International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 2 (March-April 2026) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 2
March-April 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Divergent Hydration Kinetics: Type-Aware Artificial Intelligence Framework for Compressive Strength Prediction and Sustainability Evaluation in Concrete Composites

Author(s) Talal Malik
Country Pakistan
Abstract Artificial Intelligence (AI) offers significant utility for compressive strength prediction for concrete. However, existing data-driven approaches assume homogeneous material behaviours without accounting for the compositional differences in conventional and sustainable concrete. This study examines the feasibility of developing ‘material-aware’ machine learning models that are capable of predicting compressive strength, without overlooking how intrinsic properties of sustainable or conventional concrete govern microstructural stability, and in turn, the compressive strength. Concrete datasets were preprocessed to identify parameters with statistically relevant correlation with compressive strength. Feature Engineering was employed to introduce a novel attribute ‘type’ for each concrete instance in the datasets, and the attributes corresponding to concrete sustainability were segregated to be modelled for sustainability evaluations. Further analysis of the datasets revealed a pronounced anomalous behaviour between age and compressive strength. While age demonstrated a positive correlation with compressive strength in conventional concrete, a contrasting negative correlation was observed between age and compressive strength in sustainable concrete. This disparity revealed that both concrete types had divergent kinetic pathways during the hydration process; these differing behaviours were further explained by the presence of Supplementary Cementitious Materials (SCMs) and their distinct potential for Calcium Silicate Hydrate (C-S-H) gel generation. Under these conditions, Linear Regression (LR) and Random Forest (RF) models were trained and evaluated using R^2,MAE and MSE. The results indicated that the majority of compressive strength correlations with concrete properties were non-linear, justifying Random Forest’s more robust and precise predictive capability. The RF model significantly outperformed the linear baseline model, achieving a near-perfect R^2 of 0.993, compared to 0.831 for LR, confirming the non-linear nature of the predictor-target interactions in concrete. The research further translated the predictive results into user-friendly Graphical User Interfaces (GUIs) that allow compressive strength estimation and sustainability assessment. Collectively, the findings of this research highlight the significance of incorporating material classification into AI-driven modelling of concrete properties and demonstrate how domain-informed machine learning can enhance both predictive accuracy and real-world usability. The study contributes a practical and scalable framework for integrating intelligent modelling into sustainable construction analysis and decision-making workflows, providing a meaningful resource for engineers and field experts.
Keywords Compressive Strength; Sustainable Construction; Hydration Kinetics; Linear Regression; Concrete; Ferrock; Random Forest; Rice Husk Ash; Ferrock; Water to Cement Ratio; Artificial Intelligence; Cement Chemist Notation; Sustainability; Pozzolanic Materials; Supplementary Cementitious Materials; Superplasticisers; Coarse Aggregate; Fine Aggregate; Calcium Silicate Hydrate; Hyperparameters; Target Variables; Predictor Variables; Life Cycle Assessment
Field Physics > Civil Engineering
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-07
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2Cover Page Vol 8 Isu 1Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals