TY - JOUR
T1 - Prediction and multi-objective optimization of mechanical, economical, and environmental properties for strain-hardening cementitious composites (SHCC) based on automated machine learning and metaheuristic algorithms
AU - Mahjoubi, Soroush
AU - Barhemat, Rojyar
AU - Guo, Pengwei
AU - Meng, Weina
AU - Bao, Yi
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/12/20
Y1 - 2021/12/20
N2 - This study develops a framework for property prediction and multi-objective optimization of strain-hardening cementitious composites (SHCC) based on automated machine learning. Three machine learning models are developed to predict the compressive strength, tensile strength, and ductility of SHCC. A tree-based pipeline optimization method is enhanced and used to enable automatic configuration of machine learning models, which are trained using three datasets considering 14 mix design variables and achieve reasonable prediction accuracy. With the predictive models, five objective functions are formulated for mechanical properties, life-cycle cost, and carbon footprint of SHCC, and the five objective functions are optimized in six design scenarios. The objective functions are optimized using innovative optimization and decision-making techniques (Unified Non-dominated Sorting Genetic Algorithm III and Technique for Order of Preference by Similarity to Ideal Solution). This research will promote efficient development and applications of high-performance SHCC in concrete and construction industry.
AB - This study develops a framework for property prediction and multi-objective optimization of strain-hardening cementitious composites (SHCC) based on automated machine learning. Three machine learning models are developed to predict the compressive strength, tensile strength, and ductility of SHCC. A tree-based pipeline optimization method is enhanced and used to enable automatic configuration of machine learning models, which are trained using three datasets considering 14 mix design variables and achieve reasonable prediction accuracy. With the predictive models, five objective functions are formulated for mechanical properties, life-cycle cost, and carbon footprint of SHCC, and the five objective functions are optimized in six design scenarios. The objective functions are optimized using innovative optimization and decision-making techniques (Unified Non-dominated Sorting Genetic Algorithm III and Technique for Order of Preference by Similarity to Ideal Solution). This research will promote efficient development and applications of high-performance SHCC in concrete and construction industry.
KW - Automated machine learning
KW - Carbon footprint
KW - Evolutionary algorithm
KW - Multi-objective optimization
KW - Strain-hardening cementitious composite (SHCC)
KW - Tree-based pipeline optimization
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U2 - 10.1016/j.jclepro.2021.129665
DO - 10.1016/j.jclepro.2021.129665
M3 - Article
AN - SCOPUS:85118844595
SN - 0959-6526
VL - 329
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 129665
ER -