Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning

被引：28
|
作者：
Izadgoshasb, Hamed [1 ]
Kandiri, Amirreza [2 ]
Shakor, Pshtiwan [3 ]
Laghi, Vittoria [4 ]
Gasparini, Giada [4 ]
机构：
[1] Univ Genoa, DITEN Dept, I-16145 Genoa, Italy
[2] Univ Coll Dublin, Sch Civil Engn, Dublin D04 V1W8, Ireland
[3] Univ Technol Sydney, Sch Civil & Environm Engn, Ultimo, NSW 2007, Australia
[4] Univ Bologna, Dept Civil Chem Environm & Mat Engn, I-40136 Bologna, Italy
来源：
APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 22期
关键词：
multi-objective optimization; artificial neural network; compressive strength; 3DP mortar; additive manufacturing; FLY-ASH; CEMENTITIOUS MATERIALS; MIX DESIGN; CONCRETE; PERFORMANCE; MIXTURES; TAILINGS;
D O I：
10.3390/app112210826
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
Machine learning is the discipline of learning commands in the computer machine to predict and expect the results of real application and is currently the most promising simulation in artificial intelligence. This paper aims at using different algorithms to calculate and predict the compressive strength of extrusion 3DP concrete (cement mortar). The investigation is carried out using multi-objective grasshopper optimization algorithm (MOGOA) and artificial neural network (ANN). Given that the accuracy of a machine learning method depends on the number of data records, and for concrete 3D printing, this number is limited to few years of study, this work develops a new method by combining both methodologies into an ANNMOGOA approach to predict the compressive strength of 3D-printed concrete. Some promising results in the iteration process are achieved.
引用
收藏
页数：22
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