Predictive modelling of sustainable lightweight foamed concrete using machine learning novel approach

Ullah, Haji Sami; Khushnood, Rao Arsalan; Aḥmad, Junaid; Farooq, Furqan

doi:10.1016/j.jobe.2022.104746

Cited by 31 publications

(10 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The available studies on foamed concrete have proven that the 𝑓 𝑐𝑜 ′ of foamed concrete was driven by crucial factors consisting of sand and cement content [5,6], dry density, binder ratio, water to cement ratio [2], foaming volume, type of additives (i.e., fly ash, silica fume and superplasticizer) [7], curing conditions [5] and void distribution. Some of these studies have been devoted to proposing empirical models for estimating the 𝑓 𝑐𝑜 ′ of foamed concrete.…”

Section: Introductionmentioning

confidence: 99%

“…The AI technique has been widely applied in the construction industry over the last two decades in structural engineering [10][11][12][13], geotechnical engineering [14][15][16] and material sciences [17][18][19]. The application of the AI technique in prediction problems has been recognized as a reliable and robust computational solution [5]. The application of the AI technique in estimating the engineering properties of foamed concrete has been found in some research studies [5].…”

Section: Introductionmentioning

confidence: 99%

“…The application of the AI technique in prediction problems has been recognized as a reliable and robust computational solution [5]. The application of the AI technique in estimating the engineering properties of foamed concrete has been found in some research studies [5]. Ullah, et al [5] developed AIbased models for 28-day compressive strength and dry density of foamed concrete using gene expression programming.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial intelligence approach for predicting compressive strength of foamed concrete

Loc,

Duc,

Luyen

et al. 2024

UD-JST

View full text Add to dashboard Cite

Accurately predicting the compressive strength of foamed concrete plays a key role in the wide application of foamed concrete in practice. This study investigates the performance of the six AI models in estimating the compressive strength of foamed concrete. A dataset of 150 samples available in the literature was used for training and testing the AI models. The dry density, cement and sand content, and water-to-cement ratio were employed as input parameters, while the 28-day compressive strength was used as the output parameter. Four statistical indicators were utilized to evaluate the performance of the AI models. The study results reveal that the AI models yield an accurate prediction of the compressive strength of foamed concrete. The best performance model in estimating the compressive strength of foamed concrete is the M5Rules model, while the least accurate model depends on the indicators used to measure the accuracy of the AI models.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence approach for predicting compressive strength of foamed concrete

Loc,

Duc,

Luyen

et al. 2024

UD-JST

View full text Add to dashboard Cite

show abstract

“…In this context, Zheng et al (2023) 17 developed an ML model to predict the compressive strength of silica fume concrete. Ullah et al (2022) 18 , 19 used the ML method to predict dry density and compressive strength of lightweight foamed concrete. In this field, Khan et al (2021) 20 used the ML method to formulate the depth of wear of fly-ash concrete.…”

Section: Introductionmentioning

confidence: 99%

An efficient machine learning approach for predicting concrete chloride resistance using a comprehensive dataset

Hosseinzadeh,

Mousavi,

Hosseinzadeh

et al. 2023

Sci Rep

View full text Add to dashboard Cite

By conducting an analysis of chloride migration in concrete, it is possible to enhance the durability of concrete structures and mitigate the risk of corrosion. In addition, the utilization of machine learning techniques that can effectively forecast the chloride migration coefficient of concrete shows potential as a financially viable and less complex substitute for labour-intensive experimental evaluations. The existing models for predicting chloride resistance encounter two primary challenges: the constraints imposed by a limited dataset and the absence of certain input variables. These factors collectively contribute to a decrease in the overall effectiveness of these models. Therefore, this study aims to propose an advanced approach for dataset cleaning, utilizing a comprehensive experimental dataset comprising 1073 pre-existing experimental outcomes. The proposed model for predicting the chloride diffusion coefficient incorporates various input variables, such as water content, cement content, slag content, fly ash content, silica fume content, fine aggregate content, coarse aggregate content, superplasticizer content, fresh density, compressive strength, age of compressive strength test, and age of migration test. The utilization of the artificial neural network (ANN) technique is also employed for the processing of missing data. The current supervised learning incorporates both regression and classification tasks. The efficacy of the proposed models for accurately predicting the chloride diffusion coefficient has been effectively validated. The findings indicate that the XGBoost and SVM algorithms exhibit superior performance compared to other regression prediction algorithms, as evidenced by their high R2 scores of 0.94 and 0.91, respectively. In relation to classification algorithms, the findings demonstrate that the Random Forest, LightGBM, and XGBoost models exhibit the highest levels of accuracy, specifically 0.93, 0.96, and 0.97, respectively. Furthermore, a website has been developed that is capable of predicting the chloride migration coefficient and chloride penetration resistance of concrete.

show abstract

“…Thus, the use of machine learning (ML) anticipation would assist while designing these kinds of complex materials [ 52 , 53 , 54 , 55 , 56 , 57 , 58 ]. Many Civil Engineering issues, including concrete strength prediction [ 59 , 60 , 61 ], creep prediction [ 62 ], crack evaluation, foam concrete strength [ 63 , 64 , 65 ], microstructural features, such as surface chloride content and mechanical behavior of stabilized soil, have been effectively applied to artificial intelligence systems [ 66 , 67 ]. In addition, Table 1 represents applications of MLA in the civil engineering domain to anticipate their desired properties.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Autogenous Shrinkage of Concrete Incorporating Super Absorbent Polymer and Waste Materials through Individual and Ensemble Machine Learning Approaches

Qureshi

Saleem²,

Javed

et al. 2022

Materials

View full text Add to dashboard Cite

The use of superabsorbent polymers, sometimes known as SAP, is a tremendously efficacious method for reducing the amount of autogenous shrinkage (AS) that occurs in high-performance concrete. This study utilizes support vector regression (SVR) as a standalone machine-learning algorithm (MLA) which is then ensemble with boosting and bagging approaches to reduce the bias and overfitting issues. In addition, these ensemble methods are optimized with twenty sub-models with varying the nth estimators to achieve a robust R2. Moreover, modified bagging as random forest regression (RFR) is also employed to predict the AS of concrete containing supplementary cementitious materials (SCMs) and SAP. The data for modeling of AS includes water to cement ratio (W/C), water to binder ratio (W/B), cement, silica fume, fly ash, slag, the filer, metakaolin, super absorbent polymer, superplasticizer, super absorbent polymer size, curing time, and super absorbent polymer water intake. Statistical and k-fold validation is used to verify the validation of the data using MAE and RMSE. Furthermore, SHAPLEY analysis is performed on the variables to show the influential parameters. The SVM with AdaBoost and modified bagging (RF) illustrates strong models by delivering R2 of approximately 0.95 and 0.98, respectively, as compared to individual SVR models. An enhancement of 67% and 63% in the RF model, while in the case of SVR with AdaBoost, it was 47% and 36%, in RMSE and MAE of both models, respectively, when compared with the standalone SVR model. Thus, the impact of a strong learner can upsurge the efficiency of the model.

show abstract

Predictive modelling of sustainable lightweight foamed concrete using machine learning novel approach

Cited by 31 publications

References 83 publications

Artificial intelligence approach for predicting compressive strength of foamed concrete

Artificial intelligence approach for predicting compressive strength of foamed concrete

An efficient machine learning approach for predicting concrete chloride resistance using a comprehensive dataset

Prediction of Autogenous Shrinkage of Concrete Incorporating Super Absorbent Polymer and Waste Materials through Individual and Ensemble Machine Learning Approaches

Contact Info

Product

Resources

About