The compression ratio of municipal solid waste (MSW) is an essential parameter for evaluation of waste settlement and landfill design. However, no appropriate model has been proposed to estimate the waste compression ratio so far. In this study, a decision tree method was utilized to predict the waste compression ratio (C'c). The tree was constructed using Quinlan's M5 algorithm. A reliable database retrieved from the literature was used to develop a practical model that relates C'c to waste composition and properties, including dry density, dry weight water content, and percentage of biodegradable organic waste using the decision tree method. The performance of the developed model was examined in terms of different statistical criteria, including correlation coefficient, root mean squared error, mean absolute error and mean bias error, recommended by researchers. The obtained results demonstrate that the suggested model is able to evaluate the compression ratio of MSW effectively.

The most common apparatus used to investigate the load−deformation parameters of homogeneous fine-grained soils is a Casagrande-type oedometer. A typical Casagrande oedometer cell has an internal diameter of 76 mm and a height of 19 mm. However, the dimensions of this kind of apparatus do not meet the requirements of some civil engineering applications like studying load−deformation characteristics of specimens with large-diameter particles such as granular materials or municipal solid waste materials. Therefore, it is decided to design and develop a large-scale oedometer with an internal diameter of 490 mm. The new apparatus provides the possibility to evaluate the load−deformation characteristics of soil specimens with different diameter to height ratios. The designed apparatus is able to measure the coefficient of lateral earth pressure at rest. The details and capabilities of the developed oedometer are provided and discussed. To study the performance and efficiency, a number of consolidation tests were performed on Firoozkoh No. 161 sand using the newly developed large scale oedometer made and also the 50 mm diameter Casagrande oedometer. Benchmark test results show that measured consolidation parameters by large scale oedometer are comparable to values measured by Casagrande type oedometer.

<p>The compression ratio of Municipal Solid Waste (MSW) is an essential parameter for evaluation of waste settlement. Since it is relatively time-consuming to determine compression ratio from oedometer tests and there exist difficulties associated with working on waste materials, it will be useful to develop models based on waste physical properties. Therefore, present research attempts to develop proper prediction models using ANFIS and ANN models. The compression ratio was modeled as a function of the physical properties of waste including dry unit weight, water content, and biodegradable organic content. A reliable experimental database of oedometer tests, taken from the literature, was employed to train and test the ANN and ANFIS models. The performance of the developed models was investigated according to different statistical criteria (i.e. correlation coefficient, root mean squared error, and mean absolute error) recommended by researchers. The final models have demonstrated the correlation coefficients higher than 90% and low error values; so, they have capability for acceptable prediction of municipal solid waste compression ratio. Furthermore, the values of performance measures obtained for ANN and ANFIS models indicate that the ANFIS model performs better than ANN model.</p><p> </p><p><strong>Resumen</strong></p><p>El índice de compresión de residuos sólidos es un parámetro esencial para la evaluación del asentamiento de un basurero municipal. Debido al desgaste de tiempo para determinar el índice de compresión a partir de pruebas edométricas y debido a las dificultades asociadas al trabajo con materiales desechados es necesario desarrollar modelos basados en las propiedades físicas de los desechos solidos. Además, la presente investigación pretende  desarrollar modelos de predicción apropiados a partir de los esquemas ANFIS y ANN. El índice de comprensión se modeló como una función de propiedades físicas de desechos que incluyen el peso seco de una unidad, el contenido de agua y el contenido orgánico biodegradable. De la literatura se tomó una base de datos confiable de pruebas edométricas experimentales que fue empleada para preparar y evaluar los modelos ANFIS y ANN. El desempeño de los modelos desarrollados fue investigado de acuerdo con diferentes criterios estadísticos (por ejemplo, el coeficiente de correlación, el error cuadrático medio y el error medio absoluto) recomendados por investigadores. Los modelos finales han demostrado coeficientes de correlación mayores al 90 por ciento y valores bajos de error. Esto significa que estos modelos tienen una capacidad de predicción aceptable para el índice de comprensión del basurero municipal. Además, los valores de las medidas de desempeño obtenidos para los modelos ANFIS y ANN indican que el modelo ANFIS tiene una mayor asertividad que el modelo ANN.</p><p><strong><br /></strong></p>

Mineral tailing deposits are one of the most important issues in the field of geotechnical engineering. The void ratio of mineral tailings is an essential parameter for investigating the geotechnical behavior of tailings. However, there has not yet been a comprehensive empirical formulation for initial prediction of the void ratio of mineral tailings. In this study, the void ratio of various types of mineral waste is estimated by using gene expression programming (GEP). Therefore, taking into consideration the effective physical parameters that affect the estimation of this parameter, eight different models are presented. A reliable experimental database collected from different sources in the literature was applied to develop the GEP models. The performance of the developed GEP models was measured based on coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). According to the results, the model with effective stress σ ′ , initial void ratio (e0), and parameters of R2 = 0.92, MAE = 0.109, and RMSE = 0.180 performed the best. Finally, a new empirical formulation for the initial prediction of the void ratio parameter is proposed based on the aforementioned analyses.

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