Application of artificial intelligence models to predict the compressive strength of concrete

• Published in: Advances in computational intelligence Vol. 4; no. 2; p. 4
• Main Authors: de Andrade Cruvinel, Lucas Elias, Pereira, Wanderlei Malaquias, de Campos, Amanda Isabela, Espíndola, Rogério Pinto, Sarmento, Antover Panazzolo, de Lima Araújo, Daniel, de Assis Costa, Gustavo, Dutra, Roberto Viegas
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Compressive strength; Computational Intelligence; Concrete; Datasets; Design; Engineering; Machine Learning; Measurement techniques; Mixtures; Original Article; Problem solving; Proportioning (mixing); Reinforced concrete; Variables; Trees; Regressions; Prediction; Concrete; Compressive strength; Artificial intelligence; Ensembles; Mix proportioning
• ISSN: 2730-7794; 2730-7808
• DOI: 10.1007/s43674-024-00072-8

The concrete mixture design and mix proportioning procedure, along with its influence on the compressive strength of concrete, is a well-known problem in civil engineering that requires the execution of numerous tests. With the emergence of modern machine learning techniques, the possibility of automating this process has become a reality. However, a significant volume of data is necessary to take advantage of existing models and algorithms. Recent literature presents different datasets, each with its own unique details, for training their models. In this paper, we integrated some of these existing datasets to improve training and, consequently, the models' results. Therefore, using this new dataset, we tested various models for the prediction task. The resulting dataset comprises 2358 records with seven input variables related to the mixture design, while the output represents the compressive strength of concrete. The dataset was subjected to several pre-processing techniques, and afterward, machine learning models, such as regressions, trees, and ensembles, were used to estimate the compressive strength. Some of these methods proved satisfactory for the prediction problem, with the best models achieving a coefficient of determination ( R 2 ) above 80%. Furthermore, a website with the trained model was created, allowing professionals in the field to utilize the AI technique in their everyday problem-solving.