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

Machine learning-based destructive and non-destructive testing of paver block using fly ash and polyvinyl chloride into sustainable pedestrians DOI

Bhukya Govardhan Naik,

G. Nakkeeran,

Dipankar Roy

et al.

Innovative Infrastructure Solutions, Journal Year: 2025, Volume and Issue: 10(4)

Published: March 13, 2025

Language: Английский

Citations

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

Applications of computational intelligence for predictive modeling of properties of blended cement sustainable concrete incorporating various industrial byproducts towards sustainable construction DOI

N P Mungle,

Dnyaneshwar M. Mate,

Sham H. Mankar

et al.

Asian Journal of Civil Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 6, 2024

Language: Английский

Citations

Predictive modeling for concrete properties under variable curing conditions using advanced machine learning approaches DOI

Nischal P. Mungle, Dnyaneshwar M. Mate,

Sham H. Mankar

et al.

Asian Journal of Civil Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 17, 2024

Language: Английский

Citations

Innovative use of corncob ash in concrete: a machine learning perspective on compressive strength prediction DOI

Navaratnarajah Sathiparan

Innovative Infrastructure Solutions, Journal Year: 2025, Volume and Issue: 10(3)

Published: Feb. 4, 2025

Language: Английский

Citations

Optimized and improved DNN predictive model of fatigue life parameters in hybrid fiber reinforced self-compacting concrete DOI

Shailja Bawa, H. S. Chore, S. P. Singh

et al.

Architectural Engineering and Design Management, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 20

Published: Feb. 5, 2025

Language: Английский

Citations

Axial Compressive and Buckling Behavior of Concrete-Filled Steel Tubes Incorporating Recycled Coarse Aggregate, Plastic Waste, and Silica Fume DOI

Abubakr E. S. Musa, Almotaseembillah Ahmed, Subhan Ahmad

et al.

Arabian Journal for Science and Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

Language: Английский

Citations

Two-sided matching optimization model for green housing technology selection based on hesitant 2-tuple linguistic rough numbers DOI

Musavarah Sarwar, Ghous Ali,

Wajeeha Gulzar

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 151, P. 110559 - 110559

Published: April 5, 2025

Language: Английский

Citations

Prediction reliability improvement on long-term creep life for P91 steel using a hybrid method of artificial neural network and CDM model DOI

Kai Zhang, Xinbao Liu, Lin Zhu

et al.

Engineering Fracture Mechanics, Journal Year: 2025, Volume and Issue: unknown, P. 111172 - 111172

Published: April 1, 2025

Language: Английский

Citations

Comparative study of structural behavior of CFRP tubes and traditional steel for pile and column applications compliance with international codes DOI

Mahmoud El Gendy, Ibrahim El Arabi,

A. El-Barbary

et al.

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

Published: May 1, 2025

Abstract Carbon Fiber-Reinforced Polymers ( CFRPs ) are highly valued in structural engineering due to their exceptional strength-to-weight ratio, durability, and corrosion resistance. While the design of reinforced concrete is governed by specific building codes, have unique considerations. This study delves into a comparative analysis eight prominent codes: ACI 318 (American Concrete Institute), EC 2 (Eurocode 2), ECP (Egyptian Code Practice), BS 8110 (British Standard), IS 456 (Indian CP 65 (Singapore CSA A 23.3 (Canadian HK (Hong Kong Practice). The focus on circular CFRP sections under bending axial loads, which more complex than rectangular sections. By comparing requirements for general properties, bending, column provisions, this paper aims identify significant differences procedures. software tool was developed support analysis, its results align closely with previous studies, available well-known programs, interaction diagrams from various codes. Key factors influencing capacity elements, such as tube thickness strength, were investigated. validated analyzing columns laterally loaded piles using aforementioned generated program based 2, , 8110, showed high degree similarity those aids charts, Mean Absolute Percentage Error MAPE values less 5%, indicating software's accuracy determining ultimate load-carrying

Language: Английский

Citations