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Machine learning models for predicting compressive strength of fiber-reinforced concrete containing waste rubber and recycled aggregate

https://doi.org/10.1016/j.jclepro.2023.138673

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2 of the 135 references listed on this page are in the Retraction Watch database as retracted (export 2026-10-28): 10.1016/j.conbuildmat.2019.01.130 (notice 2022-03-03), 10.1016/j.engstruct.2015.03.070 (notice 2018-06-14).

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What this work says

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inferredcites →https://doi.org/10.1061/(asce)0899-1561(1990)2:4(202)openalex-snapshot

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inferredcites →https://doi.org/10.1061/(asce)0899-1561(1999)11:3(206)openalex-snapshot

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inferredcites →https://doi.org/10.1063/5.0072546openalex-snapshot

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inferredcites →https://doi.org/10.1080/14680629.2009.9690188openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/s41062-025-02175-8openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/s41062-025-02250-0openalex-snapshot

Review on carbonation concrete for GGBS & SF by Taguchi method

inferred← citeshttps://doi.org/10.1007/s41939-023-00355-6openalex-snapshot

A comparative analysis of tree-based machine learning algorithms for predicting the mechanical properties of fibre-reinforced GGBS geopolymer concrete

inferred← citeshttps://doi.org/10.1007/s41939-025-01130-5openalex-snapshot

Utilization of silicon carbide waste in concrete: experimental assessment and AI-driven strength prediction

inferred← citeshttps://doi.org/10.1007/s41939-025-01154-xopenalex-snapshot

Physics-guided deep ensemble and inverse design for sustainable concrete

inferred← citeshttps://doi.org/10.1007/s42107-024-01219-1openalex-snapshot

Quantifying compressive strength in limestone powder incorporated concrete with incorporating various machine learning algorithms with SHAP analysis

inferred← citeshttps://doi.org/10.1007/s42461-025-01250-zopenalex-snapshot

A Comparative Exploration of Machine Learning Techniques for Compressive Strength Prediction in Copper Mine Tailing Concretes

inferred← citeshttps://doi.org/10.1007/s43452-024-01020-6openalex-snapshot

Artificial intelligence-based predictive model for utilization of industrial coal ash in the production of sustainable ceramic tiles

inferred← citeshttps://doi.org/10.1007/s44163-026-01230-2openalex-snapshot

Data-driven multi-objective optimization of recycled aggregate concrete mixes via LightGBM strength modelling and SHAP interpretability

inferred← citeshttps://doi.org/10.1007/s44242-024-00047-5openalex-snapshot

Evolution of recycled concrete research: a data-driven scientometric review

inferred← citeshttps://doi.org/10.1007/s44290-024-00017-7openalex-snapshot

Recent efforts on investigating the effects of recycled rubber content on the mechanical properties of structural concrete

inferred← citeshttps://doi.org/10.1007/s44290-025-00353-2openalex-snapshot

Data-driven prediction and enhancement of Marshall mix properties using machine learning

inferred← citeshttps://doi.org/10.1007/s44290-026-00414-0openalex-snapshot

Machine learning methods for prediction of compressive strength of steel fibre reinforced concrete at elevated temperatures: a comparative analysis

inferred← citeshttps://doi.org/10.1016/b978-0-443-27342-1.00007-5openalex-snapshot

Maintenance of waste-derived construction materials

inferred← citeshttps://doi.org/10.1016/j.apples.2026.100298openalex-snapshot

From compressive strength studies to predictive machine learning models: Rubberised concrete containing brick powder

inferred← citeshttps://doi.org/10.1016/j.array.2026.101186openalex-snapshot

Machine learning algorithms for predicting compressive strength of concrete containing recycled wastes at elevated temperatures

inferred← citeshttps://doi.org/10.1016/j.buildenv.2025.113716openalex-snapshot

Ten questions concerning wind-focused shape and structural optimization of tall buildings

inferred← citeshttps://doi.org/10.1016/j.ceramint.2024.02.377openalex-snapshot

Utilizing machine learning to integrate silica-based production waste material in ceramic tiles manufacturing: Progressing toward sustainable solutions

inferred← citeshttps://doi.org/10.1016/j.clema.2025.100323openalex-snapshot

Data driven tensile strength prediction for fiber-reinforced rubberized recycled aggregate concrete using machine learning

inferred← citeshttps://doi.org/10.1016/j.clwas.2025.100236openalex-snapshot

Ensemble machine learning algorithms for efficient prediction of compressive strength of concrete containing tyre rubber and brick powder

inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2023.133233openalex-snapshot

Fiber-reinforced recycled aggregate concrete with crumb rubber: A state-of-the-art review

inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2024.134905openalex-snapshot

Eco-friendly 3D printed concrete with fine aggregate replacements: Fabrication, characterization and machine learning prediction

inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2024.135369openalex-snapshot

Data-driven machine learning approaches for predicting slump of fiber-reinforced concrete containing waste rubber and recycled aggregate

inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2024.136013openalex-snapshot

Multi-performance optimization of low-carbon geopolymer considering mechanical, cost, and CO2 emission based on experiment and interpretable learning

inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2024.137370openalex-snapshot

A systematic literature review of AI-based prediction methods for self-compacting, geopolymer, and other eco-friendly concrete types: Advancing sustainable concrete

inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2024.138021openalex-snapshot

Understanding and predicting micro-characteristics of ultra-high performance concrete (UHPC) with green porous lightweight aggregates: Insights from machine learning techniques

inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2025.140671openalex-snapshot

Study on the basic mechanical properties, dynamic properties, and frost resistance of rubber recycled aggregate concrete

inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2025.144185openalex-snapshot

Effect of waste glass on fresh and strength properties of recycled brick aggregate concrete

inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2026.147273openalex-snapshot

Early prediction of ready-mixed concrete compressive strength using machine learning-based algorithms: a large-scale industrial dataset study

inferred← citeshttps://doi.org/10.1016/j.cscm.2024.e02901openalex-snapshot

Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models

inferred← citeshttps://doi.org/10.1016/j.cscm.2024.e02916openalex-snapshot

Characterization and economization of cementitious tile bond adhesives using machine learning technique

inferred← citeshttps://doi.org/10.1016/j.cscm.2025.e05154openalex-snapshot

Evaluating mechanical and environmental impacts of sustainable natural fiber reinforced recycled aggregate concrete incorporating supervised machine learning methods

inferred← citeshttps://doi.org/10.1016/j.cscm.2025.e05617openalex-snapshot

Waste-to-resource design of eco-friendly ultra-high-performance concrete using reprocessed glass powder: Life cycle sustainability assessment and durability prediction

inferred← citeshttps://doi.org/10.1016/j.eng.2024.11.040openalex-snapshot

Strategies to Improve the Life Cycle Net CO2 Benefit of Recycled Aggregate Concrete

inferred← citeshttps://doi.org/10.1016/j.engappai.2025.111718openalex-snapshot

Order reduced Gaussian process fused physical information for assessing long-term multiple properties using very little high-variance experimental data -a real-world concrete dam application

inferred← citeshttps://doi.org/10.1016/j.engappai.2025.112581openalex-snapshot

Probabilistic machine learning with a cascaded framework for robust failure mode classification and capacity estimation of rectangular concrete-filled steel tube columns under axial compression

inferred← citeshttps://doi.org/10.1016/j.engfracmech.2024.110254openalex-snapshot

Experimental investigation of effects of multi-fissures on brittleness of ordinary Portland cement concrete and rubberized concrete

inferred← citeshttps://doi.org/10.1016/j.engfracmech.2026.112139openalex-snapshot

A machine learning-based prediction model for mesoscopic damage feature parameters of concrete in freeze-thaw environment

inferred← citeshttps://doi.org/10.1016/j.engstruct.2025.121270openalex-snapshot

Investigating shear behavior of fiber-reinforced rubberized recycled aggregate concrete beams using a hybrid ML-FEM approach

inferred← citeshttps://doi.org/10.1016/j.eswa.2025.127326openalex-snapshot

Reliable forecasting non-linear triaxial mechanical response of recycled aggregate concrete by knowledge-enhanced, modified, explainable and replicable machine learning algorithms

inferred← citeshttps://doi.org/10.1016/j.hazadv.2024.100489openalex-snapshot

Leveraging visible-near-infrared spectroscopy and machine learning to detect nickel contamination in soil: Addressing class imbalances for environmental management

inferred← citeshttps://doi.org/10.1016/j.heliyon.2025.e42133openalex-snapshot

Machine learning for predicting strength properties of waste iron slag concrete

inferred← citeshttps://doi.org/10.1016/j.isci.2026.117559openalex-snapshot

Assessing machine learning prediction of recycled aggregate concrete strength with a tabular foundation model

inferred← citeshttps://doi.org/10.1016/j.istruc.2025.108547openalex-snapshot

Predicting shear strength of hollow pretensioned spun precast concrete pile using machine learning models

inferred← citeshttps://doi.org/10.1016/j.istruc.2025.109531openalex-snapshot

Proposing an optimised data-efficient artificial neural network model for estimating the compressive strength of concrete containing SCMs

inferred← citeshttps://doi.org/10.1016/j.istruc.2025.109759openalex-snapshot

Hybrid resampling-enhanced machine learning for failure mode prediction of axially compressed rectangular CFST columns with imbalanced data

inferred← citeshttps://doi.org/10.1016/j.istruc.2025.110152openalex-snapshot

Interpretable machine learning approach for predicting the workability and mechanical properties of betel nut husk fiber-reinforced concrete

inferred← citeshttps://doi.org/10.1016/j.istruc.2025.110393openalex-snapshot

Compressive performance of steel fiber reinforced rubber self-compacting concrete after high-temperature exposure

inferred← citeshttps://doi.org/10.1016/j.istruc.2025.110400openalex-snapshot

Optimized strength predictions for 3D printed fiber-reinforced concrete: Machine learning-driven insights

inferred← citeshttps://doi.org/10.1016/j.jclepro.2024.142498openalex-snapshot

Performance evaluation of hybrid fiber reinforced concrete on engineering properties and life cycle assessment: A sustainable approach

inferred← citeshttps://doi.org/10.1016/j.jclepro.2024.142746openalex-snapshot

Enhancing mix proportion design of low carbon concrete for shield segment using a combination of Bayesian optimization-NGBoost and NSGA-III algorithm

inferred← citeshttps://doi.org/10.1016/j.jclepro.2024.143713openalex-snapshot

Exploring economic and environmental impacts of recycled aggregate concrete using particle swarm optimization algorithm

inferred← citeshttps://doi.org/10.1016/j.jei.2026.100023openalex-snapshot

Investigation into the mechanical behavior of alkali-slag rubber aggregate concrete

inferred← citeshttps://doi.org/10.1016/j.jer.2026.08.029openalex-snapshot

Interpretable machine learning framework for pull-out capacity prediction of post-installed concrete anchors

inferred← citeshttps://doi.org/10.1016/j.jobe.2024.108455openalex-snapshot

Investigation of optimized machine learning models with PSO for forecasting the shear capacity of steel fiber-reinforced SCC beams with/out stirrups

inferred← citeshttps://doi.org/10.1016/j.jobe.2024.108871openalex-snapshot

Effects of material properties uncertainty on seismic fragility of reinforced-concrete frames using machine learning approach

inferred← citeshttps://doi.org/10.1016/j.jobe.2026.117211openalex-snapshot

Enhancing the mechanical properties and freeze-thaw resistance of soil solidification rock-rubber recycled aggregate concrete: Synergistic effects of Nano-SiO2 and steel fibers

inferred← citeshttps://doi.org/10.1016/j.mtcomm.2023.107356openalex-snapshot

Strength evaluation of eco-friendly waste-derived self-compacting concrete via interpretable genetic-based machine learning models

inferred← citeshttps://doi.org/10.1016/j.mtcomm.2024.111286openalex-snapshot

AI-guided design framework for bond behavior of steel-concrete in steel reinforced concrete composites: From dataset cleaning to feature engineering

inferred← citeshttps://doi.org/10.1016/j.mtcomm.2025.111706openalex-snapshot

Data-driven compressive strength prediction of basalt fiber reinforced rubberized concrete using neural network-based models

inferred← citeshttps://doi.org/10.1016/j.mtcomm.2025.112028openalex-snapshot

Explainable ensemble algorithms with grey wolf optimization for estimation of the tensile performance of polyethylene fiber-reinforced engineered cementitious composite

inferred← citeshttps://doi.org/10.1016/j.mtcomm.2025.113776openalex-snapshot

Construction and demolition waste utilization and low-carbon scheme design for low-carbon concrete based on data cleaning and enhancement model

inferred← citeshttps://doi.org/10.1016/j.mtcomm.2025.114219openalex-snapshot

Prediction of the compressive strength of recycled aggregate concrete using machine learning models with experimental validation

inferred← citeshttps://doi.org/10.1016/j.pes.2025.100077openalex-snapshot

Predicting the compressive strength of self-compacting concrete containing recycled aggregate and supplementary cementitious materials using machine learning techniques

inferred← citeshttps://doi.org/10.1016/j.rineng.2025.104307openalex-snapshot

An interpretable XGBoost-SHAP machine learning model for reliable prediction of mechanical properties in waste foundry sand-based eco-friendly concrete

inferred← citeshttps://doi.org/10.1016/j.rineng.2025.105584openalex-snapshot

AI-powered machine learning models for monitoring and optimization of biodrying process

inferred← citeshttps://doi.org/10.1016/j.scp.2024.101763openalex-snapshot

Machine learning models for estimating the compressive strength of rubberized concrete subjected to elevated temperature: Optimization and hyper-tuning

inferred← citeshttps://doi.org/10.1016/j.scp.2024.101894openalex-snapshot

Novel exploration of chemical compounds in mono-variate, Bi-variate, and multi-variate scenarios of waste glass powder as a cement replacement in concrete

inferred← citeshttps://doi.org/10.1016/j.scp.2025.101905openalex-snapshot

Microstructural and mechanical evolution of recycled fiber-reinforced tunnel slag concrete under wet-dry cycles

inferred← citeshttps://doi.org/10.1016/j.tafmec.2024.104600openalex-snapshot

An effect of compound defects with x-shaped fissures and holes on brittleness and failure characteristics of deep rock mass

inferred← citeshttps://doi.org/10.1016/j.trgeo.2024.101228openalex-snapshot

Prediction and optimization of adverse responses for a highway tunnel after blasting excavation using a novel hybrid multi-objective intelligent model

inferred← citeshttps://doi.org/10.1016/j.trgeo.2025.101516openalex-snapshot

Assessment on eco-solidified alkali residue reinforced soft soils for intelligent subgrade constructions

inferred← citeshttps://doi.org/10.1016/j.wasman.2024.07.032openalex-snapshot

Machine learning modeling of thermally assisted biodrying process for municipal sludge

inferred← citeshttps://doi.org/10.1038/s41597-025-05608-2openalex-snapshot

An inventory of industrial solid waste in 337 cities of China: Applying machine learning for data completion

inferred← citeshttps://doi.org/10.1038/s41598-024-57896-0openalex-snapshot

Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches

inferred← citeshttps://doi.org/10.1038/s41598-024-64486-7openalex-snapshot

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

inferred← citeshttps://doi.org/10.1038/s41598-025-28892-9openalex-snapshot

Forecasting the rheological properties of alkali-activated concrete utilizing gene expression programming

inferred← citeshttps://doi.org/10.1038/s41598-025-32350-xopenalex-snapshot

Experimental assessment with data-driven machine learning-based prediction of compressive strength of waste natural fiber-reinforced sustainable concrete

inferred← citeshttps://doi.org/10.1038/s41598-025-32654-yopenalex-snapshot

Mechanical and durability performance prediction of geopolymer incorporating ferrosilicon slag and aluminum powder using machine learning techniques

inferred← citeshttps://doi.org/10.1038/s41598-025-94387-2openalex-snapshot

Machine learning predictions of high-strength RCA concrete utilizing chemically activated fly ash and nano-silica

inferred← citeshttps://doi.org/10.1038/s41598-026-40897-6openalex-snapshot

Modeling mechanical properties of rubberized concrete using gene expression programming (GEP) and random forest: a comparative study

inferred← citeshttps://doi.org/10.1038/s44296-025-00058-8openalex-snapshot

Artificial intelligence in the design, optimization, and performance prediction of concrete materials: a comprehensive review

inferred← citeshttps://doi.org/10.1038/s44296-026-00109-8openalex-snapshot

Machine learning-enabled pathways for low-carbon concrete

inferred← citeshttps://doi.org/10.1061/jmcee7.mteng-22082openalex-snapshot

Prediction of Compressive Strength of Iron Tailings Concrete Based on Interactive Cross-Channel Multilayer Perceptron Neural Network

inferred← citeshttps://doi.org/10.1080/10298436.2026.2613097openalex-snapshot

A comparative assessment of ANN, GP and SVM in predicting indirect tensile strength of modified bituminous mixes

inferred← citeshttps://doi.org/10.1080/15440478.2025.2502662openalex-snapshot

Machine Learning Model Coupled with Graphical User Interface for Predicting Mechanical Properties of Flax Fiber

inferred← citeshttps://doi.org/10.1080/15623599.2025.2594533openalex-snapshot

Integrating coastal segmentation on predictive modeling for data-driven bridge asset management

inferred← citeshttps://doi.org/10.1080/21650373.2025.2462183openalex-snapshot

Applications of machine learning methods for design and characterization of high-performance fiber-reinforced cementitious composite (HPFRCC): a review

inferred← citeshttps://doi.org/10.1080/21650373.2026.2646341openalex-snapshot

Explainable hybrid machine learning approach for mechanical performance of 3D-printed strain-hardening cementitious composites (3DP-SHCC)

inferred← citeshttps://doi.org/10.1080/23311916.2024.2385621openalex-snapshot

Multiple AI predictive models for compressive strength of recycled aggregate concrete

inferred← citeshttps://doi.org/10.1080/24705314.2026.2630088openalex-snapshot

Experimental assessment and hybrid machine learning prediction and optimization of compressive strength of steel fiber reinforced demolition waste concrete

inferred← citeshttps://doi.org/10.1108/wje-01-2025-0038openalex-snapshot

Data-driven prediction of concrete strength by machine learning: hybrid-fiber-reinforced recycled aggregate concrete

inferred← citeshttps://doi.org/10.13167/2024.29.11openalex-snapshot

Predictive modelling of sustainable concrete compressive strength using advanced machine learning algorithms

inferred← citeshttps://doi.org/10.1515/rams-2024-0055openalex-snapshot

Prediction of flexural strength of concrete with eggshell and glass powders: Advanced cutting-edge approach for sustainable materials

inferred← citeshttps://doi.org/10.1515/rams-2025-0230openalex-snapshot

Reducing clinker dependency through harmonized mix design of multi-supplemented concrete

inferred← citeshttps://doi.org/10.1590/1517-7076-rmat-2025-0819openalex-snapshot

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

inferred← citeshttps://doi.org/10.1590/1517-7076-rmat-2026-0819openalex-snapshot

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

inferred← citeshttps://doi.org/10.1680/jstbu.25.00010openalex-snapshot

Residual mechanical properties of steel-fibre-reinforced concrete with volcanic scoria sand after freeze–thaw cycles using machine learning

inferred← citeshttps://doi.org/10.21203/rs.3.rs-10211251/v1openalex-snapshot

Metaheuristic-Optimized Machine Learning Models for Predicting Shear Strength of Circular Concrete-Filled Steel Tubes with SHAP-Based and PDP Interpretability

inferred← citeshttps://doi.org/10.21203/rs.3.rs-6586869/v1openalex-snapshot

Evaluating mechanical and environmental impacts of sustainable natural fiber reinforced recycled aggregate concrete incorporating supervised machine learning methods

inferred← citeshttps://doi.org/10.2139/ssrn.4866069openalex-snapshot

Investigation of the Effect of Carbon Fiber Substitution for Scms Produced with Granular Rubber and its Variation with the Manufactured Element Height

inferred← citeshttps://doi.org/10.2139/ssrn.4866077openalex-snapshot

Innovation in Construction Materials: Development of Ecological Concrete Blocks with Expanded Polystyrene Integrating Design of Experiments and Machine Learning

inferred← citeshttps://doi.org/10.2139/ssrn.5162764openalex-snapshot

How Can We Assess Long-Term Multiple Properties Using Only Very Little High-Variance Experimental Data Fused Physical Information?-A Real-Life Concrete Dam Application

inferred← citeshttps://doi.org/10.2139/ssrn.5389983openalex-snapshot

Interpretable Surrogate Models for Seismic Response Estimation of Urban Highway Bridges Supported by Post-Tensioned Rocking Piers

inferred← citeshttps://doi.org/10.32604/cmes.2025.070880openalex-snapshot

Bridging the Gap in Recycled Aggregate Concrete (RAC) Prediction: State-of-the-Art Data-Driven Framework, Model Benchmarking, and Future AI Integration

inferred← citeshttps://doi.org/10.3390/buildings14092885openalex-snapshot

A Two-Level Machine Learning Prediction Approach for RAC Compressive Strength

inferred← citeshttps://doi.org/10.3390/buildings15152640openalex-snapshot

Machine Learning-Assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength–Cost–CO2 Emissions Trade-Offs

inferred← citeshttps://doi.org/10.3390/buildings15183333openalex-snapshot

Application Research Progress of Solid Waste Concrete Based on Machine Learning Technology

inferred← citeshttps://doi.org/10.3390/su16156644openalex-snapshot

AI-Driven Prediction of Compressive Strength in Self-Compacting Concrete: Enhancing Sustainability through Ultrasonic Measurements

inferred← citeshttps://doi.org/10.3390/waste1040054openalex-snapshot

A Decade Review of Research Trends Using Waste Materials in the Building and Construction Industry: A Pathway towards a Circular Economy

inferred← citeshttps://doi.org/10.56748/ejse.24776openalex-snapshot

Assessment of fiber-reinforced rubberized recycled aggregate concrete’s properties by optimal regression frameworks

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