Unconfined compressive strength prediction of recycled cement-treated base mixes using soft computing techniques

Chhabra, Rishi Singh; Mahadeva, Rajesh; Ransinchung, G.D.

doi:10.1080/14680629.2023.2199889

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…The degree of fit between regression results and true values is usually expressed by the statistical determination coefficient (also known as determination coefficient) of R 2 , which is specifically shown in equation (18). R 2 ranges from (0-1), and the closer the value is to 1, the better the fitting effect will be.…”

Section: Bp-ann Pso-bp-ann Ga-bp-ann Gsa-bp-ann Optimization Resultsmentioning

confidence: 99%

“…Optimizing ANN based on naturally inspired algorithms has been widely recognized, which can reduce the possibility of ANN models falling into local optima, improve generalization ability, and reduce computational speed, etc [16,17]. For instance, Chhabra et al [18] used Levenberg-Marquardt BP model and proportional conjugate gradient BP model to predict unconfined compressive strength values. Mahadeva et al [19] used ANN to accurately model, simulate, and analyze the performance of reverse osmosis desalination processes, among which the ANN model optimized by PSO was widely used.…”

Section: Literature Reviewmentioning

confidence: 99%

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Research on artificial neural networks to accurately predict element concentrations in nutrient solutions

Zhai

Dong

Liu

et al. 2023

Meas. Sci. Technol.

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Calcium, potassium, nitrogen, magnesium, and phosphorus, the main elements of the nutrient solution, are absorbed by plants and play an important role in plants. By measuring Ca2+, K+, Mg2+, NH4+, NO3−, HPO42−, the Artificial Neural Networks (ANNs) were used in this study to accurately calculate the concentrations of these elements. Firstly, the error sources of the calculating element concentration were analyzed based on the data of six-ion measurement experiments. Subsequently, various optimization algorithms were compared to optimize back propagation (BP) and radial basis function (RBF) ANNs. Finally, the results of mean relative errors (MREs) and recovery values show that ANNs can effectively reduce the measurement error of ion sensors. From the perspective of recovery values, the prediction error of all elements can be controlled within 15%. From the perspective of MRE, except for magnesium and phosphorus elements, the improved model prediction errors of other elements were also less than 10%.

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Section: Bp-ann Pso-bp-ann Ga-bp-ann Gsa-bp-ann Optimization Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Research on artificial neural networks to accurately predict element concentrations in nutrient solutions

Zhai

Dong

Liu

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

Assessment of Unconfined Compressive Strength of Stabilized Soil Using Artificial Intelligence Tools: A Scientometrics Review

Sari-Ahmed,

Ghrici,

Benzaamia

et al. 2024

Studies in Systems, Decision and Control

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A Scientometrics Review of Soil Properties Prediction Using Soft Computing Approaches

Khatti,

Grover

2023

Arch Computat Methods Eng

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Unconfined compressive strength prediction of recycled cement-treated base mixes using soft computing techniques

Cited by 4 publications

References 38 publications

Research on artificial neural networks to accurately predict element concentrations in nutrient solutions

Research on artificial neural networks to accurately predict element concentrations in nutrient solutions

Assessment of Unconfined Compressive Strength of Stabilized Soil Using Artificial Intelligence Tools: A Scientometrics Review

A Scientometrics Review of Soil Properties Prediction Using Soft Computing Approaches

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