Cited by An improved method for calculating roll deformation of six-high rolling mill: enhances computation speed and accuracy

Hybrid data-driven approaches to predicting the compressive strength of ultra-high-performance concrete using SHAP and PDP analyses DOI

Abul Kashem, Rezaul Karim,

Somir Chandra Malo

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 20, P. e02991 - e02991

Published: Feb. 19, 2024

Ultra-high-performance concrete (UHPC) is a cutting-edge and advanced constructions material known for its exceptional mechanical properties durability. Recently, machine learning (ML) methods play pivotal role in predicting the compressive strength (CS) of UHPC evaluating dominant input parameters suitable mix design. This research, three hybrid models were utilized: Random Forest (RF), AdaBoost (AB), Gradient Boosting (GB) algorithms with particle swarm optimization (PSO), namely AB-PSO, RF-PSO, GB-PSO, to predict perform SHAP (Shapley additive explanation) analysis. To build predictive ML models, dataset 810 experimental data points was collected from published literature. Additionally, interaction plots generated visualize impact each feature on specific prediction made by model. Our results indicate that better than traditional GB-PSO model showed high accuracy among models. The had higher precision compared other two It achieved R2 values 0.9913 during training stage 0.9804 testing CS. analysis revealed age, fiber, cement, silica fume, superplasticizer significant influence strength, while comparatively lower. PDP (Partial Dependence Plots) amount individually variables can be calculated simply designed These findings are valuable construction applications offer essential insights design engineers builders, aiding their understanding significance component UHPC.

Language: Английский

Citations

Predicting Penetration Depth in Ultra-High-Performance Concrete Targets under Ballistic Impact: An Interpretable Machine Learning Approach Augmented by Deep Generative Adversarial Network DOI

Majid Khan,

Muhammad Faisal Javed,

Nashwan Adnan Othman

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 103909 - 103909

Published: Jan. 1, 2025

Language: Английский

Citations

Sustainable mix design and carbon emission analysis of recycled aggregate concrete based on machine learning and big data methods DOI

Boqun Zhang, Lei Pan, X. C. Chang

et al.

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: 489, P. 144734 - 144734

Published: Jan. 1, 2025

Language: Английский

Citations

Predicting the compressive strength of engineered geopolymer composites using automated machine learning DOI

Mahmoud Anwar Gad,

Ehsan Nikbakht,

Mohammed Gamal Ragab

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 442, P. 137509 - 137509

Published: July 31, 2024

Language: Английский

Citations

Prediction and comparison of burning rate of n-heptane pool fire in open space based on BPNN and XGBoost DOI

Peng Xu,

Yubo Bi,

Jian Chen

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 189, P. 89 - 101

Published: June 18, 2024

Language: Английский

Citations

Explainable Ensemble Learning and Multilayer Perceptron Modeling for Compressive Strength Prediction of Ultra-High-Performance Concrete DOI

Yaren Aydın, Celal Çakıroğlu, Gebrai̇l Bekdaş

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(9), P. 544 - 544

Published: Sept. 9, 2024

The performance of ultra-high-performance concrete (UHPC) allows for the design and creation thinner elements with superior overall durability. compressive strength UHPC is a value that can be reached after certain period time through series tests cures. However, this estimated by machine-learning methods. In study, multilayer perceptron (MLP) Stacking Regressor, an ensemble models, used to predict high-performance concrete. Then, ML model’s explained feature importance analysis Shapley additive explanations (SHAPs), developed models are interpreted. effect using different random splits training test sets has been investigated. It was observed stacking regressor, which combined outputs Extreme Gradient Boosting (XGBoost), Category (CatBoost), Light Machine (LightGBM), Extra Trees regressors forest as final estimator, performed significantly better than MLP regressor. shown predicted regressor average R2 score 0.971 on set. On other hand, model 0.909. results SHAP showed age amounts silica fume, fiber, superplasticizer, cement, aggregate, water have greatest impact predictions.

Language: Английский

Citations

Predictive and experimental assessment of chloride ion permeation in concrete subjected to multi-factorial conditions using the XGBoost algorithm DOI

Xuanrui Yu, Tianyu Hu, Nima Khodadadi

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 98, P. 111041 - 111041

Published: Oct. 12, 2024

Language: Английский

Citations

Enhancing load capacity prediction of column using eReLU-activated BPNN model DOI

Rupesh Kumar Tipu, Vandna Batra,

Suman Suman

et al.

Structures, Journal Year: 2023, Volume and Issue: 58, P. 105600 - 105600

Published: Dec. 1, 2023

Language: Английский

Citations

Prediction of compressive strength of high-performance concrete using optimization machine learning approaches with SHAP analysis DOI

Md Mahamodul Islam,

Pobithra Das,

Md Mahbubur Rahman

et al.

Journal of Building Pathology and Rehabilitation, Journal Year: 2024, Volume and Issue: 9(2)

Published: May 24, 2024

Language: Английский

Citations

Intelligent predicting and monitoring of ultra-high-performance fiber reinforced concrete composites − A review DOI

Dingqiang Fan,

Ziao Chen,

Yuan Cao

et al.

Composites Part A Applied Science and Manufacturing, Journal Year: 2024, Volume and Issue: unknown, P. 108555 - 108555

Published: Oct. 1, 2024

Language: Английский

Citations