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

Shaik Mohiddin,

D. Ravi Prasad,

D. Rama Seshu

et al.

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

Published: June 4, 2025

Language: Английский

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

Fatemeh Mobasheri,

Masoud Hosseinpoor, Ammar Yahia

et al.

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

Published: April 10, 2025

Language: Английский

Citations

1
Hybrid machine learning models for predicting compressive strength of self-compacting concrete: an integration of ANFIS and Metaheuristic algorithm DOI

Somdutta,

Baboo Rai

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

Citations

1
Investigating the Impact of Biaxial Geogrid Reinforcement on Subgrade Soil Strength Enhancement: A Machine Learning Analysis Using the MARS Model DOI

M. Harshitha,

Rakesh Kumar,

J. C. Vidyashree

et al.

Indian geotechnical journal, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Language: Английский

Citations

0
Evaluating the feasibility of using iron powder as a partial replacement for fine aggregates in concrete: An AI-based modeling approach DOI

M. Harshitha,

U.S. Agrawal, S. Sathvik

et al.

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

Published: April 9, 2025

Language: Английский

Citations

0
Assessing the impact of pozzolanic materials on the mechanical characteristics of UHPC: analysis, and modeling study DOI Creative Commons

Diar Fatah Abdulrahman Askari,

Sardam Salam Shkur Shkur,

Abdulrhman Dhaif Allah Abdo Mohammed

et al.

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

Published: May 20, 2025

Language: Английский

Citations

0
A multifaceted comparative analysis of incremental dynamic and static pushover methods in bridge structural assessment, integrated with artificial neural network and genetic algorithm approach DOI Creative Commons

Ashwini Satyanarayana,

V. Sindura,

L. Geetha

et al.

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

Published: May 21, 2025

Language: Английский

Citations

0
A review of machine learning models for concrete strength prediction and mix optimization DOI

Shaik Mohiddin,

D. Ravi Prasad,

D. Rama Seshu

et al.

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

Published: June 4, 2025

Language: Английский

Citations

0