The Application of Machine Learning Techniques for Forecasting Corrosion in Concrete Structures DOI Open Access
R. Dorothy,

R.M. Joany,

S. Santhana Prabha

et al.

Oriental Journal of Physical Sciences, Journal Year: 2024, Volume and Issue: 9(2), P. 84 - 95

Published: Dec. 10, 2024

Machine learning is a distinct field within artificial intelligence (AI) that utilizes algorithms trained on data sets to create models capable of self-learning. These can independently predict results and categorize information without requiring human intervention. At present, machine employed in numerous commercial industries, including recommending products customers based their past purchases, predicting fluctuations the stock market, aiding translation text across various languages. It stands as most prevalent form technology use worldwide. You may have observed common applications your daily life, such as: Recommendation systems suggest products, music, or television shows, utilized by platforms like Amazon, Spotify, Netflix. Voice recognition technologies facilitate conversion voice notes into written text. Fraud detection used financial institutions automatically recognize alert potentially fraudulent transactions. Autonomous vehicles driver assistance systems, features blind-spot automatic braking, significantly improve road safety. This article examines techniques forecast corrosion patterns steel reinforcement bars are embedded concrete structures.

Language: Английский

Research on the Parameter Prediction Model for Fully Mechanized Mining Equipment Selection Based on RF-WOA-XGBoost DOI Creative Commons
Yue Wu,

Wenjie Sang,

Xiangang Cao

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 732 - 732

Published: Jan. 13, 2025

Fully mechanized mining equipment is core to the coal process. The selection process for this type of complex and heavily relies on experts’ experience determining parameters. This paper proposes a fully parameter prediction model based Extreme Gradient Boosting Regression Trees (XGBoost), which developed mapping relationships among geological parameters, face conditions, parameters equipment. Feature performed feature importance ranking obtained through Random Forest (RF) method, thereby reducing complexity. Different optimization algorithms are used optimize hyperparameters XGBoost, results show that Whale Optimization Algorithm (WOA) outperforms other in terms convergence speed effectiveness. By comparing different algorithms, it found WOA-XGBoost achieves higher accuracy test set, with an average absolute error 0.0458, root mean square 0.1610, coefficient determination (R2) 0.9451. Finally, RF-WOA-XGBoost-based established, suitable lightly inclined faces. reduces input complexity, improves speed, minimizes reliance experts, ensures accuracy, providing effective reference

Language: Английский

Citations

0
An intelligent non-destructive method to identify the quality of self-compacting concrete based on convolutional neural networks via image recognition DOI Creative Commons
Zhong Xiao, Zixuan Liu, Xuying Guo

et al.

Case Studies in Construction Materials, Journal Year: 2025, Volume and Issue: unknown, P. e04442 - e04442

Published: Feb. 1, 2025

Language: Английский

Citations

0
Performance degradation of fatigue-damaged concrete under the combined effect of freeze-thaw cycles and chloride-sulfate attack DOI
Yao Lv,

Ruixi Yang,

Ditao Niu

et al.

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 470, P. 140585 - 140585

Published: Feb. 26, 2025

Language: Английский

Citations

0
An Intelligent Framework for Deriving Formulas of Aerodynamic Forces between High-Rise Buildings under Interference Effects using Symbolic Regression Algorithms DOI
Kun Wang,

Tianhao Shen,

Jingyu Wei

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 111614 - 111614

Published: Dec. 1, 2024

Language: Английский

Citations

2
The Application of Machine Learning Techniques for Forecasting Corrosion in Concrete Structures DOI Open Access
R. Dorothy,

R.M. Joany,

S. Santhana Prabha

et al.

Oriental Journal of Physical Sciences, Journal Year: 2024, Volume and Issue: 9(2), P. 84 - 95

Published: Dec. 10, 2024

Machine learning is a distinct field within artificial intelligence (AI) that utilizes algorithms trained on data sets to create models capable of self-learning. These can independently predict results and categorize information without requiring human intervention. At present, machine employed in numerous commercial industries, including recommending products customers based their past purchases, predicting fluctuations the stock market, aiding translation text across various languages. It stands as most prevalent form technology use worldwide. You may have observed common applications your daily life, such as: Recommendation systems suggest products, music, or television shows, utilized by platforms like Amazon, Spotify, Netflix. Voice recognition technologies facilitate conversion voice notes into written text. Fraud detection used financial institutions automatically recognize alert potentially fraudulent transactions. Autonomous vehicles driver assistance systems, features blind-spot automatic braking, significantly improve road safety. This article examines techniques forecast corrosion patterns steel reinforcement bars are embedded concrete structures.

Language: Английский

Citations

0