Técnica de inteligencia artificial para la selección de concreto en construcciones residenciales. Una revisión sistemática

Luis Leonardo  Zambrano Salazar; Enma Katherine  Gamboa López

doi:10.59282/reincisol.V3(5)1490-1514

Authors

Luis Leonardo Zambrano Salazar Universidad Técnica de Ambato https://orcid.org/0009-0001-5966-8123
Enma Katherine Gamboa López Ministerio de Transporte y Obras Públicas del Ecuador

DOI:

https://doi.org/10.59282/reincisol.V3(5)1490-1514

Keywords:

quality, efficiency, computer science, safety, urbanism.

Abstract

The selection of construction materials in residential construction is key to ensure the durability and quality of the work, as well as the safety of the users. The selection process used to be based on rigorous tests that were costly, slow and inaccurate, but with technology and the advance of information technology, this process has improved; therefore, the objective of this research was to describe the existing artificial intelligence techniques for the selection of concrete used in residential construction. For this purpose, a review of 240 articles was carried out in databases such as Scopus, Scielo, Latindex and Google academic, on machine learning, deep learning, neural networks and Big data techniques, from which 24 of the most relevant articles were selected based on the inclusion and quality criteria. The results of the review show that selection methods based on artificial intelligence have been efficient for the evaluation of concrete quality, so they can be used for the selection of materials resistant to the most prevalent risks such as fires, earthquakes and hurricanes.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Afzal, M., Liu, Y., Cheng, J. C., & Gan, V. J. (2020). Reinforced concrete structural design optimization: A critical review. Journal of Cleaner Production, 260, 120623. Disponible en: https://doi.org/10.1016/j.jclepro.2020.120623

Ai, D., & Cheng, J. (2023). A deep learning approach for electromechanical impedance based concrete structural damage quantification using two-dimensional convolutional neural network. Mechanical Systems and Signal Processing, 183, 109634. Disponible en: https://doi.org/10.1016/j.ymssp.2022.109634

Akingbonmire, S. L., Afolayan, J. O., & Ikumapayi, C. M. (2021). Quality Assessment Of Reinforced Concrete Structural Elements Of Some Selected University Buildings Using Ultrasonic Pulse Velocity Test. Journal of Multidisciplinary Engineering Science Studies, 7(12), 4162-4169. Disponible en:https://www.jmess.org/wpcontent/uploads/2021/12/JMESSP13420815.pdf

Amini, M., & Memari, A. M. (2020). Review of literature on performance of coastal residential buildings under hurricane conditions and lessons learned. Journal of performance of constructed facilities, 34(6), 04020102. Disponible en: https://doi.org/10.1061/(asce)cf.1943-5509.0001509

Arbaoui, A., Ouahabi, A., Jacques, S., & Hamiane, M. (2021). Concrete cracks detection and monitoring using deep learning-based multiresolution analysis. Electronics, 10(15), 1772. Disponible en: https://doi.org/10.3390/electronics10151772

Archer, R., Choi, H., Vasconez, R., Najm, H., & Gong, J. (2023). Adaptive coastal construction: designing amphibious homes to resist hurricane winds and storm surges. Journal of Ocean Engineering and Marine Energy, 9(2), 273-290. Disponible en: https://doi.org/10.1007/s40722-022-00267-6

Asteris, P. G., & Mokos, V. G. (2020). Concrete compressive strength using artificial neural networks. Neural Computing and Applications, 32(15), 11807-11826. Disponible en: https://doi.org/10.1007/s00521-019-04663-2

Asteris, P. G., Skentou, A. D., Bardhan, A., Samui, P., & Pilakoutas, K. (2021). Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cement and Concrete Research, 145, 106449. Disponible en: https://doi.org/10.1016/j.cemconres.2021.106449

Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., ... & Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction, 141, 104440. Disponible en: https://doi.org/10.1016/j.autcon.2022.104440

Bojórquez, J., Ponce, S., Ruiz, S. E., Bojórquez, E., Reyes-Salazar, A., Barraza, M., ... & Baca, V. (2021). Structural reliability of reinforced concrete buildings under earthquakes and corrosion effects. Engineering Structures, 237, 112161. Disponible en: https://doi.org/10.1016/j.engstruct.2021.112161

Castaldo, P., Gino, D., Bertagnoli, G., & Mancini, G. (2020). Resistance model uncertainty in non-linear finite element analyses of cyclically loaded reinforced concrete systems. Engineering Structures, 211, 110496. Disponible en: https://doi.org/10.1016/j.engstruct.2020.110496

Crowley, H., Despotaki, V., Rodrigues, D., Silva, V., Toma-Danila, D., Riga, E., ... & Gamba, P. (2020). Exposure model for European seismic risk assessment. Earthquake Spectra, 36(1_suppl), 252-273. Disponible en: https://doi.org/10.1177/8755293020919429