Interpretable machine‐learning models for predicting creep recovery of concrete DOI
Shengqi Mei, Xiaodong Liu, Xingju Wang

et al.

Structural Concrete, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 16, 2024

Abstract Creep recovery of concrete is essential for accurately assessing the performance structures over service time. Existing creep models exhibit low accuracy, and influencing factors remain inadequately elucidated. In this paper, interpretable machine learning (ML) techniques were employed to develop a prediction model recovery. Several ML selected including random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost) light (LGBM). order maximize sample size dataset, 109 sets data collected from existing literatures training. Feature selection utilized determine input parameters models, 12 variables selected. The fine‐tuned using Bayesian optimization techniques. To ensure reliability 10‐fold cross‐validation splitting implemented. results indicate that exhibited higher accuracy compared model. Among these LGBM demonstrated superior efficiency stability (with R 2 = 0.993, 0.978, 0.973 training, testing, validation sets, respectively). Shapley additive explanations (SHAP) interpret significance each parameter on prediction. Duration after unloading, stress magnitude, ambient relative humidity main feature Upon comparing factors, it was discerned there exists distinct difference between concrete.

Language: Английский

Optimizing high-strength concrete compressive strength with explainable machine learning DOI Creative Commons
Sanjog Chhetri Sapkota,

Christina Panagiotakopoulou,

Dipak Dahal

et al.

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2025, Volume and Issue: 8(3)

Published: Feb. 3, 2025

Language: Английский

Citations

3
Predicting Compressive Strength of Oil Well Cement Slurries: Novel Moduli‐Based Analysis of Chemical Composition at Different Temperature Condition DOI Open Access
Mohammed A. Jamal, Ahmed Salih Mohammed, Jagar A. Ali

et al.

The Structural Design of Tall and Special Buildings, Journal Year: 2025, Volume and Issue: 34(2)

Published: Jan. 30, 2025

ABSTRACT This study evaluates the impact of cement chemical composition on compressive strength (CS) slurries, utilizing silica fume (SF) and fly ash (FA) as additional materials. A comprehensive analysis was conducted 317 datasets from literature, focusing factors including silicon dioxide (SiO₂), aluminum oxide (Al₂O₃), calcium (CaO), iron (Fe₂O₃), water‐to‐binder (w/b) ratio, SF FA content, well curing time temperature. The research presents three geochemical moduli, namely, silicate modulus (SM), aluminate (AM), hydraulic (HM), to assess forecast CS. investigation full quadratic (FQ) cubic (CUB) models underscores precision prediction corroborated by statistical metrics, such scatter index (SI), root mean squared error (RMSE), correlation coefficient ( R 2 ). Univariate, bivariate, multivariate evaluations indicate that SM, AM, HM significantly decrease input parameters while preserving or enhancing model accuracy. ideal replacement percentages for maximize were determined be 14.6% 11.6%, respectively. optimal values 2.62, 1.38, 2.21, results establish a solid framework optimizing formulations, presenting sustainable alternatives improved mechanical performance decreased material consumption in oil cementing building applications.

Language: Английский

Citations

1
EVALUATING THE MECHANICAL AND DURABILITY PROPERTIES OF SUSTAINABLE LIGHTWEIGHT CONCRETE INCORPORATING THE VARIOUS PROPORTIONS OF WASTE PUMICE AGGREGATE DOI Creative Commons
Hafiz Muhammad Shahzad Aslam, Atteq ur Rehman, Kennedy C. Onyelowe

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103496 - 103496

Published: Nov. 1, 2024

Language: Английский

Citations

4
Advanced machine learning techniques for predicting concrete mechanical properties: a comprehensive review of models and methodologies DOI
Fangyuan Li,

Md. Sohel Rana,

Muhammad Ahmed Qurashi

et al.

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2024, Volume and Issue: 8(1)

Published: Dec. 18, 2024

Language: Английский

Citations

3
Estimation of compressive strength of concrete with manufactured sand and natural sand using interpretable artificial intelligence DOI Creative Commons
Xiaodong Liu, Shengqi Mei, Xingju Wang

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 21, P. e03840 - e03840

Published: Oct. 10, 2024

Language: Английский

Citations

2
Interpretable machine‐learning models for predicting creep recovery of concrete DOI
Shengqi Mei, Xiaodong Liu, Xingju Wang

et al.

Structural Concrete, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 16, 2024

Abstract Creep recovery of concrete is essential for accurately assessing the performance structures over service time. Existing creep models exhibit low accuracy, and influencing factors remain inadequately elucidated. In this paper, interpretable machine learning (ML) techniques were employed to develop a prediction model recovery. Several ML selected including random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost) light (LGBM). order maximize sample size dataset, 109 sets data collected from existing literatures training. Feature selection utilized determine input parameters models, 12 variables selected. The fine‐tuned using Bayesian optimization techniques. To ensure reliability 10‐fold cross‐validation splitting implemented. results indicate that exhibited higher accuracy compared model. Among these LGBM demonstrated superior efficiency stability (with R 2 = 0.993, 0.978, 0.973 training, testing, validation sets, respectively). Shapley additive explanations (SHAP) interpret significance each parameter on prediction. Duration after unloading, stress magnitude, ambient relative humidity main feature Upon comparing factors, it was discerned there exists distinct difference between concrete.

Language: Английский

Citations

0