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

и другие.

Hybrid Advances, Год журнала: 2025, Номер unknown, С. 100385 - 100385

Опубликована: Янв. 1, 2025

Язык: 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

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 150, С. 110544 - 110544

Опубликована: Март 20, 2025

Язык: Английский

Процитировано

1
Machine Learning as an Innovative Engineering Tool for Controlling Concrete Performance: A Comprehensive Review DOI

Fatemeh Mobasheri,

Masoud Hosseinpoor, Ammar Yahia

и другие.

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Апрель 10, 2025

Язык: Английский

Процитировано

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

и другие.

Materials Today Communications, Год журнала: 2024, Номер unknown, С. 110813 - 110813

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

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

Buildings, Год журнала: 2024, Номер 14(11), С. 3660 - 3660

Опубликована: Ноя. 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.

Язык: Английский

Процитировано

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

и другие.

Case Studies in Construction Materials, Год журнала: 2025, Номер unknown, С. e04384 - e04384

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

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

и другие.

Materials Today Communications, Год журнала: 2025, Номер unknown, С. 112466 - 112466

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

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

и другие.

Materials, Год журнала: 2024, Номер 17(18), С. 4533 - 4533

Опубликована: Сен. 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.

Язык: Английский

Процитировано

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

и другие.

Results in Engineering, Год журнала: 2024, Номер 24, С. 103110 - 103110

Опубликована: Окт. 11, 2024

Язык: Английский

Процитировано

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, Год журнала: 2024, Номер unknown, С. 110759 - 110759

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

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

и другие.

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103421 - 103421

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

4