Study and prediction of photocurrent density with external validation using machine learning models DOI
Nepal Sahu, Chandrashekhar Azad, Uday Kumar

et al.

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 92, P. 1335 - 1355

Published: Nov. 1, 2024

Language: Английский

Multi-performance optimization of low-carbon geopolymer considering mechanical, cost, and CO2 emission based on experiment and interpretable learning DOI
Shiqi Wang, Keyu Chen, Jinlong Liu

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 425, P. 136013 - 136013

Published: April 1, 2024

Language: Английский

Citations

8
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
Prediction model for calculation of the limestone powder concrete carbonation depth DOI
Andrija Radović, Vedran Carević, Snežana Marinković

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 86, P. 108776 - 108776

Published: Feb. 8, 2024

Language: Английский

Citations

8
Integrated hybrid modeling and SHAP (SHapley Additive exPlanations) to predict and explain the adsorption properties of thermoplastic polyurethane (TPU) porous materials DOI Creative Commons
Kangyong Ma

RSC Advances, Journal Year: 2024, Volume and Issue: 14(15), P. 10348 - 10357

Published: Jan. 1, 2024

As a novel type of oil-water separation material, thermoplastic polyurethane (TPU) porous material exhibits many excellent properties such as low density, high specific surface area, and outstanding performance. However, the performance materials is often impeded by various factors, conducting numerous experiments to investigate relationship between these factors adsorption can be both expensive time-consuming. an alternative experiments, machine learning (ML) techniques used estimate experimental results. Therefore, in this study, we developed integrated hybrid model predict replaced some experiments. We also constructed XGBoost (XGB), Decision Tree Regressor (DT), K-Neighbors (KNN), Bagging Regression (BGR), Extra Trees (ETR) single models properties, all which exhibited prediction accuracy. On basis, SHAP values were employed explain influence single-factor multi-factor characteristics on properties.

Language: Английский

Citations

6
Can carbonation depth be measured in a nondestructive way? High-frequency quantitative ultrasound imaging for cement paste DOI
Seungo Baek, Hyeong-Ki Kim, Michael L. Oelze

et al.

Cement and Concrete Research, Journal Year: 2024, Volume and Issue: 180, P. 107519 - 107519

Published: April 25, 2024

Language: Английский

Citations

4
A hybrid artificial intelligence approach for modeling the carbonation depth of sustainable concrete containing fly ash DOI Creative Commons
Ramin Kazemi

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

Published: May 25, 2024

One of the major challenges in civil engineering sector is durability reinforced concrete structures against carbonation during physico-chemical process interaction hydrated cementitious composites with carbon dioxide. This aggressive causes penetration into reinforcement part, which affects behavior structure its lifetime due to corrosion risk. A countermeasure using alternative materials improve texture and resist increased depth (CD). Considering that CD test requires a long time skilled technician, this study strives provide an approach by moving from traditional laboratory-based methods towards artificial intelligence (AI) techniques for modeling sustainable containing fly ash (CCFA). Despite development single AI models so far, it undeniable utilizing metaheuristic optimization form hybrid can their performance. To end, new model integration biogeography-based (BBO) technique neural network (ANN) developed first estimate CCFA. The error distribution results revealed 59% ANN predictions had errors within range (- 1 mm, mm], while corresponding percentage ANN-BBO was 70%, indicating 11% reduction prediction proposed model. Furthermore, A10-index highlighted performance improvement 78% model, met closeness predicted values observed ones, value index 0.5019 0.8947, respectively. Analyzing cross-validation confirmed reliability generalizability Also, three most influential variables estimating were exposure (27%), dioxide concentration (22%), water/binder (18%), Finally, superiority verified comparing previous studies' models.

Language: Английский

Citations

4
Effect of temperature and superplasticizer on hydration of C3S and carbonation products of C-S-H DOI
Xiaochuan Hu, Lei Xu,

Molan Li

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 444, P. 137864 - 137864

Published: Aug. 9, 2024

Language: Английский

Citations

4
Concrete carbonation depth prediction model based on a gradient-boosting decision tree and different metaheuristic algorithms DOI Creative Commons

Junxi Wu,

Guoyan Zhao, M. Wang

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 21, P. e03864 - e03864

Published: Oct. 16, 2024

Language: Английский

Citations

4
Carbonation models using mix-design parameters for concretes with supplementary cementitious materials DOI
Sundar Rathnarajan, Radhakrishna G. Pillai

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112392 - 112392

Published: March 1, 2025

Language: Английский

Citations

0
Concrete Carbonization Prediction Method Based on Bagging and Boosting Fusion Framework DOI Creative Commons
Qingfu Li,

A. Xu

Buildings, Journal Year: 2025, Volume and Issue: 15(8), P. 1349 - 1349

Published: April 18, 2025

Concrete carbonation is an important factor causing corrosion of steel reinforcement, which leads to damage reinforced concrete structures. To address the problem depth prediction, this paper proposes a prediction model. The framework synergistically integrates Bagging and Boosting algorithms, specifically replacing original Random Forest base learner with gradient variants (LightGBM (version 4.1.0), XGBoost 2.1.1), CatBoost 1.2.5)). This hybrid approach exploits strengths all three algorithms reduce variance bias, further improve accuracy, Bayesian optimization were used fine-tune hyperparameters, resulting in hybrid-integrated models: Forest–LightGBM Fusion Framework, Forest–XGBoost Forest–CatBoost Framework. These models trained on dataset containing 943 case sets six input variables (FA, t, w/b, B, RH, CO2). comprehensively evaluated using comprehensive scoring formula Taylor diagrams. results showed that model outperformed single model, RF–CatBoost fusion having highest test set performance (R2 = 0.9674, MAE 1.4199, RMSE 2.0648, VAF 96.78%). In addition, Framework identified exposure t CO2 concentration as most features. demonstrates applicability predictive based predicting carbonation, providing valuable insights into durability design concrete.

Language: Английский

Citations

0