Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Innovations in Concrete and Construction Materials · Additive Manufacturing and 3D Printing Technologies · Innovative concrete reinforcement materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
DITEN Department, University of Genoa, 16145 Genoa, Italy
School of Civil Engineering, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Department of Civil, Chemical, Environmental and Materials Engineering, University of Bologna, 40136 Bologna, Italy
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