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

Abstract

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.

Prediction of Marshall Stability and Marshall Flow of Asphalt Pavements Using Supervised Machine Learning Algorithms

“…ANFIS has an enhanced estimating ability and is an effective substitute for the computation of complex and nonlinear problems with high accuracy [89][90][91]. It uses training data for learning with any sophisticated mathematical model, then generates the results onto a fuzzy inference system (FIS), similar to ANNs [70,92]. Similar to the process used by ANNs, the ANFIS tool in MATLAB R2020b starts training output and input variables for the evaluation of output and input mapping.…”

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

“…However, Materials 2022, 15, 7412 2 of 22 it is unable to shrink because of surrounding elements and thus this is the most common within the initial days after casting [18]. Additionally, it is reported that the effect of several shrinkage mechanisms contributes to total shrinkage in concrete including autogenous shrinkage, chemical shrinkage, carbonation shrinkage, drying shrinkage, plastic shrinkage, and thermal shrinkage [16,[19][20][21]. Furthermore, the effect of autogenous shrinkage has a major influence on HPC and UHPC during the initial days after casting [22][23][24].…”

“…Moreover, cementitious materials have been created in a diverse variety, including self-compacting concrete (SCC) [ 4 , 5 , 6 ], high-performance concrete (HPC) [ 7 , 8 , 9 ], lightweight concrete (LWC) [ 10 , 11 , 12 ], and ultra-high-performance concrete (UHPC) [ 13 , 14 , 15 ]. These materials are chosen carefully based on the requisite mechanical properties as well as required durability and might be vulnerable to a variety of degradations in which the most detrimental effect is cracking in the matrix [ 16 , 17 , 18 ]. The appearance of these cracks can affect the lifespan of a structure and occurs due to various reasons [ 17 ].…”