Izvorni znanstveni članak
https://doi.org/10.17794/rgn.2021.5.4
APPLICATION OF SOFT COMPUTING METHODOLOGIES TO PREDICT THE 28-DAY COMPRESSIVE STRENGTH OF SHOTCRETE: A COMPARATIVE STUDY OF INDIVIDUAL AND HYBRID MODELS
Mahtab Torkan
; Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
Hamid Kalhori
; Department of Mining Engineering, Isfahan University of Technology, Isfahan 8415683111, Iran
Mohammad Hossein Jalalian
; Department of Mining Engineering, Isfahan University of Technology, Isfahan 8415683111, Iran
Sažetak
Shotcreting is a popular construction technique with wide-ranging applications in mining and civil engineering. Compressive strength is a primary mechanical property of shotcrete with particular importance for project safety, which highly depends on its mix design. But in practice, there is no reliable and accurate method to predict this strength. In this study, existing experimental data related to shotcretes with 59 different mix designs are used to develop a series of soft computing methodologies, including individual artificial neural network, support vector regression, and M5P model tree and their hybrids with the fuzzy c-means clustering algorithm so as to predict the 28-day compressive strength of shotcrete. Analysis of the results shows the superiority of the hybrid model over the individual models in predicting the compressive strength of shotcrete. Overall, data clustering prior to use of machine learning techniques leads to certain improvement in their performance and reliability and generalizability of their results. In particular, the M5P model tree exhibits excellent capability in anticipating the compressive strength of shotcrete.
Ključne riječi
Shotcrete; Compressive Strength; Machine learning techniques; Hybrid model
Hrčak ID:
266292
URI
Datum izdavanja:
24.11.2021.
Posjeta: 680 *