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Leveraging waste-based additives and machine learning for sustainable mortar development in construction Cover

Leveraging waste-based additives and machine learning for sustainable mortar development in construction

REVIEWS ON ADVANCED MATERIALS SCIENCE
Volume 64 (2025): Issue 1 (January 2025)
By: Yi Zhang,  Qizhi Zhang,  Muwaffaq Alqurashi,  Ali H. AlAteah and  Ahmed A. Abdou Elabbasy  
Open Access
|Sep 2025

Figures & Tables

Figure 1

Overview of the input features and target output.
Overview of the input features and target output.

Figure 2

Input and output data distributions via marginal histograms: (a) CM, (b) Sd, (c) Rb, (d) S-F, (e) Mp, and (f) Gp.
Input and output data distributions via marginal histograms: (a) CM, (b) Sd, (c) Rb, (d) S-F, (e) Mp, and (f) Gp.

Figure 3

Correlation matrix of variables.
Correlation matrix of variables.

Figure 4

Step-by-step workflow of the adopted methodology.
Step-by-step workflow of the adopted methodology.

Figure 5

Process flow of the GEP-based model development [44].
Process flow of the GEP-based model development [44].

Figure 6

Flowchart of the MEP method [44].
Flowchart of the MEP method [44].

Figure 7

GEP model tree for C-S prediction: (a) Sub-ET 1, (b) Sub-ET 2, (c) Sub-ET 3, (d) Sub-ET 4, (e) Sub-ET 5, and (f) Sub-ET 6.
GEP model tree for C-S prediction: (a) Sub-ET 1, (b) Sub-ET 2, (c) Sub-ET 3, (d) Sub-ET 4, (e) Sub-ET 5, and (f) Sub-ET 6.

Figure 8

C-S GEP model: (a) correlation of predicted and observed C-S, and (b) predicted vs observed values and error distribution.
C-S GEP model: (a) correlation of predicted and observed C-S, and (b) predicted vs observed values and error distribution.

Figure 9

C-S MEP model: (a) correlation of predicted and observed C-S and (b) predicted vs observed values and error distribution.
C-S MEP model: (a) correlation of predicted and observed C-S and (b) predicted vs observed values and error distribution.

Figure 10

Taylor plot of model validation.
Taylor plot of model validation.

Figure 11

PDPs for rubberized mortar: (a) CM, (b) Sd, (c) Rb, (d) S-F, (e) Mp, and (f) Gp.
PDPs for rubberized mortar: (a) CM, (b) Sd, (c) Rb, (d) S-F, (e) Mp, and (f) Gp.

Figure 12

ICE plots for rubberized mortar: (a) CM, (b) Sd, (c) Rb, (d) S-F, (e) Mp, and (f) Gp.
ICE plots for rubberized mortar: (a) CM, (b) Sd, (c) Rb, (d) S-F, (e) Mp, and (f) Gp.

Figure 13

Framework illustrating green concrete development using waste materials and ML.
Framework illustrating green concrete development using waste materials and ML.

Findings obtained by statistical analysis

MetricUnitC-S GEPC-S MEP
RMSEMPa2.0661.588
MAEMPa1.6661.203
R MPa0.9000.982
MAPE%4.1193.026
NSEMPa0.7960.961
PI—1.3551.786
OF—4.2692.522

MEP/GEP method parameters

GroupsDescriptionsSettings
GEP/MEP samplingTraining records286
Validation/test records122
GEP parametersError measureMSE
Number of sub-populations100
Sub-population size250
Code length50
Cross-over probability0.9
Number of generations250
Mutation probability0.1
Functions+, −, ÷, ×, √, ^, ex
MEP parametersNumber of chromosomes75
Head size10
Number of genes6
Linking functionAddition
Functions+, −, ÷, ×, √, ^, ex
DOI: https://doi.org/10.1515/rams-2025-0143 | Journal eISSN: 1605-8127
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Language: English
Submitted on: Jun 18, 2025
Accepted on: Aug 4, 2025
Published on: Sep 11, 2025
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Keywords:
rubberized mortar,
sustainable construction materials,
gene and multi-expression programming
Related subjects:
Chemistry,
Chemistry, other,
Engineering,
Introductions and overviews,
Engineering, other,
Materials sciences,
Porous materials,
Physics,
Physics, other

© 2025 Yi Zhang, Qizhi Zhang, Muwaffaq Alqurashi, Ali H. AlAteah, Ahmed A. Abdou Elabbasy, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 License.

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