Machine learning prediction of steel–concrete composite beam temperatures during hot asphalt paving DOI
Yuping Zhang, Yonghao Chu,

Jiayao Zou

et al.

Measurement, Journal Year: 2024, Volume and Issue: 242, P. 116257 - 116257

Published: Nov. 16, 2024

Language: Английский

Management of waste heat in residential buildings: Predictive modelling and sensitivity analysis of variables characterising shower heat exchanger conditions DOI
Sabina Kordana, Beata Piotrowska, Mariusz Starzec

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134743 - 134743

Published: Jan. 1, 2025

Language: Английский

Citations

1
Polymer Concretes Based on Various Resins: Modern Research and Modeling of Mechanical Properties DOI Open Access
Aleksandr Palamarchuk, Pavel Yudaev, Evgeniy M. Chistyakov

et al.

Journal of Composites Science, Journal Year: 2024, Volume and Issue: 8(12), P. 503 - 503

Published: Dec. 2, 2024

This review is devoted to experimental studies and modeling in the field of mechanical physical properties polymer concretes polymer-modified concretes. The analyzes carried out over past two years. paper examines based on various resins presents advantages disadvantages models developed predict materials. Based data literature, most promising polymers for use road surface repair are with poly(meth)acrylic resins. It was found that adequate productive deep machine learning model—using several hidden layers perform calculations input parameters—and extreme gradient boosting model. In particular, model showed high R2 values forecasting (in range 0.916–0.981) when predicting damping coefficient ultimate compressive strength. turn, among additives Portland cement concrete, natural polymers, such as mammalian gelatin cold fish gelatin, superabsorbent polymers. These allow an improvement strength 200% or more. may be interest engineers specializing building construction, materials scientists involved development implementation new into production, well researchers interdisciplinary fields chemistry technology.

Language: Английский

Citations

3
Unpacking predictive relationships in graphene oxide-reinforced cementitious nanocomposites: An explainable ensemble learning approach for augmented data DOI

Hossein Adel,

Majid Ilchi Ghazaan, Asghar Habibnejad Korayem

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 144, P. 110123 - 110123

Published: Jan. 25, 2025

Language: Английский

Citations

0
Functional Deep Eutectic Solvents to Boost Antioxidant Synergism in Edible Fats DOI

Emmanuel D. Dike,

Lucas B. Ayres, Tomás E. Benavidez

et al.

ACS Sustainable Chemistry & Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 10, 2025

Language: Английский

Citations

0
Compressive strength prediction of fiber-reinforced recycled aggregate concrete based on optimization algorithms DOI Creative Commons

Shao Wei Duan

Frontiers in Built Environment, Journal Year: 2024, Volume and Issue: 10

Published: Dec. 11, 2024

With the growing emphasis on sustainable development in construction industry, fiber-reinforced recycled aggregate concrete (BFRC) has attracted considerable attention due to its superior mechanical properties and environmental benefits. However, accurately predicting compressive strength of BFRC remains a challenge because complex interaction between aggregates fiber reinforcement. This study introduces an innovative predictive framework that combines XGBoost machine learning algorithm with advanced optimization algorithms, including Seagull Optimization Algorithm (SOA), Tunicate Swarm (TSA), Mayfly (MA). The unique integration these algorithms not only improves accuracy but also optimizes model performance by enhancing parameter tuning capabilities. Experimental results demonstrated TSA-XGBoost achieved exceptional R 2 0.9847 minimum mean square error (MSE) 0.255958, outperforming other models BFRC’s strength. novel approach offers efficient accurate tool for assessing practical applications, thus supporting broader adoption construction.

Language: Английский

Citations

1
Predicting tensile strength of steel fiber-reinforced concrete based on a novel differential evolution-optimized extreme gradient boosting machine DOI
Nhat‐Duc Hoang

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 23, 2024

Language: Английский

Citations

1
Machine Learning Prediction of Steel-Concrete Composite Beam Temperatures During Hot Asphalt Paving DOI

Yuping Zhang,

Chu Yonghao,

Zou jiayao

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0
Machine learning prediction of steel–concrete composite beam temperatures during hot asphalt paving DOI
Yuping Zhang, Yonghao Chu,

Jiayao Zou

et al.

Measurement, Journal Year: 2024, Volume and Issue: 242, P. 116257 - 116257

Published: Nov. 16, 2024

Language: Английский

Citations

0