Application of Genetic Programming for Uniaxial and Multiaxial Modeling of Concrete

Babanajad, Saeed

doi:10.1007/978-3-319-20883-1_16

Cited by 2 publications

(3 citation statements)

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“…GP generates the solutions which are computer programs rather than binary strings. It could be accounted as a supervised machine learning technique while searching program space instead of a data space [7].…”

Section: Genetic Programming Methodsmentioning

confidence: 99%

“…During the mutation process, a terminal from a model is randomly chosen to be mutated, occasionally. If the randomly selected node is a function, depending on the type of mutation, a new function is assigned [7].…”

Section: Genetic Programming Methodsmentioning

confidence: 99%

“…These approaches allow for a more truthful description of the compressive strength, but the complexity to predefine the model complicate their use. With the development of machine learning methods, the application of such approaches to develop new models for concrete strength became interesting [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Comparative Study of Machine Learning Methods for Compressive Strength of Concrete

Silva¹,

Moita²

2019

World Congress on Civil, Structural, and Environmental Engineering

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This paper introduces a comparative study for the compressive strength of concrete by employing machine learning approaches such as Genetic Programming (GP) and Artificial Neural Network (ANN). The simulation of concrete strength is strongly needed to better understand its behaviours under different conditions and loads. Since many studies predict the comprehensive strength of conventional concrete from hardened characteristics, based on the data points gathered from different experimental tests, empirical models have been developed and verified in the past years. Proposed models are more reliable if the numbers of tests increase and their repeatability increase as well. However, these models are designed for a specific range of concrete strengths. On the other hand, numerical models are more reliable since they are devised based on theoretical rules which could consider behaviours of concrete under different loading paths. But, the validation of these models is made by different loading paths with different configurations that result in costly experiments and both models use only principal stresses and strains in their formulation. Employing machine learning approaches instead of traditional models makes it possible to develop a better understanding of the compressive strength of concrete. Hence, the focus of this paper is the application of machine learning process and their suitability to model concrete compressive strength compared with early models obtained from the literature and compared with some conventional approaches.

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Section: Genetic Programming Methodsmentioning

confidence: 99%

Section: Genetic Programming Methodsmentioning

confidence: 99%