Cloud evolutionary computation system for advanced engineering analytics

Chou, Jui‐Sheng; Kosasih, Jeffisa Delaosia; Chong, Wai Kiong

doi:10.1007/s00366-020-01249-8

Cited by 1 publication

(1 citation statement)

References 77 publications

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“…Therefore, real-time inversion of surrounding rock parameters is needed to guide the construction of underground powerhouse 20 , 37 . To realize the real-time analysis of monitoring data and improve the utilization rate of monitoring data, cloud computing (CC) 38 , 39 and internet of things (IOT) 40 , 41 are gradually used in geotechnical engineering monitoring and analysis. For example, Sun et al 42 designed a tailings dam monitoring and pre-alarm system (TDMPAS) based on the IOT and the CC, which realized the real-time monitoring of the saturation line, water level, and dam deformation of a tailings dam.…”

Section: Introductionmentioning

confidence: 99%

Cloud inversion analysis of surrounding rock parameters for underground powerhouse based on PSO-BP optimized neural network and web technology

Qu,

Xie,

Pei

et al. 2024

Sci Rep

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Aiming at the shortcomings of the BP neural network in practical applications, such as easy to fall into local extremum and slow convergence speed, we optimized the initial weights and thresholds of the BP neural network using the particle swarm optimization (PSO). Additionally, cloud computing service, web technology, cloud database and numerical simulation were integrated to construct an intelligent feedback analysis cloud program for underground engineering safety monitoring based on the PSO-BP algorithm. The program could conveniently, quickly, and intelligently carry out numerical analysis of underground engineering and dynamic feedback analysis of surrounding rock parameters. The program was applied to the cloud inversion analysis of the surrounding rock parameters for the underground powerhouse of the Shuangjiangkou Hydropower Station. The calculated displacement simulated with the back-analyzed parameters matches the measured displacement very well. The posterior variance evaluation shows that the posterior error ratio is 0.045 and the small error probability is 0.999. The evaluation results indicate that the intelligent feedback analysis cloud program has high accuracy and can be applied to engineering practice.

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