Predictive models of volumetric stability (durability) and erodibility of lateritic soil treated with different nanotextured bio-ashes with application of loss of strength on immersion; GP, ANN and EPR performance study

Onyelowe, Kennedy C.; Ebid, Ahmed M.; Nwobia, Light I.

doi:10.1016/j.clema.2021.100006

Cited by 10 publications

(2 citation statements)

References 32 publications

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“…Over the past two decades, advances in computational tools have considerably facilitated the use of computational intelligence techniques in fields like data mining and soft computing 33 . Among these techniques, EPR stands out due to its integration of traditional regression and GP elements 56 , making it effective for modeling complex environmental phenomena 38 , 57 – 60 . First introduced by Giustolisi and Savic 61 , EPR operates through a two-step process that constructs symbolic models by determining structure and refining relationships between inputs and outputs 62 .…”

Section: Methodological Backgroundmentioning

confidence: 99%

A formulation for asphalt concrete air void during service life by adopting a hybrid evolutionary polynomial regression and multi-gene genetic programming

Ghanizadeh,

Amlashi,

Bahrami

et al. 2024

Sci Rep

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Bitumen, aggregate, and air void (VA) are the three primary ingredients of asphalt concrete. VA changes over time as a function of four factors: traffic loads and repetitions, environmental regimes, compaction, and asphalt mix composition. Due to the high as-constructed VA content of the material, it is expected that VA will reduce over time, causing rutting during initial traffic periods. Eventually, the material will undergo shear flow when it reaches its densest state with optimum aggregate interlock or refusal VA content. Therefore, to ensure the quality of construction, VA in asphalt mixture need to be modeled throughout the service life. This study aims to implement a hybrid evolutionary polynomial regression (EPR) combined with a teaching–learning based optimization (TLBO) algorithm and multi-gene genetic programming (MGGP) to predict the VA percentage of asphalt mixture during the service life. For this purpose, 324 data records of VA were collected from the literature. The variables selected as inputs were original as-constructed VA, $${VA}_{orig}$$ VA orig (%); mean annual air temperature, $$MAAT$$ MAAT (°F); original viscosity at 77 °F, $${\eta }_{orig,77}$$ η o r i g , 77 (Mega-Poises); and $$time$$ time (months). EPR-TLBO was found to be superior to MGGP and existing empirical models due to the interquartile ranges of absolute error boxes equal to 0.67%. EPR-TLBO had an R2 value of more than 0.90 in both the training and testing phases, and only less than 20% of the records were predicted utilizing this model with more than 20% deviation from the observed values. As determined by the sensitivity analysis, $${\eta }_{orig,77}$$ η o r i g , 77 is the most significant of the four input variables, while time is the least one. A parametric study showed that regardless of $$MAAT$$ MAAT , $${\eta }_{orig,77},$$ η o r i g , 77 , of 0.3 Mega-Poises, and $${VA}_{orig}$$ VA orig above 6% can be ideal for improving the pavement service life. It was also witnessed that with an increase of $$MAAT$$ MAAT from 37 to 75 °F, the serviceability of asphalt concrete takes 15 months less on average.

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Section: Methodological Backgroundmentioning

confidence: 99%

A formulation for asphalt concrete air void during service life by adopting a hybrid evolutionary polynomial regression and multi-gene genetic programming

Ghanizadeh,

Amlashi,

Bahrami

et al. 2024

Sci Rep

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show abstract

“…On the other hand, MLR proved to have advantages such as simplified calculations, efficient descriptive power, and convenience in usability [ 32 ]. These computational methods (MLR and ANNs) for forecasting and developing estimation models have been found useful in the prediction and assessment of soil erosion and erodibility [ 20 , 21 , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of soil erodibility in Peninsular Malaysia: A case study using multiple linear regression and artificial neural networks

Rehman,

Abd Rahman,

Ibrahim

et al. 2024

Heliyon

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Data Utilization and Partitioning for Machine Learning Applications in Civil Engineering

Ebid,

Deifalla,

Onyelowe

2024

Sustainable Civil Infrastructures

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Predictive models of volumetric stability (durability) and erodibility of lateritic soil treated with different nanotextured bio-ashes with application of loss of strength on immersion; GP, ANN and EPR performance study

Cited by 10 publications

References 32 publications

A formulation for asphalt concrete air void during service life by adopting a hybrid evolutionary polynomial regression and multi-gene genetic programming

A formulation for asphalt concrete air void during service life by adopting a hybrid evolutionary polynomial regression and multi-gene genetic programming

Estimation of soil erodibility in Peninsular Malaysia: A case study using multiple linear regression and artificial neural networks

Data Utilization and Partitioning for Machine Learning Applications in Civil Engineering

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