Prediction of the shear modulus of municipal solid waste (MSW): An application of machine learning techniques
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- @Article{ALIDOUST:2021:JCP,
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author = "Pourya Alidoust and Mohsen Keramati and
Pouria Hamidian and Amir Tavana Amlashi and
Mahsa Modiri Gharehveran and Ali Behnood",
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title = "Prediction of the shear modulus of municipal solid
waste ({MSW):} An application of machine learning
techniques",
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journal = "Journal of Cleaner Production",
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volume = "303",
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pages = "127053",
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year = "2021",
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ISSN = "0959-6526",
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DOI = "doi:10.1016/j.jclepro.2021.127053",
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URL = "https://www.sciencedirect.com/science/article/pii/S0959652621012725",
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keywords = "genetic algorithms, genetic programming, Municipal
solid waste, Cyclic triaxial test, Shear modulus,
Artificial neural network (ANN), Multivariate adaptive
regression splines (MARS), Multi-gene genetic
programming (MGGP), M5 model tree (M5Tree)",
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abstract = "The dynamic properties of Municipal Solid Waste (MSW)
are site-specific and need to be evaluated separately
in different regions. The laboratory-based evaluation
of MSW has difficulties such as an unpleasant aroma or
degradability of MSW, making the testing procedure
unfavorable. Moreover, these evaluations are time- and
cost-intensive, which may also require trained
personnel to conduct the tests. To address this
concern, alternatively, the shear modulus of MSW can be
estimated through some predictive models. In this
study, the shear modulus was evaluated using 153 cyclic
triaxial tests. For this purpose, the effects of
various factors, including the shear strain (ShS), age
of the MSW (Age), percentage of plastic (POP),
confining pressure (CP), unit weight (UW), and loading
frequency (F) on the shear modulus of MSW were
evaluated. The data obtained through laboratory
experiments was then employed to model the dynamic
response of MSW using four different machine learning
techniques including Artificial Neural Networks (ANN),
Multivariate Adaptive Regression Splines (MARS),
Multi-Gene Genetic Programming (MGGP), and M5 model
Tree (M5Tree). A comparison of the performance of
developed models indicated that the ANN model
outperformed the other models. More specifically, for
ANN, MARS, MGGP, and M5Tree models, the corresponding
values of R-squared equal to 0.9897, 0.9640, 0.9617,
and 0.8482 for the training dataset, while the values
for the testing dataset for ANN, MARS, MGGP, and M5Tree
are 0.9812, 0.9551, 0.9574, and 0.8745. Furthermore,
although the developed models using MARS and MGGP
techniques resulted in more errors compared to the ANN
technique, they were found to produce reliable
predictions. To further compare the performance and
efficiency of the developed models and study the
effects of each input variable on the output variable
(i.e., shear modulus), model validity, parametric
study, and sensitivity analysis were performed",
- }
Genetic Programming entries for
Pourya Alidoust
Mohsen Keramati
Pouria Hamidian
Amir Tavana Amlashi
Mahsa Modiri Gharehveran
Ali Behnood
Citations