Author

Md. Kawsarul Islam Kabbo

h-index 14 View on Semantic Scholar

We have 5 of 38 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

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. · Scientific Reports · 0 citations

Open access Jul 2026

Mechanical assessment with data-driven hybrid machine learning-based optimization of compressive strength of sustainable biochar-concrete composite.

The rapid rise in global population and industrial activity has intensified environmental challenges, particularly carbon dioxide (CO₂) emissions from the cement and concrete industry. Biochar, a carbon-rich byproduct of biomass pyrolysis, has emerged as a promising solution for sustainable construction by enhancing ca...

M. Uddin, Md. Samsuzzaman Sobuz, Mohamed Ghalla et al. · Scientific Reports · 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. · Journal of Structural Design... · 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. · Scientific Reports · 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. · Matéria · 0 citations