A novel approach based on soft computing techniques for unconfined compression strength prediction of soil cement mixtures

Tinoco, Joaquim Agostinho Barbosa; Alberto, António; Venda, Paulo da; Correia, A. Gomes; Lemos, Luís

doi:10.1007/s00521-019-04399-z

Cited by 38 publications

(22 citation statements)

References 27 publications

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“…Generally, RMSE is more sensitive compared to MAE. This is because RMSE takes the square of the distance between the predicted and actual data into account [ 71 ]. Mohammed et al [ 72 ] have previously proven the efficiency and capacity of several predictive models with small MAE values, specifically ranging between 3 and 11.…”

Section: Resultsmentioning

confidence: 99%

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

et al. 2021

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Although free-cement-based alkali-activated paste, mortar, and concrete have been recognised as sustainable and environmental-friendly materials, a considerable amount of effort is still being channeled to ascertain the best binary or ternary binders that would satisfy the requirements of strength and durability as well as environmental aspects. In this study, the mechanical properties of alkali-activated mortar (AAM) made with binary binders, involving fly ash (FA) and granulated blast-furnace slag (GBFS) as well as bottle glass waste nano-silica powder (BGWNP), were opti-mised using both experimentally and optimisation modelling through three scenarios. In the first scenario, the addition of BGWNP varied from 5% to 20%, while FA and GBFS were kept constant (30:70). In the second and third scenarios, BGWNP (5–20%) was added as the partial replacement of FA and GBFS, separately. The results show that the combination of binary binders (FA and GBFS) and BGWNP increased AAM’s strength compared to that of the control mixture for all scenarios. In addition, the findings also demonstrated that the replacement of FA by BGWNP was the most significant, while the effect of GBFS replacement by BGWNP was less significant. In particular, the highest improvement in compressive strength was recorded when FA, GBFS, and BGWNP were 61.6%, 30%, and 8.4%, respectively. Furthermore, the results of ANOVA (p values < 0.0001 and high F-values) as well as several statistical validation methods (R > 0.9, RAE < 0.1, RSE < 0.013, and RRSE < 0.116) confirmed that all the models were robust, reliable, and significant. Similarly, the data variation was found to be less than 5%, and the difference between the predicted R2 and adj. R2 was very small (<0.2), thus confirming that the proposed non-linear quadratic equations had the capability to predict for further observation. In conclusion, the use of BGWNP in AAM could act as a beneficial and sustainable strategy, not only to address environmental issues (e.g., landfill) but to also enhance strength properties.

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Section: Resultsmentioning

confidence: 99%

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

et al. 2021

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“…The 2 ranges between zero and one, with an optimum value of one. Hence, the closer R 2 is to one, the better the prediction accuracy (Alzabeebee et al 2017;Tinoco et al 2019). 2 is calculated using Equation 16 (Mohammadzadeh et al, 2019).…”

Section: Criteria Considered To Evaluate the Accuracy Of The Moga-epr Model And The Analytical Methodsmentioning

confidence: 99%

Evolutionary computing to determine the skin friction capacity of piles embedded in clay and evaluation of the available analytical methods

Alzabeebee

Chapman

2020

Transportation Geotechnics

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Link to publication on Research at Birmingham portal General rightsUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law.• Users may freely distribute the URL that is used to identify this publication.• Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research.• User may use extracts from the document in line with the concept of 'fair dealing' under the Copyright, Designs and Patents Act 1988 (?) • Users may not further distribute the material nor use it for the purposes of commercial gain.Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.When citing, please reference the published version. Take down policyWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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“…In contrast, the GSA is a method that is applied after the training phase, which measures responses in any type of complex models when a given input is changed, allowing the quantification of the relative importance of each input parameter to the output parameter. [ 17 ] Therefore, GSA techniques are needed.…”

Section: Methodsmentioning

confidence: 99%

“…The global sensitivity analysis (GSA) is a method to quantitatively identify and prioritize the most influential input parameter, [ 15 ] which can be calculated by numerous methods, including Spearman's correlation coefficient (SCC), rank correlation coefficient (RCC), Latin hypercube sampling (LHS) with standardized rank regression coefficient index (LHS‐SRRC), [ 16 ] and others. [ 17 ] In recent years, GSA has been applied in corrosion science. Chen et al [ 18 ] obtained the major parameter influencing the corrosion rate of N80 steels, the pH value, by using RCC.…”

Section: Introductionmentioning

confidence: 99%

Global sensitivity analysis of influence parameters in pitting corrosion behavior of 304 stainless steel using adaptive neuro‐fuzzy inference systems

Wang

Yang

et al. 2020

Materials & Corrosion

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Pitting corrosion is simultaneously influenced by many parameters. However, conventional experimental methods do not offer information on the relative importance of each parameter to pitting corrosion. Herein, pitting corrosion of 304SS under multiple parameter influences was investigated by the global sensitivity analysis (GSA) based on adaptive neuro‐fuzzy inference system (ANFIS). It was applied for the first time in quantitatively analyzing the relative importance of each parameter to pitting corrosion. It was developed in two different systems, based on the experimental data and the published data, respectively. Both results show that ANFIS exhibits a highly accurate prediction result. The GSA based on ANFIS quantitatively analyzes the relative importance of the initiation of pitting corrosion of 304SS for each parameter, and based on the results of GSA, it can also be determined whether the parameter inhibits or promotes pitting corrosion. We believe this method could provide guidance for corrosion research.

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A novel approach based on soft computing techniques for unconfined compression strength prediction of soil cement mixtures

Cited by 38 publications

References 27 publications

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

Evolutionary computing to determine the skin friction capacity of piles embedded in clay and evaluation of the available analytical methods

Global sensitivity analysis of influence parameters in pitting corrosion behavior of 304 stainless steel using adaptive neuro‐fuzzy inference systems

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