Prediction of the shear modulus of municipal solid waste (MSW): An application of machine learning techniques

Alidoust, Pourya; Keramati, Mohsen; Hamidian, Pouria; Amlashi, Amir Tavana; Gharehveran, Mahsa Modiri; Behnood, Ali

doi:10.1016/j.jclepro.2021.127053

Cited by 38 publications

(13 citation statements)

References 33 publications

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“…The complex, nonlinear relationships in Figure 2, most of which are not amenable to explicit analytical forms, can be empirically simulated with multilayer feed-forward neural networks (NNs). 38 As a universal function approximator, 39 NN is a powerful computation tool that has been successfully applied to predict MSW generation, 40 properties, 41 and management. 13 In a typical feed-forward NN model with multiple hidden layers, input data propagate through the entire network after a certain linear combination and nonlinear (sigmoid) activation at each node (or neuron) in the subsequent layers.…”

Section: Hybrid Machine Learning Modelmentioning

confidence: 99%

A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems

He,

Small,

Scott

et al. 2023

Environ. Sci. Technol.

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Sustainability challenges, such as solid waste management, are usually scientifically complex and data scarce, which makes them not amenable to science-based analytical forms or data-intensive learning paradigms. Deep integration between data science and sustainability science in highly complementary manners offers new opportunities for tackling these conundrums. This study develops a novel hybrid neural network (HNN) model that imposes the holistic decision-making context of solid waste management systems (SWMS) on a traditional neural network (NN) architecture. Equipped with adaptable hybridization designs of hand-crafted model structure, constrained or predetermined parameters, and a customized loss function, the HNN model is capable of learning various technical, economic, and social aspects of SWMS from a small and heterogeneous data set. In comparison, the versatile HNN model not only outperforms traditional NN models in convergence rates, which leads to a 22% lower mean testing error of 0.20, but also offers superior interpretability. The HNN model is capable of generating insights into the enabling factors, policy interventions, and driving forces of SWMS, laying a solid foundation for data-driven decision making.

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Section: Hybrid Machine Learning Modelmentioning

confidence: 99%

A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems

He,

Small,

Scott

et al. 2023

Environ. Sci. Technol.

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“…Othman 30 also used ANNs to determine key Marshall mix design parameters, highlighting the diverse applications of ANNs in optimizing asphalt mixture designs and improving material property predictions. Due to the black-box nature of ANN and its complex calculations which usually cannot be performed manually, engineers are always in need of new solutions that provide straightforward equations to predict unidentified characteristics and make it easy to predict the outcome of projects by placing new measured values 31 , 32 . As a result, symbolic regression methods like evolutionary polynomial regression (EPR) and multi-gene genetic programming (MGGP) have become very prominent and widely employed in recent years.…”

Section: Introductionmentioning

confidence: 99%

A formulation for asphalt concrete air void during service life by adopting a hybrid evolutionary polynomial regression and multi-gene genetic programming

Ghanizadeh,

Amlashi,

Bahrami

et al. 2024

Sci Rep

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Bitumen, aggregate, and air void (VA) are the three primary ingredients of asphalt concrete. VA changes over time as a function of four factors: traffic loads and repetitions, environmental regimes, compaction, and asphalt mix composition. Due to the high as-constructed VA content of the material, it is expected that VA will reduce over time, causing rutting during initial traffic periods. Eventually, the material will undergo shear flow when it reaches its densest state with optimum aggregate interlock or refusal VA content. Therefore, to ensure the quality of construction, VA in asphalt mixture need to be modeled throughout the service life. This study aims to implement a hybrid evolutionary polynomial regression (EPR) combined with a teaching–learning based optimization (TLBO) algorithm and multi-gene genetic programming (MGGP) to predict the VA percentage of asphalt mixture during the service life. For this purpose, 324 data records of VA were collected from the literature. The variables selected as inputs were original as-constructed VA, $${VA}_{orig}$$ VA orig (%); mean annual air temperature, $$MAAT$$ MAAT (°F); original viscosity at 77 °F, $${\eta }_{orig,77}$$ η o r i g , 77 (Mega-Poises); and $$time$$ time (months). EPR-TLBO was found to be superior to MGGP and existing empirical models due to the interquartile ranges of absolute error boxes equal to 0.67%. EPR-TLBO had an R2 value of more than 0.90 in both the training and testing phases, and only less than 20% of the records were predicted utilizing this model with more than 20% deviation from the observed values. As determined by the sensitivity analysis, $${\eta }_{orig,77}$$ η o r i g , 77 is the most significant of the four input variables, while time is the least one. A parametric study showed that regardless of $$MAAT$$ MAAT , $${\eta }_{orig,77},$$ η o r i g , 77 , of 0.3 Mega-Poises, and $${VA}_{orig}$$ VA orig above 6% can be ideal for improving the pavement service life. It was also witnessed that with an increase of $$MAAT$$ MAAT from 37 to 75 °F, the serviceability of asphalt concrete takes 15 months less on average.

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“…Researchers showed that the composition of the MSW can greatly impacts its behavior [22][23][24][25]. Researches on the waste behavioral model consider waste as a soil that has a combination of plastic materials and are not considered as a single material [14,[26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Constitutive modeling of a municipal solid waste considering the effects of creep and temperature using composite theory

2023

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In this paper, a constitutive model for the stress-strain response of municipal solid waste (MSW) is presented, considering the effects of temperature and aging. Our goal is to provide a model for predicting the behavior of these materials. The models used to predict the behavior of waste have used the theory of soils, and in this research, a composite based model has been developed. A Cylindrical sample is modeled and loaded under triaxial loading conditions as observed in the experimental specimens. A fresh MSW, as our sample was investigated with specific composition. The constant of the relation between stress-strain was calculated using optimization technique. The obtained results are compared with previous models' experimental data and results to verify the proposed model. At the lower strains, a good agreement can be reported between the numerical and experimental results. The root mean square percentage of the relative error between the present model and the model of other researchers has shown acceptable results. It is also revealed that, as the temperature of the MSW increases, the stress on the waste is increased.

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Prediction of the shear modulus of municipal solid waste (MSW): An application of machine learning techniques

Cited by 38 publications

References 33 publications

A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems

A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems

A formulation for asphalt concrete air void during service life by adopting a hybrid evolutionary polynomial regression and multi-gene genetic programming

Constitutive modeling of a municipal solid waste considering the effects of creep and temperature using composite theory

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