Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

João M. P. Q. Delgado (Autor) FEUP; Fernando A. N. Silva (Autor) Outra; Rosely S. Cavalcanti (Autor) Outra; António Azevedo (Autor) FEUP; Ana Sofia Guimarães (Autor) FEUP; António G. Barbosa de Lima (Autor) Outra

Revista

Título: Buildings

Vol. 11

Páginas: 1-15

ISSN: 0007-3725

Editora: MDPI

Indexação

ISI Web of Knowledge - 0 Citações

ISI Web of Science

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00T-GC9

DOI: 10.3390/buildings11020044

Abstract (EN): The work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water-cement ratio, aggregate-cement ratio, age of testing, and percentage cement/metakaolin ratios (5% and 10%). We prepared 162 cylindrical concrete specimens with dimensions of 10 cm in diameter and 20 cm in height and 27 prismatic specimens with cross sections measuring 25 and 50 cm in length, with 9 different concrete mixture proportions. A longitudinal transducer with a frequency of 54 kHz was used to measure the ultrasonic velocities. An ANN model was developed, different ANN configurations were tested and compared to identify the best ANN model. Using this model, it was possible to assess the contribution of each input variable to the compressive strength of the tested concretes. The results indicate an excellent performance of the ANN model developed to predict compressive strength from the input parameters studied, with an average error less than 5%. Together, the water-cement ratio and the percentage of metakaolin were shown to be the most influential factors for the compressive strength value predicted by the developed ANN model.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15