Application of artificial neural network models for predicting the resilient modulus of recycled aggregates

Oskooei, Parisa Rahimzadeh; Mohammadinia, Alireza; Arulrajah, Arul; Horpibulsuk, Suksun

doi:10.1080/10298436.2020.1791863

Cited by 21 publications

(5 citation statements)

References 47 publications

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“…In a related study, predictions of of fine-grained subgrade soils were executed using various machine learning methods and the upshots of the study clearly show that hybrid adaptive neuro-fuzzy inference system prevailed over gradient boosting regression and ANN with excellent R 2 above 0.98 for both the training and testing set [ 24 ]. Similar outcomes have also been widely reported [ 28 – 31 ]. Instructively, these AI models seem to be highly reliable, but their performance when considering the spatial variability of soil properties for predicting the of unbound granular materials has not been properly addressed.…”

Section: Introductionsupporting

confidence: 90%

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

Ikeagwuani

Nwonu

2022

Innov. Infrastruct. Solut.

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Prediction methods for the estimation of the resilient modulus( ) of unbound granular materials continues to be fraught with challenges associated with the in situ dependency of the existing methods, which makes them unreliable when considering the spatial variability of soil properties. Moreover, the artificial intelligence methods, which prove to be superior for prediction, indicate that more recent and hybrid machine learning methods are more apt. Consequently, a comparative analysis was executed in the present study using advanced statistical approaches and a simple routine method for the prediction of the of coarse-grained soils used as unbound granular materials in highway pavements. This study considered also the spatial variability of the routine soil properties based on the long-term pavement performance database. The result of the descriptive analysis conducted using principal component analysis revealed that the variation in is due to three principal factors, which include the effect of moisture on the fines content, effect of coarse particles on the compaction characteristics and the effect of the soil stress state. Subsequently, prediction of the using multiple linear regression was unreliable based on residual analysis and multi-collinearity problems. Thus, predictive modeling of was then executed using artificial neural network (ANN) and ensemble techniques (random forest and gradient boosting machine), which resulted in very high R 2 values above 0.9, considered apt for MEPD. The ANN model was found to provide a superior prediction performance than the ensemble techniques.

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Section: Introductionsupporting

confidence: 90%

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

Ikeagwuani

Nwonu

2022

Innov. Infrastruct. Solut.

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“…In poor clayey soil, using an optimal amount of CDW improves the unconfined compressive strength, CBR, and permeability. The increased secant modulus and the regression analysis performed for various tests revealed that laboratory results and anticipated values were upgraded as CDW is added to natural soil ( Oskooei et al., 2020 ; Sharma and Sharma, 2020 ). Using recycled CDW as a compaction pile alternative in foundation construction for soil improvement has a good prospect ( Farias et al., 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Improvement of consolidation properties of clay soil using fine-grained construction and demolition waste

Islam

Islam²,

Hoque³

et al. 2022

Heliyon

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“…The M r of compacted subgrade soils was predicted under influences of freeze-thaw cycles and moisture using the GEP and ANN approaches. The formulated GEP and ANN models computed the M r value and attained superior performance in comparison with a variety of other empirical models [29,64]. While determining the elastic modulus of soil, the accuracy of the developed ANN model was superior (R 2 of 0.98) and it supersedes the multiple regression model developed using the same data.…”

Section: Introductionmentioning

confidence: 94%

“…It was revealed that the highest predictive performance and fitness of generalization was attained in cases of stone mastic asphalt, which comprised recycled concrete aggregates. Oskooei et al [29] studied the incorporation of MLP in the form of substructure of an artificial neural network (ANN) technique by considering a detailed database obtained from the available literature to forecast the M r of recycled aggregates. The proposed ANN models are thought to be cost-effective methods for reducing the experimental testing; however, one of the primary drawbacks of utilizing ANN for prediction is that it operates in a black box and does not produce a formula that can be used in the future.…”

Section: Introductionmentioning

confidence: 99%

Prediction Models for Evaluating Resilient Modulus of Stabilized Aggregate Bases in Wet and Dry Alternating Environments: ANN and GEP Approaches

et al. 2022

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Stabilized aggregate bases are vital for the long-term service life of pavements. Their stiffness is comparatively higher; therefore, the inclusion of stabilized materials in the construction of bases prevents the cracking of the asphalt layer. The effect of wet–dry cycles (WDCs) on the resilient modulus (Mr) of subgrade materials stabilized with CaO and cementitious materials, modelled using artificial neural network (ANN) and gene expression programming (GEP) has been studied here. For this purpose, a number of wet–dry cycles (WDC), calcium oxide to SAF (silica, alumina, and ferric oxide compounds in the cementitious materials) ratio (CSAFRs), ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ3), and deviator stress (σ4) were considered input variables, and Mr was treated as the target variable. Different ANN and GEP prediction models were developed, validated, and tested using 30% of the experimental data. Additionally, they were evaluated using statistical indices, such as the slope of the regression line between experimental and predicted results and the relative error analysis. The slope of the regression line for the ANN and GEP models was observed as (0.96, 0.99, and 0.94) and (0.72, 0.72, and 0.76) for the training, validation, and test data, respectively. The parametric analysis of the ANN and GEP models showed that Mr increased with the DMR, σ3, and σ4. An increase in the number of WDCs reduced the Mr value. The sensitivity analysis showed the sequences of importance as: DMR > CSAFR > WDC > σ4 > σ3, (ANN model) and DMR > WDC > CSAFR > σ4 > σ3 (GEP model). Both the ANN and GEP models reflected close agreement between experimental and predicted results; however, the ANN model depicted superior accuracy in predicting the Mr value.

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Application of artificial neural network models for predicting the resilient modulus of recycled aggregates

Cited by 21 publications

References 47 publications

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

Improvement of consolidation properties of clay soil using fine-grained construction and demolition waste

Prediction Models for Evaluating Resilient Modulus of Stabilized Aggregate Bases in Wet and Dry Alternating Environments: ANN and GEP Approaches

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