Ensemble machine learning model for corrosion initiation time estimation of embedded steel reinforced self-compacting concrete

Salami, Babatunde Abiodun; Rahman, Syed Masiur; Oyehan, Tajudeen A.; Maslehuddin, Mohammed; Dulaijan, Salah U. Al

doi:10.1016/j.measurement.2020.108141

Cited by 68 publications

(23 citation statements)

References 54 publications

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“…Bui et al [ 33 ] employed a modified firefly algorithm with ANN on high performance concrete (HPC) and reported better performance of the model. Moreover, Salamai et al [ 34 ] report good accuracy of R 2 = 0.9867 by using the RF algorithm. In turn, Cai et al [ 35 ] use various supervised machine ensemble algorithms for the prediction of chloride penetration in the RC structure situated in a marine environment.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

et al. 2021

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Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.

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Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

et al. 2021

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“…In order to get the best results, ensemble learning trains numerous base learners to aggregate their findings according to a predetermined methodology [ 30 ]. The design and building of fundamental learners, as well as their integration, is critical to ensemble learning algorithms.…”

Section: Soft Computing Techniquesmentioning

confidence: 99%

Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning

et al. 2022

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In civil engineering, ultra-high-strength concrete (UHSC) is a useful and efficient building material. To save money and time in the construction sector, soft computing approaches have been used to estimate concrete properties. As a result, the current work used sophisticated soft computing techniques to estimate the compressive strength of UHSC. In this study, XGBoost, AdaBoost, and Bagging were the employed soft computing techniques. The variables taken into account included cement content, fly ash, silica fume and silicate content, sand and water content, superplasticizer content, steel fiber, steel fiber aspect ratio, and curing time. The algorithm performance was evaluated using statistical metrics, such as the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The model’s performance was then evaluated statistically. The XGBoost soft computing technique, with a higher R2 (0.90) and low errors, was more accurate than the other algorithms, which had a lower R2. The compressive strength of UHSC can be predicted using the XGBoost soft computing technique. The SHapley Additive exPlanations (SHAP) analysis showed that curing time had the highest positive influence on UHSC compressive strength. Thus, scholars will be able to quickly and effectively determine the compressive strength of UHSC using this study’s findings.

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“…The determination of a variety of structural properties of reinforced concrete is an important issue that has piqued the interest of researchers, who have attempted to simulate them using different ML techniques [ 31 , 32 , 33 , 34 ]. With the advancement of computer science and the increasing volume of associated experimental datasets, data-driven approaches based on machine learning (ML) algorithms have recently emerged as alternative methods for establishing prediction models using comprehensive experimental data and information [ 35 , 36 , 37 , 38 , 39 ]. Some of the most commonly and successfully deployed ML algorithms for estimating the BS of FRP are artificial neural networks (ANNs), support vector machines (SVMs), multiple linear regression (MLR), genetic and evolutionary algorithms (GEAs), random forest (RF), and ensemble learning (gradient boosted regression trees [GBRT]) [ 18 , 27 , 28 , 35 , 40 , 41 , 42 , 43 , 44 , 45 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model

et al. 2022

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Rebars made of fiber-reinforced plastic (FRP) might be the future reinforcing material, replacing mild steel rebars, which are prone to corrosion. The bond characteristics of FRP rebars differ from those of mild steel rebars due to their different stress-strain behavior than mild steel. As a result, determining the bond strength (BS) qualities of FRP rebars is critical. In this work, BS data for FRP rebars was investigated, utilizing non-linear capabilities of gene expression programming (GEP) on 273 samples. The BS of FRP and concrete was considered a function of bar surface (Bs), bar diameter (db), concrete compressive strength (fc′), concrete-cover-bar-diameter ratio (c/d), and embedment-length-bar-diameter ratio (l/d). The investigation of the variable number of genetic parameters such as number of chromosomes, head size, and number of genes was undertaken such that 11 different models (M1–M11) were created. The results of accuracy evaluation parameters, namely coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) imply that the M11 model outperforms other created models for the training and testing stages, with values of (0.925, 0.751, 1.08) and (0.9285, 0.802, 1.11), respectively. The values of R2 and error indices showed that there is very close agreement between the experimental and predicted results. 30 number chromosomes, 9 head size, and 5 genes yielded the optimum model. The parametric analysis revealed that db, c/d, and l/d significantly affected the BS. The FRP rebar diameter size is greater than 10 mm, whereas a l/d ratio of more than 12 showed a considerable decrease in BS. In contrast, the rise in c/d ratio revealed second-degree increasing trend of BS.

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Ensemble machine learning model for corrosion initiation time estimation of embedded steel reinforced self-compacting concrete

Cited by 68 publications

References 54 publications

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning

Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model

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