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This study aims to investigate the behavior of ground surface settlement in TBM double-line tunnels constructed under existing buildings and to devise a calculative representation for that behavior. Numerical simulation and field monitoring methods were used to examine the Zhongcong Tunnel in Chongqing Metro Line 9. The ground surface settlement was analyzed using an orthogonal test of 3D numerical simulation methods. The results showed that ground surface settlement was influenced by TBM tunneling parameters and the location of the existing building in the following manner. The existing building reduced the settlement trough width. Surface settlement was increased by frictional and palm surface thrust forces but reduced by grouting pressure. The settlement trough width of the first excavation iz correlated with that of the last excavation iy. To accommodate the influence of existing buildings, the tilt factor of the settlement trough TR was introduced to improve the formula for calculating the ground surface settlement of TBM double-line tunnels. The improved formula was validated by comparing the calculated results with actual measurements.

During high speed railway construction, shield-tunnel undercrossing frequently induces subgrade settlement, which threatens project safety and progress. Existing settlement monitoring methods struggle to provide timely early warnings due to unclear data features and inadequate long-term dependency modeling.To address this, we propose a settlement early warning method for high-speed railway subgrades based on TD Transformer. Firstly, we utilize temporal-spatial enhanced attention (TSEA) for feature extraction from high-speed railway settlement data, effectively resolving the problem of vague features post-extraction. Secondly, dynamic global temporal attention (DGTA) is employed to dynamically capture and represent the long-term dependencies of settlement data. Experimental results demonstrate that TD Transformer achieves Accuracy, Precision, Recall, and F1-Score of 93.39%, 93.10%, 93.40%, and 93.24%, respectively, outperforming other advanced settlement early warning methods for high-speed railway subgrade with relative improvements of 1.24%, 1.3%, 1.3%, and 1.27%.This method effectively forecasts subgrade settlement and exhibits significant superiority in the task of multi-factor settlement early warning for high-speed railway subgrades.

A major consideration in urban tunnel design is to estimate the ground movements and surface settlements associated with the tunnelling operations. Excessive ground movements may result in damage to adjacent buildings and utilities. Numerous empirical and analytical solutions have been proposed to relate the shield tunnel characteristics and surface/subsurface deformation. Numerical analyses, either 2D or 3D, have also been applied to such tunnelling problems. However, substantially fewer approaches have been developed for earth pressure balance (EPB) tunnelling. Based on instrumented data on ground deformation and shield operation from three separate EPB tunnelling projects in Singapore, this paper utilizes a multivariate adaptive regression splines (MARS) approach to establish relationships between the maximum surface settlement and the major influencing factors, including the operation parameters, the cover depth and the ground conditions. Since the method has the ability to map input to output patterns, MARS enables one to map all influencing parameters to surface settlements. The main advantages of MARS over other soft computing techniques such as ANN, RVM, SVM and GP are its capacity to produce a simple, easy-to-interpret model, its ability to estimate the contributions of the input variables, and its computational efficiency.

•During experimental simulations of both dry excavation and dewatering excavation, we collected data on surface settlement, groundwater heads outside the pit, and diaphragm wall deflection.•We compared and analyzed the characteristics of surface settlement under various conditions and discussed the patterns of settlement during dewatering and excavation at key locations outside the pit.•We thoroughly investigated the relationship between the type of settlement pattern and the lateral displacement shape of the diaphragm wall. To investigate surface settlement under the combined effect of foundation pit dewatering and excavation, a series of experiments was conducted using a scaled model of a deep foundation pit at a metro station. During experimental simulations of the dry excavation and dewatering processes, data were collected on surface settlement, water heads outside the pit, and deflection of the diaphragm wall. The characteristics of surface settlement were compared and analyzed under different conditions with a focus on the development of surface settlement during dewatering and excavation at key locations outside the pit. The combined effect of dewatering and excavation was found to increase surface settlement outside the pit and expand its area of influence. The insertion ratio of the diaphragm wall (n) significantly affected surface settlement; as the insertion ratio increased, surface settlement, along with its area of influence, decreased. For n < 1.25, the area beyond twice the excavation depth was considered a minor area of settlement influence. In contrast, for n ≥ 1.25, this area wasn’t classified as a minor area of settlement influence. As excavation depth increased, the surface settlement pattern outside the pit transitioned from triangle-type to groove-type, groove-type settlement occurred when As ≥ 1.6Ac, whereas triangle-type settlement occurred under other conditions (As, which represents the area of the deep inward part of the convex deformation of the diaphragm wall; Ac, which refers to the cantilever part of the diaphragm wall). This study provides insights into the development of surface settlement during dewatering and excavation and serves as a valuable reference for innovations in sustainable and resilient underground design.

The study is based on a section of the Zhengzhou Metro Line 8 quasi-rectangular shield tunnel. Field excavation trials were conducted to analyze the surface settlement patterns caused by the construction of a large-section quasi-rectangular shield tunnel in the rich water sand layer in Zhengzhou. Based on the characteristics of the rich water sand layer, ground settlement control measures were proposed. The research findings show that the surface settlement caused by the construction of the large-section quasi-rectangular shield tunnel in the rich water sand layer exhibits a temporal curve pattern of slow settlement (Stage I: pre-arrival of the shield), rapid settlement (Stage II: shield passage, Stage III: shield tail exiting 14.4 ~ 18 m), and stable settlement (Stage IV: late settlement). In Stage I, controlling the excavation rate to maintain balance between the cutter face pressure and soil pressure is effective. In Stage II, injecting lubricating mud between the shell and the sand layer to reduce soil friction and shear slip is recommended. In Stage III, increasing the synchronous grouting volume at the shield tail and adjusting grouting pressure, as well as timely filling the shield tail construction gap, are effective methods to reduce surface settlement. The Peck formula was used to fit the transverse settlement trough on the surface, with a linear correlation coefficient R²=0.983, validating the use of the Peck formula to predict surface settlement troughs for quasi-rectangular shield tunneling in the rich water sand layer. These research findings can provide data support and reference for similar projects.

The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized by particle swarm optimization (PSO) for prediction of MSS. Here, this combination is abbreviated using PSO-ANN. To indicate the performance capacity of the PSO-ANN model in predicting MSS, a pre-developed ANN model was also developed. To construct the mentioned models, horizontal to vertical stress ratio, cohesion and Young's modulus were set as input parameters, whereas MSS was considered as system output. A database consisting of 143 data sets, obtained from the line No. 2 of Karaj subway, in Iran, was used to develop the predictive models. The performance of the predictive models was evaluated by comparing performance prediction parameters, including root mean square error (RMSE), variance account for (VAF) and coefficient correlation (R 2). The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results. In addition, the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs.

Physical characteristics of aggregates have a significant influence on the performance of concrete. Compared to conventional concrete, the mix design of highly flowable concrete is more complex and should ensure that the mixture can develop adequate static and dynamic stability. The selection of aggregate plays a major role for the mix design and mixture optimization of flowable concrete. The study seeks to understand the influence of physical characteristics of coarse and fine aggregates, including packing density, texture or roughness, fine particle content, shape, and quantity of flat and elongated particles on the workability, rheological properties, and mechanical properties of superworkable concrete (SWC). Three types of sands with different fineness moduli of 2.5, 2.6, and 3.0 and different textures (smooth and rough) were used. Seven types of coarse aggregates with different texture characteristics, flat and elongated particle contents, and different shapes were investigated. Sand-to-total aggregate volume ratio was varied between 0.45 and 0.60. Test results indicated that the packing density, the quantity of fines passing 315 µm sieve, and the shape of coarse aggregate can have significant effect on rheology, stability, and compressive strength of SWC. For the crush aggregate with 5–14 mm particle sizes (CA14), a 13 % increase in aggregate packing density from 0.69 to 0.79 by the use of optimum sand-to-aggregate ratio and natural sand can lead to more than 50 % reduction in surface settlement, i.e. 50 % increase in static stability. For the CA14 coarse aggregate, a good relationship was established between surface settlement and quantity of fine particles with diameter smaller than 315 µm. Mixtures with rounded coarse aggregates had 22–42 % higher surface settlement compared to those made with crushed aggregates of the same maximum size aggregate.

This paper presents a surrogate modeling approach for predicting ground surface settlement caused by synchronous grouting during shield tunneling process. The proposed method combines finite element simulations with machine learning algorithms and introduces an intelligent optimization algorithm to invert geological parameters and synchronous grouting variables, thereby predicting ground surface settlement without conducting numerous finite element analyses. Two surrogate models based on the random forest algorithm are established. The first is a parameter inversion surrogate model that combines an artificial fish swarm algorithm with random forest, taking into account the actual number and distribution of complex soil layers. The second model predicts surface settlement during synchronous grouting by employing actual cover-diameter ratio, inverted soil parameters, and grouting variables. To avoid changes to input parameters caused by the number of overlying soil layers, the dataset of this model is generated by the finite element model of the homogeneous soil layer. The surrogate modeling approach is validated by the case history of a large-diameter shield tunnel in Beijing, providing an alternative to numerical computation that can efficiently predict surface settlement with acceptable accuracy.

Urban tunneling, especially in the construction of shallow metro stations, can lead to significant ground movements that may affect the safety and integrity of nearby surface structures. This research explores how surface settlements develop during NATM-based tunnel excavation, focusing particularly on the role of ground surface loads from traffic and buildings. The study centers on a real-world subway station in Shiraz, Iran, and employs the FLAC3D v9 software to simulate ground responses during excavation using the finite volume method. Initial modeling, carried out in the absence of surface loading, showed a maximum ground settlement of 62.3 mm. Subsequently, variable surface loads, including traffic and building weights, were applied to the ground surface above the metro station. The results demonstrated a linear increase in surface settlement with increasing surface loads. For instance, the load of a 3-storey building elevated the settlement to 73.3 mm, while that of a 9-storey building further increased it to 93.4 mm (21.5% increase). Similarly, traffic loads exhibited proportional effects on settlement. In a final analysis mimicking the conditions at Esteghlal Station, a combined surface load consisting of 20 kPa traffic load and a 3-storey building load was applied. The findings revealed that the lateral stress imposed by side buildings mitigated vertical stresses on the station, thereby reducing surface settlement. The lateral load from buildings above the metro station decreased maximum surface settlement by approximately 5 to 10 mm (6.5–14.8% reduction). These results suggest that moderate lateral loads from side surface structures can positively influence settlement behavior during urban tunneling projects.

Urban expansion and subsurface resource exploitation have intensified ground subsidence, posing significant geological risks. Conventional prediction models often overlook multi-scale spatiotemporal effects that critically influence accuracy. This study proposes an integrated MGTWR-CNN-BiLSTM-AM (MGCBA) model to address this gap. Utilizing SBAS-InSAR-derived deformation data from Shanghai’s primary subsidence zones, validated through GNSS and PS-InSAR observations, we developed a Multi-scale Geographically and Temporally Weighted Regression (MGTWR) framework. This model quantifies nonlinear spatiotemporal relationships between subsidence and driving factors, including monthly-scale variables (groundwater extraction, precipitation) and annual-scale parameters (land use, soil type), generating dynamic weight matrices. The integrated CNN-BiLSTM-AM (CBA) deep learning network extracts critical time-series features to optimize spatiotemporal weights adaptively. Experimental results demonstrate a prediction accuracy of 0.99347 (RMSE: 1.8643 mm), outperforming the standalone CBA model (0.98494) by 0.85%. SHAP value analysis identifies monthly groundwater levels, soil moisture, and annual-scale soil type/DEM as dominant contributors to Shanghai’s urban core subsidence. The proposed multi-scale spatiotemporal modeling framework advances surface deformation prediction by enhancing the interpretability of key drivers under spatiotemporally variable conditions.