Evidence map›Paper›PMID 42009827›Full record

ArticleScientific reports2026

Mathematical modeling and artificial neural network of textile fabric reinforced foamed concrete for enhanced composite material performance.

Md Azree Othuman Mydin, P Jagadesh, Roshartini Omar, Khairunisa Muthusamy, Blessen Skariah Thomas, Mohd Mustafa Al Bakri Abdullah, Paul O Awoyera, Olaolu George Fadugba, Nadhim Hamah Sor

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Md Azree Othuman MydinSchool of Housing, Building and Planning, Universiti Sains Malaysia, 11800, Penang, Malaysia. azree@usm.my.
P JagadeshDepartment of Civil Engineering, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, 638 056, India.
Roshartini OmarDepartment of Construction Management, Faculty of Technology Management and Business, Universiti Tun Hussein Onn Malaysia (UTHM), Batu Pahat, 86400, Parit Raja, Johor, Malaysia.
Khairunisa MuthusamyFaculty of Civil Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Persiaran Tun Khalil Yaakob, 26300, Kuantan, Pahang, Malaysia.
Blessen Skariah ThomasDepartment of Civil Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia.
Mohd Mustafa Al Bakri AbdullahGeopolymer & Green Technology, Centre of Excellence, Universiti Malaysia Perlis, Perlis, Malaysia.
Paul O AwoyeraDepartment of Civil Engineering, Prince Mohammad Bin Fahd University, AL Khobar, 31952, Saudi Arabia. pawoyera@pmu.edu.sa.
Olaolu George FadugbaDepartment of Civil Engineering, Kabale University, Kabale Municipality, P.O Box 317, Kabale, Uganda. ogfadugba@kab.ac.ug.
Nadhim Hamah SorCivil Engineering Department, University of Garmian, Kurdistan Region, Kalar, 46021, Iraq.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The construction industry is increasingly seeking sustainable and cost-effective solutions to improve the properties of foamed concrete (FC). While textile fabric reinforced mortar (TRM) systems have been widely studied for external strengthening applications, this research focuses on the use of textile fabric as internal reinforcement in FC, aiming to enhance its mechanical and durability properties. This study investigates the mechanical and durability properties of textile fabric reinforced foamed concrete (TFRFC), focusing on its potential as an internal reinforcement material for enhancing the performance of FC in structural applications. Two densities of FC, 600 kg/m³ and 1200 kg/m³, were tested with three textile fabric configurations (1-layer, 2-layers, and 3-layers). The experimental program evaluated the compressive, flexural, and splitting tensile strengths, alongside durability properties such as water absorption, porosity, and drying shrinkage. The results revealed significant improvements in material properties with the inclusion of textile fabric; compressive strength increased by up to 59% for 600 kg/m³ and 28% for 1200 kg/m³ at 7 days with three layers of fabric. Additionally, water absorption decreased by 15%, and porosity was reduced by up to 11%. The drying shrinkage was notably reduced by 80–95% with three fabric layers, demonstrating the fabric’s effectiveness in mitigating shrinkage. An artificial neural network (ANN) model, developed from a dataset of 576 samples, predicted the compressive strength of TFRFC with an R² value of 0.99. This study emphasizes the viability of textile fabric as an internal reinforcement, providing a cost-effective and sustainable solution for improving the mechanical and durability properties of FC in structural applications.

Indexed as

Artificial neural networkFoamed concreteK-fold cross validationStatistical modellingTextile fabric reinforcement

Identifiers

PMID42009827
PMCPMC13260410

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.