## 1 Introduction  
In recent years, urban rail transit in China has developed rapidly. Shield tunneling has become the mainstream construction method for urban transit tunnels owing to its significant technical advantages [1, 2, 3]. However, because of complex geological conditions and improper operational practices, shield tunneling inevitably induces loss to soil layers, posing safety risks to nearby buildings (structures) [4]. Accurate prediction of surface settlement induced by shield tunneling in complex construction environments, along with timely adjustment of tunneling parameters to minimize soil layer disturbance, has become a key focus in shield tunnel research [5, 6, 7]. The mechanism of surface settlement caused by shield tunneling is complex. The interplay of multiple factors increases the difficulty of prediction and poses significant challenges to settlement control [8, 9]. Methods for predicting surface settlement caused by shield tunneling can be broadly categorized into three types: theoretical analysis [10; 11], numerical simulation [12; 13], and machine learning [14; 15]. Theoretical analysis requires many simplifications and assumptions, limiting its applicability to real-world projects. Recent advancements in numerical modeling have further quantified the sensitivity of key tunneling parameters to settlement outcomes. For example, ref. [16] proposed a validated TBM excavation model and demonstrated that tunnel diameter and eccentricity account for 22.5% and 17% of structural settlement variation, respectively. While such parametric studies provide critical insights, their reliance on predefined assumptions limits real-time adaptability to dynamic construction conditions. In contrast, machine-learning methods, with their robust self-learning and analytical capabilities, can uncover intrinsic patterns within data and have been widely applied to shield-induced surface settlement prediction [15; 17; 18]. Algorithms such as support vector regression [19], random forest (RF) [4; 20], multilayer perceptrons [21], long short-term memory (LSTM) networks [22], the Informer algorithm [23], the Transformer algorithm [24], and the WaveNet algorithm [25] have been successfully applied to the prediction of settlement induced by shield tunneling. Proposed in 2021, the Informer algorithm [26] is a deep-learning algorithm specifically designed for long-time-series prediction. It arranges and predicts data samples according to the temporal and spatial continuity inherent in shield tunneling processes, offering significant advantages over machine-learning algorithms that rely solely on individual settlement values. [PERSON] et al. employed the standard Informer model to predict subway tunnel settlement, integrating multivariate inputs, including temperature and soil pressure. No architectural modifications were made, but leveraging its native long-sequence processing capability effectively captured nonlinear deformation patterns in complex geological environments [27]. [PERSON] and [PERSON] applied the vanilla Informer architecture for TBM thrust prediction. Their key adaptation involves relaxing the Pearson correlation threshold during feature selection to reduce noise. This preprocessing optimization enhances model robustness while maintaining cross-project generalization without altering Informer's core design [28]. [PERSON] and [PERSON] benchmarked the unmodified Informer against Autoformer for ground deformation forecasting. The results reveal Informer's competitive performance but highlight inherent limitations in extreme long-horizon predictions, suggesting opportunities for architectural enhancements in future work [29]. [PERSON] et al. found that the proposed LSPP model leverages Informer's long-term prediction capability by integrating tunneling parameters and stratigraphic data. They improved Informer with ProbSparse self-attention and dynamic decoding to address error accumulation in traditional RNNs, enhancing long-distance forecasting accuracy. However, computational demands remain a challenge [30].