Multi Expression Programming Model for Strength Prediction of Fly-Ash-Treated Alkali-Contaminated Soils

Abstract

Rapid industrialization is leading to the pollution of underground natural soil by alkali concentration which may cause problems for the existing expansive soil in the form of producing expanding lattices. This research investigates the effect of stabilizing alkali-contaminated soil by using fly ash. The influence of alkali concentration (2 N and 4 N) and curing period (up to 28 days) on the unconfined compressive strength (UCS) of fly ash (FA)-treated (10%, 15%, and 20%) alkali-contaminated kaolin and black cotton (BC) soils was investigated. The effect of incorporating different dosages of FA (10%, 15%, and 20%) on the UCSkaolin and UCSBC soils was also studied. Sufficient laboratory test data comprising 384 data points were collected, and multi expression programming (MEP) was used to create tree-based models for yielding simple prediction equations to compute the UCSkaolin and UCSBC soils. The experimental results reflected that alkali contamination resulted in reduced UCS (36% and 46%, respectively) for the kaolin and BC soil, whereas the addition of FA resulted in a linear rise in the UCS. The optimal dosage was found to be 20%, and the increase in UCS may be attributed to the alkali-induced pozzolanic reaction and subsequent gain of the UCS due to the formation of calcium-based hydration compounds (with FA addition). Furthermore, the developed models showed reliable performance in the training and validation stages in terms of regression slopes, R, MAE, RMSE, and RSE indices. Models were also validated using parametric and sensitivity analysis which yielded comparable variation while the contribution of each input was consistent with the available literature.

Prognosis of compressive strength of fly‐ash‐based geopolymer‐modified sustainable concrete with ML algorithms

“…Khan et al 32 adopted the MEP‐based algorithm to calculate the strength of the fly‐ash‐treated alkali‐contaminated soils using 384 datasets with four different input parameters. The study concluded that the developed model had an R ‐value and RMSE of 0.937 and 18.271, respectively.…”

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

“…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 ].…”

“…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 ]. One cause of cracking in the cementitious matrix is restrained shrinkage, which occurs during the shrinkage of structural elements.…”

“…Over the last few years, SAP has been effectively used to reduce concrete shrinkage in high pH concrete matrixes by optimizing their release rate and water absorption capacity. The use of SAP in the concrete mixture in a percentage of approximately 0.2–0.6% by cement mass has proven to be effective in reducing drying shrinkage, autogenous shrinkage, and stress improvement [ 17 , 46 , 47 ]. However, some subsequent lateral deformations may be detected when SAP is depleted of its contents.…”

“…However, some subsequent lateral deformations may be detected when SAP is depleted of its contents. Moreover, their efficiency depends upon their nature, initial cross-linking, and chemical components [ 17 ]. Furthermore, it can be measured before mixing concrete by conducting absorption tests through the filtration method and tea bag method.…”