Prediction and optimization framework of shear strength of reinforced concrete flanged shear wall based on machine learning and non-dominated sorting genetic algorithm-II DOI
Hanwen Zhang, Jinlong Liu, Shiqi Wang

et al.

Advances in Structural Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 10, 2024

Reinforced concrete (RC) flanged shear wall has good lateral strength and stiffness, which been widely used in building structures. Due to the coupling effect of many factors such as section shape, span ratio, so performance evaluation is still very limited. This paper proposed a prediction framework for capacity RC walls. A database containing 14 input variables, 1 output variable 153 samples was constructed evaluate accuracy 11 existing design methods. The Pearson coefficient preliminarily analyze correlation between variables. grid search optimize hyperparameters 4 machine learning models, six statistical indicators ( R 2 , R, RMSE, SD, MAE, MAPE) were comprehensively compare results ML models determine best model. On this basis, SHapley Additive exPlanations (SHAP) enhance interpretability mechanism variables on quantified. graphical user interface (GUI) guide engineering design. multi-objective model (MOO) established trade-off cost, thereby determining optimal scheme. show that better than XGB performance, with RMSE are 0.99, 118.96, respectively. SHAP method can effectively t w l f ′ c key parameters affecting wall.

Language: Английский

A review on properties and multi-objective performance predictions of concrete based on machine learning models DOI

Bowen Ni,

Md Zillur Rahman, Shuaicheng Guo

et al.

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112017 - 112017

Published: Feb. 1, 2025

Language: Английский

Citations

1
Physics-based probabilistic analysis of corrosion initiation in alkali-activated slag concrete assisted by machine learning DOI Creative Commons
Bin Dong, Shaoyu Zhao, Yingyan Zhang

et al.

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 471, P. 140661 - 140661

Published: March 8, 2025

Language: Английский

Citations

1
Deep learning–based prediction of compressive strength of eco-friendly geopolymer concrete DOI Creative Commons
Harun Tanyıldızı

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(28), P. 41246 - 41266

Published: June 7, 2024

The greenhouse gases cause global warming on Earth. cement production industry is one of the largest sectors producing gases. geopolymer produced with synthesized by reaction an alkaline solution and waste materials such as slag fly ash. use eco-friendly concrete decreases energy consumption In this study, f

Language: Английский

Citations

5
Sustainable Utilization of Waste Carbon Black in Recycled Steel Fibre Substituted Ultra High-Performance Concrete DOI

R. Rajiv Gandhi,

B. Saritha

Iranian Journal of Science and Technology Transactions of Civil Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 24, 2025

Language: Английский

Citations

0
Sustainable Catalysts: Advances in Geopolymer-Catalyzed Reactions and Their Applications DOI
Fernando Gomes de Souza, Shekhar Bhansali, Viviane Silva Valladão

et al.

Journal of Molecular Structure, Journal Year: 2025, Volume and Issue: unknown, P. 142017 - 142017

Published: March 1, 2025

Language: Английский

Citations

0
Intelligent evaluation of interference effects between tall buildings based on wind tunnel experiments and explainable machine learning DOI
Kun Wang, Jinlong Liu, Yong Quan

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 96, P. 110449 - 110449

Published: Aug. 13, 2024

Language: Английский

Citations

3
AI-based non-linear models for mechanical and toughness properties of sustainable fiber-reinforced geopolymer concrete (FRGPC) DOI
Muhammad Naveed,

Asif Hameed,

Ali Murtaza Rasool

et al.

Mechanics of Advanced Materials and Structures, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 25

Published: Oct. 31, 2024

The incorporation of fibers into geopolymer concrete (GPC) produces FRGPC which mitigates brittle failure and restrains macro crack propagation; however, research on predicting the mechanical toughness properties remains limited. This study addresses this gap by developing prediction models for compressive strength (CS), flexural (FS), (FT), fracture (FR). A dataset 600 data points was compiled from published literature, encompassing various constituent proportions, fiber shapes, dosages, aspect ratios, curing conditions. Employing an artificial neural network (ANN) methodology, 10 independent variables (g1, g2, …., g10) were utilized to predict four dependent (CS, FS, FT, FR), resulting in development eight non-linear ANN both straight (SFs) hooked (HFs). Each model has shown a higher R2 value lower root mean square error (RMSE) training (70%), testing (15%), validation (15%) datasets. CS with HFs (CS-HF) SF (CS-SF) showed values 0.983 0.973, RMSE 2.088 2.435 MPa highlighting accuracy models. Similarly, comparative analysis FR exhibited 0.997 0.987, 0.045 Mpa √m 0.032 FR-HF FR-SF, that addition strongly impacts improving properties. To identify most influential variable(s), sensitivity revealed g10, g1, g8, g9 as parameters SFs. For HFs, g3 properties, g9, g10 toughness. also presented mathematical formulation developed better interpretability facilitate optimal economical mix design selection, potential AI-based advancing sustainable construction materials.

Language: Английский

Citations

2
Prediction and optimization framework of shear strength of reinforced concrete flanged shear wall based on machine learning and non-dominated sorting genetic algorithm-II DOI
Hanwen Zhang, Jinlong Liu, Shiqi Wang

et al.

Advances in Structural Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 10, 2024

Reinforced concrete (RC) flanged shear wall has good lateral strength and stiffness, which been widely used in building structures. Due to the coupling effect of many factors such as section shape, span ratio, so performance evaluation is still very limited. This paper proposed a prediction framework for capacity RC walls. A database containing 14 input variables, 1 output variable 153 samples was constructed evaluate accuracy 11 existing design methods. The Pearson coefficient preliminarily analyze correlation between variables. grid search optimize hyperparameters 4 machine learning models, six statistical indicators ( R 2 , R, RMSE, SD, MAE, MAPE) were comprehensively compare results ML models determine best model. On this basis, SHapley Additive exPlanations (SHAP) enhance interpretability mechanism variables on quantified. graphical user interface (GUI) guide engineering design. multi-objective model (MOO) established trade-off cost, thereby determining optimal scheme. show that better than XGB performance, with RMSE are 0.99, 118.96, respectively. SHAP method can effectively t w l f ′ c key parameters affecting wall.

Language: Английский

Citations

0