Effective Hybrid Soft Computing Approach for Optimum Design of Shallow Foundations

Mohammad Khajehzadeh, Suraparb Keawsawasvong and Moncef L. Nehdi
Additional contact information
Mohammad Khajehzadeh: Department of Civil Engineering, Anar Branch, Islamic Azad University, Anar 7741943615, Iran
Suraparb Keawsawasvong: Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani 12120, Thailand
Moncef L. Nehdi: Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4M6, Canada

Sustainability, 2022, vol. 14, issue 3, 1-20

Abstract: In this study, an effective intelligent system based on artificial neural networks (ANNs) and a modified rat swarm optimizer (MRSO) was developed to predict the ultimate bearing capacity of shallow foundations and their optimum design using the predicted bearing capacity value. To provide the neural network with adequate training and testing data, an extensive literature review was used to compile a database comprising 97 datasets retrieved from load tests both on large-scale and smaller-scale sized footings. To refine the network architecture, several trial and error experiments were performed using various numbers of neurons in the hidden layer. Accordingly, the optimal architecture of the ANN was 5 × 10 × 1. The performance and prediction capacity of the developed model were appraised using the root mean square error (RMSE) and correlation coefficient (R). According to the obtained results, the ANN model with a RMSE value equal to 0.0249 and R value equal to 0.9908 was a reliable, simple and valid computational model for estimating the load bearing capacity of footings. The developed ANN model was applied to a case study of spread footing optimization, and the results revealed that the proposed model is competent to provide better optimal solutions and to outperform traditional existing methods.

Keywords: neural network; rat swarm; spread footing; optimization; bearing capacity (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/3/1847/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/3/1847/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:3:p:1847-:d:743017

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().