Development of Robust Machine Learning Models for Predicting Flexural Strengths of Fiber-Reinforced Polymeric Composites DOI Creative Commons
Abdulhammed K. Hamzat, Umar Salman, Md Shafinur Murad

et al.

Hybrid Advances, Journal Year: 2025, Volume and Issue: unknown, P. 100385 - 100385

Published: Jan. 1, 2025

Language: Английский

Prediction of the flexural strength and elastic modulus of cementitious materials reinforced with carbon nanotubes: An approach with artificial intelligence DOI
Mahyar Ramezani, Doeun Choe, A. Rasheed

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 150, P. 110544 - 110544

Published: March 20, 2025

Language: Английский

Citations

1
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
Leveraging machine learning to minimize experimental trials and predict hot deformation behaviour in dual phase high entropy alloys DOI
Sandeep Jain, Reliance Jain, K. Raja Rao

et al.

Materials Today Communications, Journal Year: 2024, Volume and Issue: unknown, P. 110813 - 110813

Published: Oct. 1, 2024

Language: Английский

Citations

9
Sustainable High-Performance Concrete Using Zeolite Powder: Mechanical and Carbon Footprint Analyses DOI Creative Commons
Hasan Mostafaei, Hadi Bahmani

Buildings, Journal Year: 2024, Volume and Issue: 14(11), P. 3660 - 3660

Published: Nov. 18, 2024

This study investigates environmentally friendly high-performance concrete (HPC) by partially replacing cement and silica sand with zeolite powder. The replacement levels included 10%, 20%, 30% for up to 50% sand. optimal mix achieved 85 MPa compressive strength, 6 tensile 7.8 flexural strength a replacement, reducing the carbon footprint approximately 659.72 kg CO2/m3. These findings demonstrate potential of powder enhance sustainability in HPC without compromising essential mechanical properties, promoting eco-friendly practices construction.

Language: Английский

Citations

8
Unveiling the Combined Thermal and High Strain Rate Effects on Compressive Behavior of Steel Fiber-Reinforced Concrete: A Novel Predictive Approach DOI Creative Commons
Mohsin Ali, Li Chen, Bin Feng

et al.

Case Studies in Construction Materials, Journal Year: 2025, Volume and Issue: unknown, P. e04384 - e04384

Published: Feb. 1, 2025

Language: Английский

Citations

1
Data-driven study on the mechanical properties of strain-hardening cementitious composites using algorithm-enhanced interpretable machine learning models and interactive interface development DOI
Xiaoyu Huang, Hongrui Ma, Xuejun Ren

et al.

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112466 - 112466

Published: April 1, 2025

Language: Английский

Citations

1
Predicting High-Strength Concrete’s Compressive Strength: A Comparative Study of Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Response Surface Methodology DOI Open Access
Tianlong Li, Jianyu Yang,

Pengxiao Jiang

et al.

Materials, Journal Year: 2024, Volume and Issue: 17(18), P. 4533 - 4533

Published: Sept. 15, 2024

Machine learning and response surface methods for predicting the compressive strength of high-strength concrete have not been adequately compared. Therefore, this research aimed to predict (HSC) using different methods. To achieve purpose, neuro-fuzzy inference systems (ANFISs), artificial neural networks (ANNs), methodology (RSM) were used as ensemble Using an ANN ANFIS, output was modeled optimized a function five independent variables. The RSM designed with three input variables: cement, fine coarse aggregate. facilitate data entry into Design Expert, model divided six groups, p-values responses 1 6 0.027, 0.010, 0.003, 0.023, 0.002, 0.026. following metrics evaluate projection: R, R2, MSE ANFIS modeling; Adj. Pred. R2 modeling. Based on data, it can be concluded that (R = 0.999, 0.998, 0.417), 0.981 0.963), 0.962, 0.926, 0.655) good chance accurately (HSC). Furthermore, there is strong correlation between ANN, RSM, models experimental data. Nevertheless, network demonstrates exceptional accuracy. sensitivity analysis shows cement aggregate most significant effect (45.29% 35.87%, respectively), while superplasticizer has least (0.227%). RSME values in 0.313 0.453 during test process 0.733 0.563 training process. Thus, found both presented better results higher accuracy construction materials.

Language: Английский

Citations

6
A machine learning comparison of transportation mode changes from high-speed railway promotion in Thailand DOI Creative Commons

Chinnakrit Banyong,

Natthaporn Hantanong, Panuwat Wisutwattanasak

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 103110 - 103110

Published: Oct. 11, 2024

Language: Английский

Citations

4
An Experimental Investigation to Predict the Compressive Strength of Lightweight Ceramsite Aggregate UHPC Using Boosting and Bagging Techniques DOI

Md. Sohel Rana,

Fangyuan Li

Materials Today Communications, Journal Year: 2024, Volume and Issue: unknown, P. 110759 - 110759

Published: Oct. 1, 2024

Language: Английский

Citations

4
Machine Learning Prediction of Permeability Distribution in the X Field Malay Basin Using Elastic Properties DOI Creative Commons

Zaky Ahmad Riyadi,

John Oluwadamilola Olutoki, Maman Hermana

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103421 - 103421

Published: Nov. 1, 2024

Language: Английский

Citations

4