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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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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Application of a Predictive Model to Reduce Unplanned Downtime in Automotive Industry Production Processes: A Sustainability Perspective

Sustainability, Journal Year: 2025, Volume and Issue: 17(9), P. 3926 - 3926

Published: April 27, 2025

The automotive industry constantly seeks intelligent technologies to increase competitiveness, reduce costs, and minimize waste, in line with the advancements of Industry 4.0. This study aims implement analyze a predictive model based on machine learning within industry, validating its capability impact unplanned downtime. implementation process involved identifying central problem root causes using quality tools, prioritizing equipment through Analytic Hierarchy Process (AHP), selecting critical failure modes Risk Priority Number (RPN) derived from Failure Mode Effects Analysis (PFMEA). Predictive algorithms were implemented select best-performing error metrics. Data collected, transformed, cleaned for preparation training. Among five models trained, Random Forest demonstrated highest accuracy. was subsequently validated real data, achieving an average accuracy 80% predicting cycles. results indicate that can effectively contribute reducing financial caused by downtime, enabling anticipation preventive actions model’s predictions. highlights importance multidisciplinary approaches Production Engineering, emphasizing integration techniques as promising approach efficient maintenance production management reinforcing feasibility effectiveness contributing sustainability.

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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Concrete water cement ratio prediction system using random forest regression

Published: May 28, 2025

Language: Английский

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A review of machine learning models for concrete strength prediction and mix optimization

Journal of Building Pathology and Rehabilitation, Journal Year: 2025, Volume and Issue: 10(2)

Published: June 4, 2025

Language: Английский

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