Prediction of the shear modulus of municipal solid waste (MSW): An application of machine learning techniques-Reference-Cited by-同舟云学术

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

Published:2021-06 Issue: Volume:303 Page:127053
ISSN:0959-6526
Container-title:Journal of Cleaner Production
language:en
Short-container-title:Journal of Cleaner Production

Author:

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

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Strategy and Management,General Environmental Science,Renewable Energy, Sustainability and the Environment

Reference59 articles.

1. Forecasting municipal solid waste generation using artificial intelligence modelling approaches;Abbasi;Waste Manag.,2016

2. Dynamic Characteristics and Stability Analysis of Municipal Solid Waste in Bioreactor Landfills;Adil Haque,2007

3. Laboratory-based assessment on similarities between dynamic behavior of MSW and clay;Alidoust;J. Mater. Cycles Waste Manag.,2021

4. Laboratory studies on effect of fiber content on dynamic characteristics of municipal solid waste;Alidoust;Waste Manag.,2018

5. Application of computational intelligence and statistical approaches for auto-estimating the compressive strength of plastic concrete;Amlashi;Eur. J. Environ. Civ. Eng.,2020

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1. Application of hybridized ensemble learning and equilibrium optimization in estimating damping ratios of municipal solid waste;Scientific Reports;2024-07-30

2. A formulation for asphalt concrete air void during service life by adopting a hybrid evolutionary polynomial regression and multi-gene genetic programming;Scientific Reports;2024-06-10

3. Intelligent Prediction of Asphalt Concrete Air Voids during Service Life Using Cubist and GBRT-Ensemble Learning Approaches Hybridized with an Equilibrium Optimizer Algorithm;Journal of Materials in Civil Engineering;2024-05

4. Prediction and Feed-In Tariffs of Municipal Solid Waste Generation in Beijing: Based on a GRA-BiLSTM Model;Sustainability;2024-04-24

5. A comparison of novel hybrid ensemble learners to predict the compressive strength of green engineering materials: a case of concrete composed of rice husk ash;European Journal of Environmental and Civil Engineering;2024-04-08

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