Machine Learning Driven Fluidity and Rheological Properties Prediction of Fresh Cement-Based Materials DOI Open Access
Yi Liu, Zeyad M. A. Mohammed, Jialu Ma

и другие.

Materials, Год журнала: 2024, Номер 17(22), С. 5400 - 5400

Опубликована: Ноя. 5, 2024

Controlling workability during the design stage of cement-based material mix ratios is a highly time-consuming and labor-intensive task. Applying artificial intelligence (AI) methods to predict optimize materials can significantly enhance efficiency design. In this study, experimental testing was conducted create dataset 233 samples, including fluidity, dynamic yield stress, plastic viscosity materials. The proportions cement, fly ash (FA), silica fume (SF), water, superplasticizer (SP), hydroxypropyl methylcellulose (HPMC), sand were selected as inputs. Machine learning (ML) employed establish predictive models for these three early indicators. To improve prediction capability, optimized hybrid models, such Particle Swarm Optimization (PSO)-based CatBoost XGBoost, adopted. Furthermore, influence individual input variables on each indicator examined using Shapley Additive Explanations (SHAP) Partial Dependence Plot (PDP) analyses. This study provides novel reference achieving rapid accurate control workability.

Язык: Английский

Data-intelligence driven methods for durability, damage diagnosis and performance prediction of concrete structures DOI Creative Commons
Fan Li, Daming Luo, Ditao Niu

и другие.

Communications Engineering, Год журнала: 2025, Номер 4(1)

Опубликована: Июнь 3, 2025

A large number of in-service reinforced concrete structures are now entering the mid-to-late stages their service life. Efficient detection damage characteristics and accurate prediction material performance degradation have become essential for ensuring safety these structures. Traditional methods, which primarily rely on manual inspections sensor monitoring, inefficient lack accuracy. Similarly, models materials, often based limited experimental data polynomial fitting, oversimplify influencing factors. In contrast, partial differential equation that account mechanisms computationally intensive difficult to solve. Recent advancements in deep learning machine learning, as part artificial intelligence, introduced innovative approaches both This paper provides a comprehensive overview theories models, reviews current research application durability structures, focusing two main areas: intelligent predictive modeling durability. Finally, article discusses future trends offers insights into innovation structure

Язык: Английский

Процитировано

0
Intelligent prediction framework for axial compressive capacity of FRP-RACFST columns DOI
Qicheng Xu, Junpeng Li,

Yingcai Fang

и другие.

Materials Today Communications, Год журнала: 2024, Номер unknown, С. 110999 - 110999

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

1
Machine Learning Driven Fluidity and Rheological Properties Prediction of Fresh Cement-Based Materials DOI Open Access
Yi Liu, Zeyad M. A. Mohammed, Jialu Ma

и другие.

Materials, Год журнала: 2024, Номер 17(22), С. 5400 - 5400

Опубликована: Ноя. 5, 2024

Controlling workability during the design stage of cement-based material mix ratios is a highly time-consuming and labor-intensive task. Applying artificial intelligence (AI) methods to predict optimize materials can significantly enhance efficiency design. In this study, experimental testing was conducted create dataset 233 samples, including fluidity, dynamic yield stress, plastic viscosity materials. The proportions cement, fly ash (FA), silica fume (SF), water, superplasticizer (SP), hydroxypropyl methylcellulose (HPMC), sand were selected as inputs. Machine learning (ML) employed establish predictive models for these three early indicators. To improve prediction capability, optimized hybrid models, such Particle Swarm Optimization (PSO)-based CatBoost XGBoost, adopted. Furthermore, influence individual input variables on each indicator examined using Shapley Additive Explanations (SHAP) Partial Dependence Plot (PDP) analyses. This study provides novel reference achieving rapid accurate control workability.

Язык: Английский

Процитировано

0