Gradient Boosting Hybridized with Exponential Natural Evolution Strategies for Estimating the Strength of Geopolymer Self-Compacting Concrete DOI Open Access

Samuel Alves Basilio,

Leonardo Goliatt

Knowledge-Based Engineering and Sciences, Journal Year: 2022, Volume and Issue: 3(1), P. 1 - 16

Published: April 30, 2022

The current global demand to minimize carbon dioxide (CO2$) emissions from Portland cement manufacturing processes has led the use of environmentally friendly additives in products. so-called green cementitious composites have become increasingly essential design composite mixtures, providing environmental compatibility concrete as a building material. Engineers face difficult problem predicting mechanical properties due their changing nature under various circumstances. Machine learning models then emerge surrogate perform this task. very such challenge for machine learning. This study presents gradient boosting algorithm hybridized with Natural Exponential Evolution Strategies inspired by predict geopolymeric self-compacting concrete. hybrid model is used evolve parameters, automating selection best set internal parameters estimate strength geopolymer Results show predictive ability superiority and optimization algorithms hybridization compared manually tuned models. In addition, approach can laboratory work, potentially optimize experimental design, reduce sample production time associated activity burden

Language: Английский

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects DOI Creative Commons
Ibomoiye Domor Mienye, Yanxia Sun

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 99129 - 99149

Published: Jan. 1, 2022

Ensemble learning techniques have achieved state-of-the-art performance in diverse machine applications by combining the predictions from two or more base models. This paper presents a concise overview of ensemble learning, covering three main methods: bagging, boosting, and stacking, their early development to recent algorithms. The study focuses on widely used algorithms, including random forest, adaptive boosting (AdaBoost), gradient extreme (XGBoost), light (LightGBM), categorical (CatBoost). An attempt is made concisely cover mathematical algorithmic representations, which lacking existing literature would be beneficial researchers practitioners.

Language: Английский

Citations

513
Prediction of compressive strength of rice husk ash concrete based on stacking ensemble learning model DOI
Qingfu Li, Zongming Song

Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 382, P. 135279 - 135279

Published: Nov. 22, 2022

Language: Английский

Citations

89
Optimization design for alkali-activated slag-fly ash geopolymer concrete based on artificial intelligence considering compressive strength, cost, and carbon emission DOI
Yue Li, Jiale Shen, Hui Lin

et al.

Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 75, P. 106929 - 106929

Published: May 25, 2023

Language: Английский

Citations

76
Interpretable machine learning for predicting the strength of 3D printed fiber-reinforced concrete (3DP-FRC) DOI
Md Nasir Uddin, Junhong Ye, Bo-Yu Deng

et al.

Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 72, P. 106648 - 106648

Published: April 25, 2023

Language: Английский

Citations

48
Machine learning and interactive GUI for concrete compressive strength prediction DOI Creative Commons
Mohamed Kamel Elshaarawy, Mostafa M. Alsaadawi, Abdelrahman Kamal Hamed

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 19, 2024

Concrete compressive strength (CS) is a crucial performance parameter in concrete structure design. Reliable prediction reduces costs and time design prevents material waste from extensive mixture trials. Machine learning techniques solve structural engineering challenges such as CS prediction. This study used Learning (ML) models to enhance the of CS, analyzing 1030 experimental data ranging 2.33 82.60 MPa previous research databases. The ML included both non-ensemble ensemble types. were regression-based, evolutionary, neural network, fuzzy-inference-system. Meanwhile, consisted adaptive boosting, random forest, gradient boosting. There eight input parameters: cement, blast-furnace-slag, aggregates (coarse fine), fly ash, water, superplasticizer, curing days, with output. Comprehensive evaluations include visual quantitative methods k-fold cross-validation assess study's reliability accuracy. A sensitivity analysis using Shapley-Additive-exPlanations (SHAP) was conducted understand better how each variable affects CS. findings showed that Categorical-Gradient-Boosting (CatBoost) model most accurate during testing stage. It had highest determination-coefficient (R

Language: Английский

Citations

41
Metaheuristic optimization based- ensemble learners for the carbonation assessment of recycled aggregate concrete DOI Creative Commons
Emadaldin Mohammadi Golafshani, Ali Behnood, Taehwan Kim

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 159, P. 111661 - 111661

Published: April 23, 2024

This study addresses the enhanced prevalence of carbonation, a process accelerating steel reinforcement corrosion, in recycled aggregate concrete (RAC) compared to natural concrete. Traditional carbonation depth assessment methods RAC are noted for being labor-intensive, costly, and requiring specialized expertise. There's deficiency application machine learning techniques accurately predicting RAC, gap this aims fill. Utilizing extreme gradient boosting (XGBoost) technique, recognized its efficacy ensemble learning, innovates modeling RAC. It emphasizes criticality hyperparameter optimization XGBoost algorithm maximizing model accuracy. To achieve this, three novel metaheuristic algorithms, including reptile search (RSA), Aquila optimizer (AO), arithmetic (AOA), were introduced as global optimizers tunning hyperparameters. The was underpinned by comprehensive database compiled from extensive literature, facilitating development an accurate model. Through rigorous evaluations, sensitivity analyses, Wilcoxon signed-rank test, runtime comparisons, synthesized models demonstrated exceptional accuracy, with coefficients determination exceeding 0.95. XGBoost-AO algorithm, particular, showcased superior performance, XGBoost-RSA providing efficient predictions considering runtime. SHapley Additive exPlanations (SHAP) interpretation highlighted environmental conditions significant influencers. A user-friendly graphical user interface developed, enhancing practical utility findings progression over time. research significantly advances predictive accuracy contributing sustainable management infrastructures emphasizing integration advanced structural engineering advancements.

Language: Английский

Citations

19
Multi-objective optimization design of recycled aggregate concrete mixture proportions based on machine learning and NSGA-II algorithm DOI

Mengtian Fan,

Yue Li, Jiale Shen

et al.

Advances in Engineering Software, Journal Year: 2024, Volume and Issue: 192, P. 103631 - 103631

Published: March 23, 2024

Language: Английский

Citations

18
Sustainable foam glass property prediction using machine learning: A comprehensive comparison of predictive methods and techniques DOI Creative Commons

Mohamed Abdellatief,

Leong Sing Wong,

Norashidah Md Din

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104089 - 104089

Published: Jan. 1, 2025

Language: Английский

Citations

9
An Ultrasonic-AI Hybrid Approach for Predicting Void Defects in Concrete-Filled Steel Tubes via Enhanced XGBoost with Bayesian Optimization DOI Creative Commons
Shuai Wan, Shaozhi Li, George Z. Chen

et al.

Case Studies in Construction Materials, Journal Year: 2025, Volume and Issue: unknown, P. e04359 - e04359

Published: Feb. 1, 2025

Language: Английский

Citations

6
Leveraging machine learning to evaluate the effect of raw materials on the compressive strength of ultra-high-performance concrete DOI Creative Commons

Mohamed Abdellatief,

G. Murali,

Saurav Dixit

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: 25, P. 104542 - 104542

Published: March 1, 2025

Language: Английский

Citations

3