• Home
  • Search
  • Optimized Hybrid Learning Model for Strength Prediction of Textile Effluent Sludge‐Based Concrete
  • https://doi.org/10.1002/eng2.70516Copy DOI Icon

Optimized Hybrid Learning Model for Strength Prediction of Textile Effluent Sludge‐Based Concrete

Show More
  • Abstract
  • Literature Map
  • References
  • Similar Papers
Abstract

ABSTRACT In previous concrete applications, fly ash (FA), silica fume (SF), metakaolin (MK), ground granulated blast furnace slag (GGBS), waste glass powder (WGP), rice husk ash (RHA), ceramic waste powder (CWP), and marble powder (MP) have all been proven as potential industrial materials that can be utilized as supplementary cementitious material (SCM). Recently, the use of textile effluent sludge (TES) as SCM has drawn significant attention of several researchers for promoting sustainability. This study examines the prediction of compressive strength (CS) and tensile strength (TS) of concrete utilizing TES through several machine learning (ML) techniques, particularly random forest (RF), support vector machine (SVM), extreme gradient boost (XGBoost), and K‐nearest neighbors (KNN). Furthermore, deep learning techniques, involving convolutional neural networks and long short‐term memory networks, are utilized. Additionally, a hybrid machine learning (HML) model integrating RF and SVM algorithms as well as hybrid deep learning (HDL) models incorporating CNN and LSTM networks were developed for strength prediction. After hyperparameter tuning with the grid searching method, the standalone LSTM model has demonstrated superior performance in CS prediction (), achieving the highest accuracy compared to all ML, DL, and hybrid models whereas the standalone SVM model exhibited the highest accuracy among all models after tuning for TS prediction (). Notable, both hybrid frameworks exhibited competitive performance, demonstrating predictive accuracies comparable to the best standalone models, hybrid CNN‐LSTM model has shown the second‐highest accuracy () in terms of CS prediction the hybrid RF‐SVM model has been proven to possess greater predictive accuracy () than CNN‐LSTM in terms of TS prediction. In fact, this study has been designed to promote the high‐scale utilization of TES as SCM in concrete, which will be helpful for environmental pollution control.

Similar Papers
  • Research Article
  • Citations289

Combined effects of supplementary cementitious materials (silica fume, GGBS, fly ash and rice husk ash) and steel fiber on the hardened properties of recycled aggregate concrete

  • Sep 06, 2020
  • Construction and Building Materials
  • Liaqat Ali Qureshi +2
  • PDF
  • Research Article
  • Citations5

Strength and Microstructural Investigation of Quaternary Blended High‐Strength Concrete

  • Jan 01, 2021
  • Advances in Civil Engineering
  • Ayele Bereda +1
  • Research Article

Performance evaluation of blended cement mortar with supplementary cementitious materials: from pozzolans to industrial wastes

  • Jan 23, 2026
  • REVIEWS ON ADVANCED MATERIALS SCIENCE
  • Mutahir Abbas +5
  • Research Article
  • Citations10

Performance of self-compacting concrete with treated rice husk ash at different curing temperatures

  • Sep 10, 2024
  • Journal of Building Engineering
  • Ayman Almutlaqah +3
  • Research Article
  • Citations10

A systematic review of metakaolin-based alkali-activated and geopolymer concrete: A step toward green concrete

  • Feb 17, 2025
  • REVIEWS ON ADVANCED MATERIALS SCIENCE
  • Diyar N Qader +4
  • Research Article
  • Citations225

Role of binary cement including Supplementary Cementitious Material (SCM), in production of environmentally sustainable concrete: A critical review

  • Jul 08, 2017
  • International Journal of Sustainable Built Environment
  • S Samad +1
  • PDF
  • Research Article
  • Citations7

Alkali Activated Concrete using Industrial and Agro Waste-Mix proportioning and Experimental Investigation

  • Dec 19, 2022
  • ASPS Conference Proceedings
  • Ganta Mounika +5
  • Research Article
  • Citations52

Utilization of agricultural and industrial waste as replacement of cement in pavement quality concrete: a review.

  • Jan 22, 2022
  • Environmental Science and Pollution Research
  • Arunabh Pandey +1
  • PDF
  • Research Article
  • Citations11

Establishing the Carbonation Profile with Raman Spectroscopy: Effects of Fly Ash and Ground Granulated Blast Furnace Slag

  • Apr 05, 2021
  • Materials
  • Yanfei Yue +3
  • Research Article

Effect of Supplementary Cementitious Materials on the Mechanical and Physical Properties of Lightweight Concrete

  • Jan 01, 2024
  • E3S Web of Conferences
  • Evgeny Vladimirovich Kotov +6
  • Research Article
  • Citations38

Hardening, microstructure, and shrinkage development of UHPC: A review

  • Dec 31, 2019
  • Journal of Asian Concrete Federation
  • Jianhui Liu +2
  • Research Article

Analysis of a New Type of Concrete that Partially Replaces Cement with GGBS and Glass Powder

  • Dec 31, 2024
  • International Journal for Research in Applied Science and Engineering Technology
  • G Madhava Swamy +1
  • Research Article
  • Citations28

Environmental impacts and environmental justice implications of supplementary cementitious materials for use in concrete

  • Jul 27, 2021
  • Environmental Research: Infrastructure and Sustainability
  • Leah Brinkman +1
  • Supplementary Content
  • Citations9

Integrating Life-Cycle Assessment (LCA) and Artificial Neural Networks (ANNs) for Optimizing the Inclusion of Supplementary Cementitious Materials (SCMs) in Eco-Friendly Cementitious Composites: A Literature Review

  • Sep 14, 2025
  • Materials
  • A Arvizu-Montes +3
  • Research Article

A Systematic Approach for Selection of Fit-for-Purpose Low-Carbon Concrete for Various Bridge Elements to Reduce the Net Embodied Carbon of a Bridge Project

  • Oct 13, 2025
  • Infrastructures
  • Harish Kumar Srivastava +2
Cactus Communications logo

Copyright 2026 Cactus Communications. All rights reserved.