Extensive database of MSW shear strength parameters obtained from laboratorial direct shear tests: Proposal for data classification.

Leonardo Vinícius Paixão Daciolo, Natalia de Souza Correia, Maria Eugenia Gimenez Boscov

Waste management (New York, N.Y.) 2022 Mar 01

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For the design of Municipal Solid Waste (MSW) landfills, especially in the initial stages of the project, shear strength parameters are often selected from published results or literature recommendations and results of published tests. However, when adopting MSW shear strength parameters of MSW in the literature, a great variability is present, associated with testing procedures and intrinsic regional differences in the samples. Despite the lack of standardization of observations in the literature, statistical techniques results can help identify the main factors that affect this variability and categorize the observations for better inference of the results. This research gathered 313 observations of laboratory direct shear tests results presented in 30 international published researches, addressing results from different countries and testing configurations, in order to assess data statistical behavior and propose a classification. Results showed the factors that most contribute to the observational divergences, being the main factor associated with the mechanical-morphological behavior of waste components. A reorganization of data into classes (A, B and C) according to waste compressible, incompressible and reinforcement compositions was carried out in order to group shear strength parameters in a ternary diagram. The classification of shear strength envelopes for each proposed class and different strain levels enabled the verification of a hardening behavior of MSW and a prediction of mechanical parameters. Copyright © 2021 Elsevier Ltd. All rights reserved.

Citation

Leonardo Vinícius Paixão Daciolo, Natalia de Souza Correia, Maria Eugenia Gimenez Boscov. Extensive database of MSW shear strength parameters obtained from laboratorial direct shear tests: Proposal for data classification. Waste management (New York, N.Y.). 2022 Mar 01;140:245-259