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Frontiers of Structural and Civil Engineering >> 2023, Volume 18, Issue 1 doi: 10.1007/s11709-024-1041-y
A comprehensive comparison of different regression techniques and nature-inspired optimization algorithms to predict carbonation depth of recycled aggregate concrete
1. Department of Civil and Environmental Engineering, Politecnico Di Milano, Milano 20133, Italy;2. Leibniz Institute of Ecological Urban and Regional Development (IOER), Dresden 01217, Germany;3. Universidad Politécnica de Madrid–ETSI Minas y Energía, Madrid 28003, Spain;4. Department of Civil Engineering, KU Leuven Campus, Brugge 8200, Belgium;5. School of Resources and Safety Engineering, Central South University, Changsha 410083, China;3. Universidad Politécnica de Madrid–ETSI Minas y Energía, Madrid 28003, Spain
Abstract
Keywords
recycled aggregate concrete ; carbonation depth ; nature-inspired optimization algorithms ; extreme gradient boosting technique ; parametric analysis
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