Back Analysis of Surrounding Rock Parameters of Large-Span Arch Cover Station Based on GP-DE Algorithm

• Published in: Applied sciences 2022-12, Vol.12 (24), p.12590
• Main Authors: Zheng, Fu, Jiang, Annan, Guo, Xinping, Min, Qinghua, Yin, Qingfeng
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; arch-cover method; Arches; back analysis; Civil engineering; Design optimization; differential evolution algorithm; Evolutionary algorithms; Evolutionary computation; Excavation; Gaussian process; Geology; Inversion; Machine learning; Neural networks; Optimization; orthogonal design; Parameter identification; Rocks; Simulation; Support vector machines; Underground construction
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app122412590

Due to the characteristics of soil–rock composites and large-span arches, the surrounding rock parameters of stations are difficult to obtain accurately under soft upper and hard lower geological conditions when the arch cover method is used to carry out the construction of a large-span underground excavation station. To optimize the design of stations and guide the next step of construction, an intelligent inverse analysis method, the Gaussian process differential evolution co-optimization algorithm (GP-DE algorithm), is proposed for the arch cover method for station construction. Taking the Shikui Road station of the Dalian Metro Line Five as the engineering background, the finite element model of FLAC3D is established. By combining the measured data of the sensor and the monitoring data obtained using the orthogonal scheme, this algorithm is used for the joint back analysis of displacement stress and the accuracy of the inversion parameters is verified by forwarding the calculation for FLAC3D. By using the obtained surrounding rock parameters, the demolition length of the center diaphragm to the Shikui Road station is optimized. Under different numbers of training samples, the inversion effect of the GP-DE algorithm and the other three common back-analysis algorithms is compared and analyzed. Finally, based on the iteration rate and convergence effect, the value range of the differential evolution algorithm parameters F and CR is given. The results show that the forward calculation results of the parameters obtained from the back analysis are in good agreement with the actual values, and the accuracy of the back-analysis results is high.