Combining Artificial Neural Network and Seeker Optimization Algorithm for Predicting Compression Capacity of Concrete-Filled Steel Tube Columns

Author:

Hu Pan12,Aghajanirefah Hamidreza3,Anvari Arsalan4,Nehdi Moncef5ORCID

Affiliation:

1. School of Civil and Architectural Engineering, Technical University of Munich, 80333 Munich, Germany

2. Wuhan Municipal Construction Group Co., Ltd., Wuhan 430023, China

3. Department of Civil Engineering, Faculty of Engineering, Qazvin Branch Islamic Azad University, Qazvin 3419915195, Iran

4. Engineering and Management, Faculty of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran

5. Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada

Abstract

Accurate and reliable estimation of the axial compression capacity can assist engineers toward an efficient design of circular concrete-filled steel tube (CCFST) columns, which are gaining popularity in diverse structural applications. This study proposes a novel methodology based on computational intelligence for estimating the compression capacity of CCFST. Accordingly, a conventional artificial neural network (ANN) is hybridized with a metaheuristic algorithm called the seeker optimization algorithm (SOA). Utilizing information such as the column’s length, compressive strength of ultra-high-strength concrete, and the diameter, thickness, yield stress, and ultimate stress of the steel tube, the capacity of the column is predicted through non-linear calculations. In addition to the SOA, the future search algorithm (FSA) and social ski driver (SSD) are used as comparative benchmarks. The prediction results showed that the SOA-ANN can learn and predict the compression capacity pattern with high accuracy (relative error < 2.5% and correlation > 0.99). Also, this model outperformed both benchmark hybrids (i.e., FSA-ANN and SSD-ANN). Apart from accuracy, the configuration of the SOA-ANN is simpler owing to the smaller population recruited for the optimization task. An explicit formula for the proposed model is developed, which, owing to its observed efficiency, can be reliably applied to CCFST columns for the early estimation of the compression capacity.

Funder

Research on Key Hydraulic Technology of Bionic Fishway Construction

Publisher

MDPI AG

Subject

Building and Construction,Civil and Structural Engineering,Architecture

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