Abstract

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.

Highlights

  • Every engineering work has its inherent risks, as countless uncertainties are embedded in all phases of its development and execution

  • 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 multilinear regression (MLR) and artificial neural networks (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, being unsuitable for situations where prompt decision making is mandatory

  • Some of the coefficients in Equation (1) were nullified at the end of the iterative process, which indicates that the corresponding variables had very little to no influence in determining the maximum dry unit weight and could be excluded

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Summary

Introduction

Every engineering work has its inherent risks, as countless uncertainties are embedded in all phases of its development and execution.

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Methods
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