Research on Dynamic Prediction of Multi-Index Variables During Foundation Excavation Based on the Genetic Algorithm

FU Xinzheng; CUI Chunyu; ZHANG Qianqing; WANG Sirui; XUE Youquan; GAO Peng

doi:10.3724/j.gyjzG23052511

Volume 54 Issue 11

Nov. 2024

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Article Navigation > INDUSTRIAL CONSTRUCTION > 2024 > 54(11): 33-40

FU Xinzheng, CUI Chunyu, ZHANG Qianqing, WANG Sirui, XUE Youquan, GAO Peng. Research on Dynamic Prediction of Multi-Index Variables During Foundation Excavation Based on the Genetic Algorithm[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(11): 33-40. doi: 10.3724/j.gyjzG23052511

Citation:

FU Xinzheng, CUI Chunyu, ZHANG Qianqing, WANG Sirui, XUE Youquan, GAO Peng. Research on Dynamic Prediction of Multi-Index Variables During Foundation Excavation Based on the Genetic Algorithm[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(11): 33-40. doi: 10.3724/j.gyjzG23052511

Citation:

FU Xinzheng, CUI Chunyu, ZHANG Qianqing, WANG Sirui, XUE Youquan, GAO Peng. Research on Dynamic Prediction of Multi-Index Variables During Foundation Excavation Based on the Genetic Algorithm[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(11): 33-40. doi: 10.3724/j.gyjzG23052511

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Research on Dynamic Prediction of Multi-Index Variables During Foundation Excavation Based on the Genetic Algorithm

doi: 10.3724/j.gyjzG23052511

1. Shandong Luqiao Group Co., Ltd., Jinan 250014, China;
2. Shandong Jiaotong University, Jinan 250357, China;
3. Institute of Geotechnical and Underground Engineering, Shandong University, Jinan 250061, China

Received Date: 2023-05-25
Available Online: 2024-12-05

Abstract

Abstract

A hybrid model of genetic algorithm combined with residual network (the GA-ResNN dynamic prediction model) for multi-index variables during deep foundation excavation and the construction risk assessment method were established by combining the back propagation (BP) artificial neural network, genetic algorithm(GA), and residual network(ResNN) to address the problems of low training efficiency of existing machine learning prediction models, the possibility of a single algorithm falling into local optima, and inability to converge. An intelligent early warning platform for excavation construction risks was developed. Research showed that the proposed GA-ResNN dynamic prediction model was of better prediction accuracy compared with the BP neural network model and GA-BP network model and could realize quantitative prediction and qualitative risk level evaluation. The intelligent warning platform for foundation excavation construction risks could present the prediction curve and warning threshold, which could improve the intelligent management and risk control level for engineering projects of foundation excavation.
- foundation excavation,
- machine learning,
- heuristic gemetic algorithm,
- dynamic prediction,
- risk assessment

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References(21)

References

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