Prediction of Compaction Characteristics of Soils from Index Test’s Results

Karimpour-Fard, Mehran; Machado, Sandro Lemos; Falamaki, Amin; Carvalho, Miriam Fatima; Tizpa, Parichehr

doi:10.1007/s40996-018-0161-9

Cited by 18 publications

(10 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Omar et al [6], among the three parameters (specific gravity, fine content, and liquid limit) that have been used in the correlation, the specific gravity has a greater influence on MDD. The significant effect of the specific gravity was confirmed in the study carried out by Di Matteo et al [5], Karimpour-Fard et al [4], and Bera and Ghosh [3] using 4, 4, and 5 soil parameters, respectively, on the MDD predictions.…”

Section: Introductionmentioning

confidence: 68%

“…Several studies have correlated MDD with numerous index soil properties and compaction parameters. For example, the MDD was correlated to: (1) A combination of six parameters in Gunaydın [2] using liquid limit, plastic limit, specific gravity, fine content, gravel content, and sand content; (2) A group of five parameters in Bera and Ghosh [3] using compaction energy, specific gravity, liquid limit, plastic limit, and grain size; (3) A combination of four parameters in Karimpour-Fard et al [4] using optimum moisture content, saturation degree, specific gravity, and unit weight of water also in Di Matteo et al [5] using optimum moisture content, plastic limit, liquid limit, and specific gravity; (4) A group of three parameters in Omar et al [6] using specific gravity, fine content, and liquid limit, Farooq et al [7] using liquid limit, plasticity index, and compaction effort;…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Maximum dry density of sand–kaolin mixtures predicted by using fine content and specific gravity

Alshameri

2020

SN Appl. Sci.

View full text Add to dashboard Cite

Numerous studies have covered the comparison of maximum dry density (MDD) toward the combination of soil properties and compaction parameters. These combinations are dominated by Atterberg limits, moisture content, and compaction effort. Nevertheless, minimal efforts have been expended in predicting the MDD using two simple soil properties, i.e., fine content (FC) and specific gravity (G s ). Sand-kaolin mixtures were compacted and then used to examine the correlation between MDD, FC, and G s through different moisture content, and FC ranged from 20 to 70%. The results showed the importance of considering both G s and FC in the correlation estimation in order to reduce the uncertainty of using only FC as a single parameter. A linear relationship was obtained with an excellent correlation coefficient (R 2 = 0.99) between MDD and the ratio of FC/G s 2 . The empirical correlation equation was justified using data from 163 measured MDD obtained from previous studies. All predicted MDD were within the confidence interval of ± 10, which confirmed a low error range of the predicted MDD. The application of the equation was limited to FC and FC/G s less than 77% and 0.302, respectively. It should, however, be noted that the presence of a high amount of gravel in the soil mixture will not be applicable for this correlation.

show abstract

Section: Introductionmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Maximum dry density of sand–kaolin mixtures predicted by using fine content and specific gravity

Alshameri

2020

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…Mean and standard deviation for MoPesm and laboratory tests were similar. MoPesm predictions had a MAPE of 2,57%, a RMSE of 0,585, a R of 0,873, and a R 2 of 0,763, a very acceptable performance according to Wang and Yin (2020), to whom a high R 2 and low MAPE and RMSE indicate that a model has a higher precision, and to Karimpour-Fard et al (2019), who considered a R 2 above 0,70 as acceptable, labeling such model as "strong".…”

Section: Resultsmentioning

confidence: 98%

“…The compaction parameters -optimum moisture content (OMC) and maximum dry unit weight (γ d, max ) -are determined through very time-consuming laboratory tests. This makes prediction models that are based on easily measurable soil properties (such as those described above) highly desirable and remarkably useful (Karimpour-Fard et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Regarding soils from Brazil, a study by Karimpour-Fard et al (2019) analyzed data from 728 sets of granular and finegrained soils, most from 20 literature sources, and 227 of their own, collected in Salvador metropolitan region (State of Bahia, northeastern Brazil). Their approach involved analyses using MLR and ANNs, and the results demonstrated that the ANN model could predict compaction parameters with a zero average error, but it required a lot more of processing time, thus being unsuitable for situations where prompt decision making is mandatory.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Empirical Models to Predict Compaction Parameters for Soils in the State of Ceará, Northeastern Brazil

et al. 2021

View full text Add to dashboard Cite

This work developed prediction models for maximum dry unit weight (γd,max) and optimum moisture content (OMC) for compacted soils in Ceará, Brazil, ba M Winnie the Pooh sed on index and physical properties. The methodology included data from soils used in the construction of 15 dams in Ceará, with available information regarding laboratory tests of interest. Correlations were developed using non-linear regression, from 169 laboratory results (83 for training and 86 for validating the models), which presented a R2 of 0,763 for MoPesm (prediction model for γd,max) and 0,761 for MoTuo (model for OMC). A posteriori, the same physical indexes used to train and validate MoPesm and MoTuo were used as inputs of other prediction models available in the literature, whose outputs differed considerably from laboratory results for the evaluated soils. MoPesm and MoTuo were able to satisfactorily predict compaction parameters, with outputs close to those obtained in the laboratory for tested soil samples. Their performance justifies their use for predicting compaction parameters in geotechnical structures that use compacted soils when there are financial restraints, short timeframes, or unavailability of test equipment, particularly in early design stages and preliminary studies, before appropriate soil sampling and field investigation can be conducted, thus saving substantial time and financial resources.

show abstract

Prediction of Compaction Parameters Based on the Atterberg Limit by Using a Machine Learning Approach

Kassa,

Wubineh,

Geremew

et al. 2024

EAI/Springer Innovations in Communication and Computing

View full text Add to dashboard Cite

Prediction of Compaction Characteristics of Soils from Index Test’s Results

Cited by 18 publications

References 38 publications

Maximum dry density of sand–kaolin mixtures predicted by using fine content and specific gravity

Maximum dry density of sand–kaolin mixtures predicted by using fine content and specific gravity

Empirical Models to Predict Compaction Parameters for Soils in the State of Ceará, Northeastern Brazil

Prediction of Compaction Parameters Based on the Atterberg Limit by Using a Machine Learning Approach

Contact Info

Product

Resources

About