision of the GEP model, reflected in an R² equal to 0.863, underscores the reliability and generalisability of the model across unseen data points. Importantly, the equation not only enhances model interpretability, a standard limitation in black-box machine learning models, but also contributes to the ongoing shift toward explainable AI in civil engineering material design. By encapsulating the empirical knowledge learned from the dataset into a closed-form expression, this model aids in both forward prediction and reverse engineering of mix proportions to achieve target strength requirements. In the context of sustainable construction, such predictive modelling aligns with the objectives of optimising material usage, reducing experimental iterations, and accelerating the deployment of low-carbon cementitious systems such as GGBS concrete. Hence, the derived GEP equation represents a pivotal outcome of this research, demonstrating both technical rigour and practical utility.

Local interpretable model-agnostic explanations (LIME) were employed to examine how individual input features influenced the prediction of CS for a representative test instance. As shown in Figure 19, the model predicted a relatively lower compressive strength for this mix, primarily due to two dominant factors: the absence of superplasticizer (≤0.00 kg/m³) and a water-to-binder (W/Binder) ratio between 0.41 and 0.51. Both features had strong negative contributions, consistent with the understanding that reduced workability and increased porosity adversely affect early-age strength.

Figure 19:

Local feature contribution to the predicted CS of a GGBS-based concrete mix using LIME.

Conversely, the model assigned positive weight to features such as specimen age (7–28 days) and cement content greater than 326 kg/m³. These findings align with established concrete behaviour, where strength development is governed by the progression of hydration and sufficient binder availability. Notably, the influence of sand and water-to-cement (W/C) ratio was comparatively minor in this specific case. The LIME plot demonstrates the model’s ability to learn physically meaningful relationships, providing confidence in its application to material design scenarios.

To complement the local interpretation, global feature importance was evaluated using permutation-based sensitivity analysis. The results, illustrated in Figure 20, indicate that the water-to-cement (W/C) ratio and age were the most influential features across the entire dataset. This is consistent with the fundamental principles of concrete technology: the W/C ratio is a key driver of strength, durability, and porosity, while age governs the extent of hydration and pozzolanic activity, particularly relevant for GGBS-based systems. Interestingly, cement content and W/Binder ratio also contributed meaningfully to model performance, while features like GGBS content, aggregate volume, and superplasticizer dosage had relatively lower global influence. The lower importance of GGBS may be attributed to its indirect and time-dependent effect, which is already captured through the age variable, or potentially due to limited variability within the dataset.

Figure 20:

Permutation sensitivity analysis with the MLP model revealed the global influence of each input feature.

Together, these insights demonstrate that while local predictions may highlight case-specific sensitivities, the global analysis identifies parameters with consistent influence across the model’s learning space. The combination of LIME and sensitivity analysis offers a well-rounded view of the model’s decision-making logic and affirms its compatibility with engineering expectations.