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Author

A. Alzlfawi

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Open access Sep 2026

Sustainable concrete using paper ash and stone powder: experimental and microstructural study

This study evaluates the feasibility of paper ash (PA) as a partial cement replacement (0%, 5%, 10%, and 15% ) with a constant 15% stone powder (SP) as a sand replacement. Fresh and hardened properties were assessed through slump, compressive strength, and split-cylinder tensile tests. Furthermore, the microstructura...

A. Alzlfawi, S. Alogla, Jawad Ahmad et al. · 0 citations
Open access Aug 2026

Innovative development of an advanced machine learning modeling for predicting compressive strength of graphene-infused concrete: a SHAP and PDP approach

The quasi-brittle response of cementitious materials continues to limit their structural efficiency, necessitating advanced reinforcement strategies. While conventional fibers improve mechanical performance, the emergence of graphene-based nanomaterials offers new opportunities to tailor interfacial interactions and en...

A. Alzlfawi, M. H. R. Sobuz, Md. Kawsarul Islam Kabbo et al. · 0 citations
Review Nov 2026

Precision Assessment of Data-Driven Supervised Machine-Learning Models for Predicting Compressive Strength of Sustainable Waste Foundry Sand Concrete

The rapid rate of urbanization and industrialization has driven the excessive use of natural resources like river sand and gravel, raising significant sustainability concerns. Waste foundry sand (WFS), a discarded by-product of ferrous and nonferrous metal casting industries, offers a promising substitute for natur...

M. H. R. Sobuz, Md. Kawsarul Islam Kabbo, A. Alzlfawi et al. · 0 citations
Open access Jul 2026

Optimization and predictive measurement of compressive strength of iron ore slag modified concrete using data-driven supervised machine learning algorithms.

An integrated machine learning-experimental framework to predict the compressive strength (CS) of concrete incorporating ternary industrial wastes glass powder, marble powder, and iron ore slag is developed and the hybrid XGB-GBR model demonstrates the highest balanced performance.

Md. Samsuzzaman Sobuz, Md. Kawsarul Islam Kabbo, Abdullah Alzlfawi et al. · 0 citations
Open access 2026

Quantifying the compressive strength of basalt fiber reinforced concrete using advanced hybrid machine learning models

ABSTRACT Basalt Fiber Reinforced Concrete (BFRC) is being recognized as an eco-friendly advanced material with reduced environmental footprint, higher mechanical performance and long-term durability. However, its compressive strength prediction still appears to be a difficult problem due to the nonlinearity caused by t...

Ibrahim Y. Hakeem, Abdullah Alzlfawi, M. H. R. Sobuz et al. · 0 citations

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