Hybrid machine learning models for predicting compressive strength of self-compacting concrete: an integration of ANFIS and Metaheuristic algorithm

Nondestructive Testing And Evaluation, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 33

Published: March 25, 2025

Self-compacting concrete (SCC) has become increasingly popular due to its superior workability, segregation resistance, and compressive strength. As the traditional methods for strength prediction are costly time-intensive, this study explores machine learning (ML) techniques as efficient alternatives SCC prediction. Three state-of-the-art hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) models, optimised using Firefly Algorithm (FA), Particle Swarm Optimization (PSO) Genetic (GA). For purpose, a robust dataset of 366 instances 7 input parameters is taken from literature. After data analysis pre-processing, hyperparameters models tuned best-fit model tested on unforeseen data. ANFIS-FF stands out best-performing (RTR2 = 0.945 RTS2 0.9395) in both training testing phases, closely followed by ANFIS-GA. All outperform ANFIS model, outlining significance hybridisation, however, ANFIS-PSO lags behind other two models. The highlights importance integrating with metaheuristic algorithms tackling complex engineering problems like design optimal mix design, minimising material waste ensuring cost-effectiveness. It serves benchmark future research comparing hybridisation starting point ANFIS.

Language: Английский

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Machine Learning as an Innovative Engineering Tool for Controlling Concrete Performance: A Comprehensive Review

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 10, 2025

Language: Английский

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Microstructural Analysis of Sand Reinforced by EICP Combined with Glutinous Rice Slurry Based on CT Scanning

Materials, Journal Year: 2025, Volume and Issue: 18(7), P. 1563 - 1563

Published: March 30, 2025

Sandy soils are prone to engineering issues due their high permeability and low cohesion in the natural environment. Therefore, eco-friendly reinforcement techniques required for projects such as subgrade filling soft soil foundation enhance performance. This study proposes a synergistic method that combines Enzyme-Induced Calcium Carbonate Precipitation with Glutinous rice slurry (G-EICP). The macroscopic mechanical properties pore structure evolution of reinforced sand were systematically investigated through triaxial tests, unconfined compressive strength (UCS) microstructural characterization based on Scanning Electron Microscope (SEM) Micro- Computed Tomography (CT) tests. results indicate when glutinous volume ratio (VG) reaches 10%, UCS G-EICP-reinforced peaks at 449.2 kPa. coefficient decreases significantly increasing relative density (Dr), VG, confining pressure (σ3), seepage (p). Microstructural analysis reveals may promote calcium carbonate crystal growth, potentially by providing nucleation sites, establishing dual mechanism skeleton enhancement pore-throat clogging. increased incorporation reduces number connected pores, lowers coordination number, elevates tortuosity, thereby inducing marked enhancements both treated compared plain soil.

Language: Английский

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Evaluating the feasibility of using iron powder as a partial replacement for fine aggregates in concrete: An AI-based modeling approach

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 474, P. 140890 - 140890

Published: April 9, 2025

Language: Английский

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Assessing the impact of pozzolanic materials on the mechanical characteristics of UHPC: analysis, and modeling study

Deleted Journal, Journal Year: 2025, Volume and Issue: 2(1)

Published: May 20, 2025

Language: Английский

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A multifaceted comparative analysis of incremental dynamic and static pushover methods in bridge structural assessment, integrated with artificial neural network and genetic algorithm approach

Discover Materials, Journal Year: 2025, Volume and Issue: 5(1)

Published: May 21, 2025

Language: Английский

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