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Predictive Modelling of Pavement Quality Fibre-Reinforced Alkali-Activated Nano-Concrete Mixes through Artificial Intelligence Cover

Predictive Modelling of Pavement Quality Fibre-Reinforced Alkali-Activated Nano-Concrete Mixes through Artificial Intelligence

Open Access
|Mar 2025

Figures & Tables

Figure 1:

The yearly growth of National Highways in India [15].
The yearly growth of National Highways in India [15].

Figure 2:

The detailed flowchart for the experimental programme and analysis.
The detailed flowchart for the experimental programme and analysis.

Figure 3:

Compaction factor values of PQAC mixes: (a) PQAC+NA and PQAC+NS and (b) PQAC+PVA and PQAC+PPF.
Compaction factor values of PQAC mixes: (a) PQAC+NA and PQAC+NS and (b) PQAC+PVA and PQAC+PPF.

Figure 4:

Split tensile strength and percentage variation in split tensile strength: (a) PQAC+NS, (b) PQAC+NA, (c) PQAC+PVAF and (d) PQAC+PPF.
Split tensile strength and percentage variation in split tensile strength: (a) PQAC+NS, (b) PQAC+NA, (c) PQAC+PVAF and (d) PQAC+PPF.

Figure 5:

Pair plots for the input parameters and observed responses.
Pair plots for the input parameters and observed responses.

Figure 6:

Correlation heatmap of input parameters and observed responses.
Correlation heatmap of input parameters and observed responses.

Figure 7:

Relative frequency distribution of the prediction-to-test STS ratio.
Relative frequency distribution of the prediction-to-test STS ratio.

Figure 8:

The violin plot illustrating the relative error percentages of different models.
The violin plot illustrating the relative error percentages of different models.

Figure 9:

Comparative analysis between actual and predicted values: a) MLR, b)DT, c) RF, d) SVR, e) AdaBoost and f) GBR.
Comparative analysis between actual and predicted values: a) MLR, b)DT, c) RF, d) SVR, e) AdaBoost and f) GBR.

Figure 10:

Correlation between expected and experimental values of STS for PQAC.
Correlation between expected and experimental values of STS for PQAC.

Figure 11:

Effect of the number of estimators on RF’s performance in terms of (a) MAE, (b) MSE, (c) RMSE, (d) R2 and (e) cross-validation mean.
Effect of the number of estimators on RF’s performance in terms of (a) MAE, (b) MSE, (c) RMSE, (d) R2 and (e) cross-validation mean.

Figure 12:

Effect of the number of estimators on AdaBoost’s performance in terms of (a) MAE, (b) MSE, (c) RMSE, (d) R2 and (e) cross-validation mean.
Effect of the number of estimators on AdaBoost’s performance in terms of (a) MAE, (b) MSE, (c) RMSE, (d) R2 and (e) cross-validation mean.

Figure 13:

Effect of the number of estimators on GBR’s performance in terms of (a) MAE, (b) MSE, (c) RMSE, (d) R2 and (e) cross-validation mean.
Effect of the number of estimators on GBR’s performance in terms of (a) MAE, (b) MSE, (c) RMSE, (d) R2 and (e) cross-validation mean.

Mix Design of PQAC mixes with fibres_

Mix IDAPVA-0.4APVA-0.8APVA-1.2APVA-1.6APVA-2.0APF-0.4APF-0.8APF-1.2APF-1.6APF-2.0
% Addition of fibres (by volume of binder)0.4%0.8%1.2%1.6%2.0%0.4%0.8%1.2%1.6%2.0%
Materialsin kg/m3
GGBS493493493493493493493493493493
NaOH flakes11.211.211.211.211.211.211.211.211.211.2
Liquid sodium silicate75.1275.1275.1275.1275.1275.1275.1275.1275.1275.12
Water157.56157.56157.56157.56157.56157.56157.56157.56157.56157.56
Natural coarse aggregate1071.41071.41071.41071.41071.41071.41071.41071.41071.41071.4
River sand fine aggregate577.84577.84577.84577.84577.84577.84577.84577.84577.84577.84
PVA0.7481.492.242.993.74-----
PPF-----0.6121.2241.8362.4483.06

Mix Design of PQAC mixes with nano-additives_

Mix IDA-0AS-0.5AS-1.0AS-1.5AS-2.0AA-0.5AA-0.75AA-1.0AA-1.25
% Addition of nano-additives (by weight of binder)0%0.5%1.0%1.5%2.0%0%0.75%1.0%1.25%
Materialsin kg/m3
GGBS493493493493493493493493493
NaOH flakes11.211.211.211.211.211.211.211.211.2
Liquid sodium silicate75.1275.1275.1275.1275.1275.1275.1275.1275.12
Water157.56157.56157.56157.56157.56157.56157.56157.56157.56
Natural coarse aggregate1071.41071.41071.41071.41071.41071.41071.41071.41071.4
River sand fine aggregate577.84577.84577.84577.84577.84577.84577.84577.84577.84
Nano-silica02.474.937.399.86----
Nano-alumina-----2.473.694.936.16

Performance results of the predictive models_

ModelRMSEMAEMSER2 scoreCV mean
Linear Regression0.6292660.4885460.3959760.6089060.445094
Decision Tree0.4522970.3823200.2045730.7979500.713217
Random Forest0.4536570.3838480.2058050.7967330.707697
Support Vector0.6367150.4920570.4054060.5995930.454470
ADA Boost0.4548210.3841500.2068620.7956880.714149
Gradient Boosting0.4595220.3901040.2111600.7914430.71143811

Statistical summary of the dataset_

CountMeanStdMin25%50%75%Max
GGBS5704930493493493493493
NaOH57011.2011.211.211.211.211.2
LSS57075.12075.1275.1275.1275.1275.12
Water570157.560157.56157.56157.56157.56157.56
NCA5701071.401071.41071.41071.41071.41071.4
RSFA570577.840577.84577.84577.84577.84577.84
NS5701.32.8100009.86
NA5700.911.8700006.16
PVAF5700.591.130000.753.74
PPF5700.480.920000.613.06
STS5705.340.943.694.645.175.818.2

Relative frequency distribution of the prediction-to-test STS ratio_

ModelMeanMedianStandard deviationSkew
Linear Regression1.0033370.998480.1084070.154706
Decision Tree1.0026380.987040.08550.399816
Random Forest1.0027640.987370.0858430.40379
Support Vector1.0048010.998470.1094840.076785
ADA Boost0.9992690.988540.0845980.367988
Gradient Boosting1.0045760.990690.087310.416545
DOI: https://doi.org/10.2478/sgem-2025-0007 | Journal eISSN: 2083-831X | Journal ISSN: 0137-6365
Language: English
Page range: 389 - 416
Submitted on: Sep 19, 2024
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Accepted on: Jan 22, 2025
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Published on: Mar 24, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Akhila Sheshadri, Shriram Marathe, Anisha P Rodrigues, Martyna Nieświec, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution 4.0 License.