For single-hole tunnels, empirical formulas such as the peck formula are often improved to predict surface settlement 1, 2, 3. At present, surface settlement prediction methods include three main categories: empirical formulas, numerical simulations, and machine learning. Many factors and complex interactions that induce surface settlement make it difficult to predict surface settlement. Therefore, the prediction of surface settlement caused by shield construction is a prominent guarantee for the safety of the construction process and the stability of the surrounding environment. The shield tunneling process usually leads to changes in the stress state of the soil around the tunnel, and the deformation generated by the soil disturbance is transferred to the ground surface, which eventually forms surface settlement. With the development of urban underground space, the shield construction method has become a common tunneling method. Intelligent prediction methods can predict surface subsidence more automatically and quickly, becoming a new development trend. The combined EMD-CASSA-ELM prediction model can greatly improve the accuracy and speed of surface settlement prediction, and provide a new means for safety monitoring in shield tunnel construction. Taking a shield interval in Jiangsu, China as an example, the meta-heuristic algorithm-optimized ELM model improves the prediction accuracy by 10.70% compared with the traditional ELM model. The fluctuation components and trend components decomposed by EMD are predicted one by one, and the prediction results are superimposed and reconstructed to obtain the predicted final settlement. First, the EMD is used to decompose the settlement sequence into trend vectors and fluctuation vectors to fully extract the effective information of the sequence Second, the sparrow search algorithm is improved by introducing Cubic chaotic mapping to initialize the population and adaptive factor to optimize the searcher’s position formula, and the chaotic adaptive sparrow search algorithm is proposed Finally, the CASSA-ELM prediction model is constructed by using CASSA to find the optimal values of weights and thresholds in the extreme learning machine. In this paper, a prediction method combining empirical mode decomposition (EMD), chaotic adaptive sparrow search algorithm (CASSA), and extreme learning machine (ELM) is proposed. To ensure the safety and stability of the shield tunnel construction process, the ground settlement induced by the shield construction needs to be effectively predicted.
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