Harnessing Synergy of Machine Learning and Nature-Inspired Optimization for Enhanced Compressive Strength Prediction in Concrete

Hybrid Advances, Год журнала: 2025, Номер unknown, С. 100404 - 100404

Опубликована: Янв. 1, 2025

Язык: Английский

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An AutoGluon-enabled robust machine learning model for concrete tensile and compressive strength forecast

International Journal of Construction Management, Год журнала: 2025, Номер unknown, С. 1 - 12

Опубликована: Фев. 4, 2025

Язык: Английский

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Optimized Decision-Making for Tunnel Boring Machine Control Parameters

Rock Mechanics and Rock Engineering, Год журнала: 2025, Номер unknown

Опубликована: Фев. 17, 2025

Язык: Английский

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

Nondestructive Testing And Evaluation, Год журнала: 2025, Номер unknown, С. 1 - 33

Опубликована: Март 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.

Язык: Английский

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Prediction of compression coefficient of Nanjing floodplain soft soil based on explainable artificial intelligence

Advanced Engineering Informatics, Год журнала: 2025, Номер 65, С. 103308 - 103308

Опубликована: Апрель 8, 2025

Язык: Английский

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