The unconfined compressive strength estimation of rocks using a novel hybridization technique based on the regulated Gaussian processor

• Published in: Journal of engineering and applied science (Online) Vol. 71; no. 1; pp. 101 - 22
• Main Authors: Huang, Linhua, Li, Song, Guo, Enping
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024; Springer Nature B.V; SpringerOpen
• Subjects: Accuracy; Algorithms; Business metrics; Civil Engineering; Compressive strength; Convergence; Datasets; Decision making; Electrical Engineering; Engineering; Engineering Mathematics; Equilibrium slime mould algorithm; Gaussian process; Gaussian process regression; Geology; Geotechnical engineering; Hybridization; Industrial Chemistry/Chemical Engineering; Load; Mechanical Engineering; Methods; Microprocessors; Neural networks; Optimization; Prediction models; Rocks; Sand cat swarm optimization; Security; Stone; Tunnel construction; Unconfined compressive strength; Gaussian process regression; Equilibrium slime mould algorithm; Sand cat swarm optimization; Unconfined compressive strength
• ISSN: 1110-1903; 2536-9512
• DOI: 10.1186/s44147-024-00416-8

The unconfined compressive strength (UCS) of rocks is a crucial factor in geotechnical engineering, assuming a central role in various civil engineering undertakings, including tunnel construction, mining operations, and the design of foundations. The precision in forecasting UCS holds paramount importance in upholding the security and steadfastness of these endeavors. This article introduces a fresh methodology for UCS prognostication by amalgamating Gaussian process regression (GPR) with two pioneering optimization techniques: sand cat swarm optimization (SCSO) and the equilibrium slime mould algorithm (ESMA). Conventional techniques for UCS prediction frequently encounter obstacles like gradual convergence and the potential for becoming ensnared in local minima. In this investigation, GPR is the foundational predictive model due to its adeptness in managing nonlinear associations within the dataset. The fusion of GPR with cutting-edge optimizers is envisioned to elevate the precision and expeditiousness of UCS prognostications. An extensive collection of rock samples, each accompanied by UCS measurements, is harnessed to assess the suggested methodology. The efficacy of the GPSC and GPES models is juxtaposed with the conventional GPR technique. The findings reveal that incorporating SCSO and ESMA optimizers into GPR brings about a noteworthy enhancement in UCS prediction accuracy and expedites convergence. Notably, the GPSC models exhibit exceptional performance, evidenced by an exceptional R 2 value of 0.995 and an impressively minimal RMSE value of 1.913. These findings emphasize the GPSC model’s potential as an exceedingly auspicious tool for experts in the realms of engineering and geology. It presents a sturdy and dependable method for UCS prediction, a resource of immense value in augmenting the security and efficiency of civil engineering endeavors.