Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

Khan, Mohsin Ali; Farooq, Furqan; Javed, Muhammad Faisal; Zafar, Adeel; Ostrowski, Krzysztof; Aslam, Fahid; Malazdrewicz, Seweryn; Maślak, Mariusz

doi:10.3390/ma15010058

Cited by 57 publications

(14 citation statements)

References 89 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MEP technique has the ability to significantly outperform similar approaches that are based on numerical experiments. MEP can be used as an efficient substitute to the traditional GP (tree-based) approaches [46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Predicting Marshall Flow and Marshall Stability of Asphalt Pavements Using Multi Expression Programming

Awan¹,

Hussain²,

Javed

et al. 2022

Buildings

View full text Add to dashboard Cite

The traditional method to obtain optimum bitumen content and the relevant parameters of asphalt pavements entails time-consuming, complicated and expensive laboratory procedures and requires skilled personnel. This research study uses innovative and advanced machine learning techniques, i.e., Multi-Expression Programming (MEP), to develop empirical predictive models for the Marshall parameters, i.e., Marshall Stability (MS) and Marshall Flow (MF) for Asphalt Base Course (ABC) and Asphalt Wearing Course (AWC) of flexible pavements. A comprehensive, reliable and wide range of datasets from various road projects in Pakistan were produced. The collected datasets contain 253 and 343 results for ABC and AWC, respectively. Eight input parameters were considered for modeling MS and MF. The overall performance of the developed models was assessed using various statistical measures in conjunction with external validation. The relationship between input and output parameters was determined by performing parametric analysis, and the results of trends were found to be consistent with earlier research findings stating that the developed predicted models are well trained. The results revealed that developed models are superior and efficient in terms of prediction and generalization capability for output parameters, as evident by the correlation coefficient (R) (in this case >0.90) for both ABC and AWC.

show abstract

Section: Introductionmentioning

confidence: 99%

Predicting Marshall Flow and Marshall Stability of Asphalt Pavements Using Multi Expression Programming

Awan¹,

Hussain²,

Javed

et al. 2022

Buildings

View full text Add to dashboard Cite

show abstract

“…Thus, machine-learning (ML) approaches may now be used to predict the compressive strength of concrete, thanks to recent advances in artificial-intelligence algorithms [ 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. The evolution of the advanced prediction algorithms could be used for a variety of purposes, such as regression, classification, and clustering of data [ 53 ]. Estimating the compressive loading capacity of concrete is just one application of the ML regression function.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques

et al. 2022

Self Cite

View full text Add to dashboard Cite

The emission of greenhouse gases and natural-resource depletion caused by the production of ordinary Portland cement (OPC) have a detrimental effect on the environment. Thus, an alternative means is required to produce eco-friendly concrete such as geopolymer concrete (GPC). However, GPC has a complex cementitious matrix and an ambiguous mix design. Aside from that, the composition and proportions of materials utilized may have an impact on the compressive strength. Similarly, the use of robust and efficient machine-learning (ML) approaches is now required to forecast the strength of such a composite cementitious matrix. As a result, this study anticipated the compressive strength of GPC with waste resources using ensemble and non-ensemble ML algorithms. This was accomplished through the use of Anaconda (Python). To build a strong ensemble learner by integrating weak learners, adaptive boosting, random forest (RF), and ensemble learner bagging were employed. Furthermore, ensemble learners were utilized on non-ensemble or weak learners, such as decision trees (DT) and support vector machines (SVM) via regression. The data encompassed 156 statistical samples in which nine variables, namely superplasticizer (kg/m3), fly ash (kg/m3), ground granulated blast-furnace slag (GGBS), temperature (°C), coarse and fine aggregate (kg/m3), sodium silicate (Na2SiO3), and sodium hydroxide (NaOH), were chosen to anticipate the results. Exploring it in depth, twenty sub-models with ensemble boosting and bagging approaches were trained, and tuning was performed to achieve the highest possible coefficient of determination (R2). Moreover, cross K-Fold validation analysis and statistical checks were performed via indicators for the evaluation of the models. The result revealed that ensemble approaches yielded robust performance compared to non-ensemble algorithms. Generally, an ensemble learner with the RF and bagging approach on a DT yielded robust performance by achieving a better R2 as 0.93, and with the lowest statistical errors. The communal model in artificial-intelligence analysis, on average, improved the accuracy of the model.

show abstract

“…The regression analysis as shown in Figure 4a depicts that the model shows robustness performance with R 2 = 0.81. Similarly, Furqan et al [67] forecasted…”

Section: Support Vector Machine Modelingmentioning

confidence: 90%

“…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

Self Cite

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

Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

Cited by 57 publications

References 89 publications

Predicting Marshall Flow and Marshall Stability of Asphalt Pavements Using Multi Expression Programming

Predicting Marshall Flow and Marshall Stability of Asphalt Pavements Using Multi Expression Programming

Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques

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