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

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