4.7 Article

Fuzzy adaptive jellyfish search-optimized stacking machine learning for engineering planning and design

Journal

AUTOMATION IN CONSTRUCTION
Volume 143, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.autcon.2022.104579

Keywords

Civil engineering informatics; Planning and design; Enhanced metaheuristic algorithm; Jellyfish search optimizer; Fuzzy adaptive logic controller; Machine learning; Stacking ensemble

Funding

  1. National Science and Technol- ogy Council
  2. [110-2221-E-011-080-MY3]

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This paper presents a novel fuzzy adaptive jellyfish search-optimized stacking system that demonstrates high accuracy and effectiveness in global optimization and engineering informatics.
This paper presents a novel fuzzy adaptive jellyfish search-optimized stacking system (FAJS-SS) that integrates the jellyfish search (JS) optimizer, the fuzzy adaptive (FA) logic controller, and stacking ensemble machine learning. First, FA logic is incorporated into JS optimizer to construct an efficient metaheuristic algorithm for global optimization. The proposed algorithm is benchmarked against various well-known optimizers using mathematical functions. The FAJS optimizer is then used to optimize the hyperparameters of the stacking system (SS). Cases that involve construction productivity, the compressive strength of a masonry structure, the shear capacity of reinforced deep beams, the axial strength of steel tube-confined concrete, and the resilient modulus of subgrade soils were investigated. Results of analyses reveal that the FAJS-SS predicts more accurately than the other machine learning systems in the literature. Accordingly, the proposed fuzzy adaptive metaheuristicoptimized stacking system is effective for providing engineering informatics in the planning and design phase.

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