GEP tree-based computational AI approach to evaluate unconfined compression strength characteristics of Fly ash treated alkali contaminated soils
Source abstract
Abstract
The structural failures induced by alkali-contamination of soils are well established by earlier researchers. In the present study, the effect of alkali concentration (2 M and 4 M) and duration of curing on the unconfined compressive strength (UCS) of two types of soils (Kaolin and BC soil) were evaluated. Further, the effect of fly ash (FA) addition (10%, 15%, and 20%) on the UCS of alkali-contaminated soils was also determined. The results revealed that alkali contamination resulted in reducing UCS (by 36% and 46%), observed for kaolin and BC soil, respectively. The addition of FA has resulted in a linear increase in UCS of both soils. The optimum content was noted at 15%, attributed to the pozzolanic reaction initiated by alkali and subsequent gain of UCS with the formation of calcium (with FA addition) based hydration compounds. In addition, three attributes, i.e., FA content, curing period, and alkali concentration, have been used to develop GEP tree-based models to furnish simple prediction equations for calculating the UCS of Kaolin and BC soils. The accuracy of the developed models were initially validated using various statistical checks such as R, MAE, RMSE and RSE. For the 2nd level validation, parametric and sensitivity analyses were carried out for both soils, which yielded a comparable variation and contribution of each input conforming to the literature.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Concrete and Cement Materials Research · Landfill Environmental Impact Studies · Soil and Unsaturated Flow
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (76)
Show source-provided references
1. A study on strength behavior of alkali-contaminated soils treated with fly ash
2. Soil erosion and sediment dynamics in the Anthropocene: a review of human impacts during a period of rapid global environmental change
3. Applicability of cavity-throat connecting model for estimating the hydraulic conductivity of fine-grained soils: a geometrical and mathematical approach10.1007/s11368-018-2054-8 ↗
4. Ferric chloride treatment to control alakli induced heave in weathered red earth10.1007/s10706-005-1137-7 ↗
5. Fly ash to control alkali-induced volume changes in soils10.1680/grim.2009.162.4.167 ↗
6. Highly efficient remediation of decabromodiphenyl ether-contaminated soil using mechanochemistry in the presence of additive and its mechanism10.1016/j.jenvman.2021.113595 ↗
7. Influence of fly ash and quicklime addition on behaviour of municipal solid wastes10.1007/s11368-013-0720-4 ↗
8. Effect of zeolitization on the volume-change behaviour of kaolinitic soils10.1139/t06-048 ↗
9. Brine treatment technologies towards minimum/zero liquid discharge and resource recovery: State of the art and techno-economic assessment10.1016/j.jenvman.2021.113681 ↗
10. Mitchell · 2005 · 3
11. Mineralogical changes and geotechnical properties of an expansive soil interacted with caustic solution10.1007/s12665-009-0260-9 ↗
12. Calcium-based stabiliser treatment of sulfate-bearing soils10.1680/grim.13.00008 ↗
13. Paper and wood industry waste as a sustainable solution for environmental vulnerabilities of expansive soil: a novel approach.10.1016/j.jenvman.2020.110285 ↗
14. Ground heave from caustic soda solution spillage—a case study10.3208/sandf1972.34.2_13 ↗
15. Effect of clay minerals on immobilization of heavy metals and microbial activity in a sewage sludge-contaminated soil (8 pp)10.1065/jss2005.05.141 ↗
16. Effect of initial compaction pressure and elevated temperature on swelling pressure of two Indian bentonites10.1007/s12665-020-08937-0 ↗
17. On the recent trends in expansive soil stabilization using calcium-based stabilizer materials (CSMs): a comprehensive review
18. Emerging trends in expansive soil stabilisation: a review
19. Review of stabilization of clays and expansive soils in pavements and lightly loaded structures—history, practice, and future10.1061/(ASCE)0899-1561(2002)14:6(447) ↗
20. Reliability-based design optimization of chemically stabilized coal gangue
21. Activation of fly ash cementitious systems in the presence of quicklime: part I. Compressive strength and pozzolanic reaction rate10.1016/j.cemconres.2003.08.008 ↗
22. Water absorption process effect on strength of Ayazini tuff, such as the uniaxial compressive strength (UCS), flexural strength and freeze and thaw effect10.1007/s12665-013-2819-8 ↗
23. Utilization of fly ash in geotechnical construction
24. Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network10.1016/j.eswa.2011.01.156 ↗
25. Prediction of triaxial behavior of recycled aggregate concrete using multivariable regression and artificial neural network techniques10.1016/j.conbuildmat.2019.07.155 ↗
26. Artificial neural network prediction models for soil compaction and permeability10.1007/s10706-007-9146-3 ↗
27. Application of 3-algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil. Multiscale and Multidisciplinary Modeling
28. Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP10.1016/j.jenvman.2021.112420 ↗
29. Computational AI prediction models for residual tensile strength of GFRP bars aged in the alkaline concrete environment10.1016/j.oceaneng.2021.109134 ↗
30. Assessing the suitability of hybridizing the Cuckoo optimization algorithm with ANN and ANFIS techniques to predict daily evaporation10.1007/s12665-015-5058-3 ↗
31. Cramer, N.L. A representation for the adaptive generation of simple sequential programs. in proceedings of an International Conference on Genetic Algorithms and the Applications. 1985.
32. Koza, J., On the programming of computersby means of natural selection. 1992.
33. Ferreira · 2006
34. Prediction model for compressive arch action capacity of RC frame structures under column removal scenario using gene expression programming10.1016/j.istruc.2020.02.028 ↗
35. Prediction of catenary action capacity of RC beam-column substructures under a missing column scenario using evolutionary algorithm10.1007/s12205-021-0431-0 ↗
36. Predicting the compaction characteristics of expansive soils using two genetic programming-based algorithms
37. Predicting and controlling the ground vibration using gene expression programming (GEP) and teaching–learning-based optimization (TLBO) algorithms10.1007/s12665-021-10052-7 ↗
38. Roadheader performance prediction using genetic programming (GP) and gene expression programming (GEP) techniques
39. Estimation of soil temperature using gene expression programming and artificial neural networks in a semiarid region10.1007/s12665-017-6395-1 ↗
40. Alavi, A.H., et al., Formulation of secant and reloading soil deformation moduli using multi expression programming. Engineering Computations, 2012.
41. Prediction of SWCC using artificial intelligent systems: a comparative study10.1016/j.scient.2011.09.002 ↗
42. Design equations for prediction of pressuremeter soil deformation moduli utilizing expression programming systems10.1007/s00521-012-1144-6 ↗
43. Genetic programming-based attenuation relationship: an application of recent earthquakes in turkey10.1016/j.cageo.2008.10.015 ↗
44. Stabilisation of expansive clay bed using calcium chloride solution10.1680/grim.2006.10.1.39 ↗
45. Soil · 2017
46. 2166. Standard test method for unconfined compressive strength of cohesive soil
47. Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems10.1016/j.compag.2018.03.010 ↗
48. Smart computing models of California bearing ratio, unconfined compressive strength, and resistance value of activated ash-modified soft clay soil with adaptive neuro-fuzzy inference system and ensemble random forest regression techniques
49. ASTM, C., Standard specification for coal fly ash and raw or calcined natural pozzolan for use in concrete. 2012.
50. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance10.3354/cr030079 ↗
51. Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature10.5194/gmd-7-1247-2014 ↗
52. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation10.7717/peerj-cs.623 ↗
53. Semi-analytical model for compressive arch action capacity of RC frame structures
54. Arshad Jamal Evaluation of tensile strength degradation of GFRP rebars in harsh alkaline conditions using non-linear genetic-based models
55. Sustainable use of fly-ash: use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete10.1016/j.asej.2021.03.018 ↗
56. The performance comparison of the soft computing methods on the prediction of soil compaction parameters10.1007/s12517-020-5171-9 ↗
57. New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach10.1016/j.advengsoft.2017.03.011 ↗
58. Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming10.1016/j.jhazmat.2019.121322 ↗
59. Functional potential of sewage sludge digestate microbes to degrade aliphatic hydrocarbons during bioremediation of a petroleum hydrocarbons contaminated soil10.1016/j.jenvman.2020.111648 ↗
60. Laboratory techniques in the prediction of soil erodibility
61. Sustainable utilization of foundry waste: Forecasting mechanical properties of foundry sand based concrete using multi-expression programming10.1016/j.scitotenv.2021.146524 ↗
62. Application of Gene Expression Programming (GEP) for the prediction of compressive strength of geopolymer concrete10.3390/ma14051106 ↗
63. Development of GP and GEP models to estimate an environmental issue induced by blasting operation10.1007/s10661-018-6719-y ↗
64. Estimation of soil cation exchange capacity using genetic expression programming (GEP) and multivariate adaptive regression splines (MARS)10.1016/j.jhydrol.2015.08.025 ↗
65. Modeling the compressive strength of geopolymeric binders by gene expression programming-GEP10.1016/j.eswa.2013.04.014 ↗
66. Nonlinear genetic-based models for prediction of flow number of asphalt mixtures10.1061/(ASCE)MT.1943-5533.0000154 ↗
67. Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models10.1016/j.cemconres.2021.106449 ↗
68. ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions10.1016/j.asoc.2021.107595 ↗
69. Prediction model for compressive arch action capacity of RC frame structures under column removal scenario using gene expression programming
70. Prediction of chloride diffusivity in concrete using artificial neural network: Modelling and performance evaluation
71. Using artificial neural network and genetics algorithm to estimate the resilient modulus for stabilized subgrade and propose new empirical formula10.1016/j.trgeo.2020.100358 ↗
72. Sustainable utilization of foundry waste: forecasting mechanical properties of foundry sand based concrete using multi-expression programming10.1016/j.scitotenv.2021.146524 ↗
73. Geopolymerization of expansive black cotton soils with alkali-activated binders
74. Strength and durability characteristics of cement and class F fly ash-treated black cotton soil
75. Experimental evaluation of kaolin stabilised with class F fly ash
76. Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest