Search Results (36)

Underground power pipelines are often encased in box culverts and buried in soil. When foundation pit excavation involves such existing pipelines, the buried box culverts can become partially suspended, risking excessive vertical deformation and requiring effective in situ protection. This study proposed analytical methods to calculate the vertical deformation of large-span box culverts under both unprotected and protected conditions. A case study of the 112 m suspended power box culverts at Yunnan Road Station on Nanjing Metro Line 5 is presented, where the methods are applied to determine the maximum allowable unsupported span and to formulate specific support and suspension protection schemes, which include a number of protection points and their spacing. Validation through ABAQUS modeling shows strong agreement among theoretical predictions, numerical simulations, and field measurements. Parametric analysis further demonstrated that the height, width, and modulus of the reinforced soil around the buried section all have a significant influence on the deformation control effectiveness. This study provides a combined theoretical framework and practical design guidelines for deformation control of large-span suspended box culverts in engineering applications. Full article

Traditional internal support systems for deep foundation pits often suffer from issues such as insufficient stiffness, excessive displacement, and large support areas. To address these problems, the authors developed a novel spatial steel joist internal support system. Based on a large-scale field model test, this study investigates the bearing characteristics of the proposed system in deep foundation pits. A stiffness formulation for the novel support system was analytically derived and experimentally validated through a calibrated finite element model. After validation with test results, the effects of different vertical prestressing forces on the structure were analyzed. The results indicate that the proposed system provides significant support in deep foundation pits. The application of both horizontal and vertical prestressing increases the internal forces within the support structure while reducing overall displacement. The numerical predictions of horizontal displacement, bending moment, and the axial force distribution of the main support, as well as their development trends, align well with the model test results. Moreover, increasing the prestressing force of the steel tie rods effectively controls the deformation of the vertical arch support and enhances the stability of the spatial structure. The derived stiffness formula shows a small error compared with the finite element results, demonstrating its high accuracy. Furthermore, the diagonal support increases the stiffness of the lower chord bar support by 28.24%. Full article

With the exponential growth of engineering monitoring data, data-driven neural networks have gained widespread application in predicting retaining structure deformation in foundation pit engineering. However, existing models often overlook the spatial deflection correlations among monitoring points. Therefore, this study proposes a novel deep learning framework, CGCA (Convolutional Gated Recurrent Unit with Cross-Attention), which integrates ConvGRU and cross-attention mechanisms. The model achieves spatio-temporal feature extraction and deformation prediction via an encoder–decoder architecture. Specifically, the convolutional structure captures spatial dependencies between monitoring points, while the recurrent unit extracts time-series characteristics of deformation. A cross-attention mechanism is introduced to dynamically weight the interactions between spatial and temporal data. Additionally, the model incorporates multi-dimensional inputs, including full-depth inclinometer measurements, construction parameters, and tube burial depths. The optimization strategy combines AdamW and Lookahead to enhance training stability and generalization capability in geotechnical engineering scenarios. Case studies of foundation pit engineering demonstrate that the CGCA model exhibits superior performance and robust generalization capabilities. When validated against standard section (CX1) and complex working condition (CX2) datasets involving adjacent bridge structures, the model achieves determination coefficients (R²) of 0.996 and 0.994, respectively. The root mean square error (RMSE) remains below 0.44 mm, while the mean absolute error (MAE) is less than 0.36 mm. Comparative experiments confirm the effectiveness of the proposed model architecture and the optimization strategy. This framework offers an efficient and reliable technical solution for deformation early warning and dynamic decision-making in foundation pit engineering. Full article

The unloading effect induced by foundation pit excavation leads to soil deformation, which may adversely affect the underlying tunnel. Foundation pit excavation is a three-dimensional (3D) deformation process, whereas most existing methods are based on a two-dimensional (2D) plane assumption. To improve conventional 2D analysis methods, this study considers the influence of the actual construction sequence on tunnel deformation. A 3D analytical method for evaluating tunnel deformation and stress induced by foundation pit excavation is proposed, based on the image source method and the rotational dislocation-coordinated deformation model. The proposed method is validated through comparative analysis with other methods using monitoring data from three engineering cases. Furthermore, the study examines and discusses the impact of excavation sequences on the final longitudinal displacement of the tunnel. The results indicate that the proposed method provides more accurate predictions of tunnel deformation induced by foundation pit excavation in actual projects. Staged and segmented excavation reduces bottom heave of the foundation pit, thereby mitigating its impact on the underlying tunnel. When the segmentation efficiency is positive, increasing the number of excavation blocks contributes to better tunnel deformation control. However, when the segmentation efficiency is negative, an increase in excavation blocks has an insignificant effect on deformation control or leads to excessive construction workload. Full article

The development trend of urban underground space towards deep and large three-dimensional foundation pit projects in complex environments faces the challenges of deformation and instability of supporting structures, strong sensitivity of the surrounding environment, and significant limitations of the traditional design theory. Based on the ultra-long/deep foundation pit project at the Shenzhen Airport East Station, a refined three-dimensional finite element simulation is used to systematically study the deformation mechanism of the supporting structures of deep and large foundation pits under a complex environment and investigate the influence on the neighbouring buildings. In this study, a three-dimensional finite element model is constructed considering the soil–structure coupling effect, and the mechanical response law of the foundation pit under the compliant–inverse combination method is revealed. Based on ABAQUS 6.14, a 10 m wide strip-shaped model of the central island area and an environmental risk source model including an underground station and group pile foundation are established. The analysis shows the following: the lateral shift in the ground wall is distributed in a ‘convex belly’ shape, with a maximum displacement of 29.98 mm; the pit bottom is raised in the shape of the bottom of a rebutted pot, and the settlement behind the wall has an effect ranging up to 3.8 times the depth of the excavation; the lateral shift in the side wall of the neighbouring underground station and the differential settlement of the group piles validate the predictive ability of the model on the complex-environment coupling effect. The research results can provide guidance for the design and construction of support structure projects and similar projects. Full article

The rapid development of urban infrastructure has accelerated the construction of large foundation pit projects, posing challenges for deformation monitoring and safety. This study proposes a novel approach integrating time-series InSAR data with a multivariate LE-Transformer model for deformation prediction. The LE-Transformer model integrates Long Short-Term Memory (LSTM) to capture temporal dependencies, Efficient Additive Attention (EAA) to reduce computational complexity, and Transformer mechanisms to model global data relationships. Deformation monitoring was performed using PS-InSAR and SBAS-InSAR techniques, showing a high correlation coefficient (0.92), confirming the reliability of the data. Gray relational analysis identified key influencing factors, including rainfall, subway construction, residential buildings, soil temperature, and hydrogeology, with rainfall being the most significant (correlation of 0.838). These factors were incorporated into the LE-Transformer model, which outperformed univariate models, achieving a mean absolute percentage error (MAPE) of 2.5%. This approach provides a robust framework for deformation prediction and early warning systems in urban infrastructure projects. Full article

During deep foundation pit construction, slight improper operations may lead to excessive deformation, resulting in engineering accidents. Therefore, how to accurately predict the deformation of the deep foundation pit is of significant importance. With advancements in artificial intelligence technology, machine learning has been utilized to learn and simulate complex nonlinear relationships among various factors influencing foundation pit deformation. Prediction accuracy is significantly improved, and the dynamic trend of foundation pit deformation is accurately grasped to curb the risk of safety accidents. This paper systematically reviews the current applications of machine learning in deep foundation pit deformation prediction. The fundamental principles of machine learning models, including neural networks, support vector machines, and Bayesian networks, are elaborated in the context of their application to deep foundation pit deformation prediction. The application effects of various machine learning models in predicting deep foundation pit supporting structure deformation, surrounding surface settlement, and assessing foundation pit risks are summarized. The limitations and future development prospects of current machine learning models for deformation prediction in deep foundation pit construction are discussed. The research results offer valuable insights for the application and advancement of machine learning in the deep foundation pit deformation prediction field. Full article

The axial force in assembly steel struts with servo systems is a critical indicator of stability in foundation pit support systems. Due to its high sensitivity to temperature variations and direct influence on the lateral deformation of the foundation pit enclosure structure, accurate prediction is essential for safety monitoring and early warning. This study proposes a novel method for predicting the axial force in assembly steel struts with servo systems based on a spatiotemporal adaptive network. The method begins by feeding historical axial force data from multiple steel struts into an LSTM network to extract temporal sequence features. A self-attention mechanism is then employed to capture the global dependencies within the axial force data, enhancing the feature representation. Concurrently, a convolutional neural network (CNN) is utilized to extract local spatial features. Additionally, excavation depth and excavated soil stratification data are processed through convolutional operations to derive stratification-related features. Subsequently, the temporal and spatial features of axial force are fused with stratification-related features derived from excavation data and further refined through a CNN, enabling more accurate predictions. Validation using deep foundation pit data from a metro station in Zhejiang Province demonstrated the method’s reliability and improved performance across multiple metrics compared to the existing approaches. Full article

The hardening soil model with small-strain stiffness (HSS model) is widely applied in deep foundation pit engineering in coastal soft-soil areas, yet it is characterized by a multitude of parameters that are relatively cumbersome to acquire. In this study, we incorporate a genetic algorithm and a back-propagation neural network (BPNN) model into an inversion analysis for HSS model parameters, with the objective of facilitating a more streamlined and accurate determination of these parameters in practical engineering. Utilizing horizontal displacement monitoring data from retaining structures, combined with local engineering, both a BPNN model and a BPNN optimized by a genetic algorithm (GA-BPNN) model were established to invert the stiffness modulus parameters of the HSS model for typical strata. Subsequently, numerical simulations were conducted based on the inverted parameters to analyze the deformation characteristics of the retaining structures. The performances of the BPNN and GA-BPNN models were evaluated using statistical metrics, including R², MAE, MSE, WI, VAF, RAE, RRSE, and MAPE. The results demonstrate that the GA-BPNN model achieves significantly lower prediction errors, higher fitting accuracy, and predictive performance compared to the BPNN model. Based on the parameters inverted by the GA-BPNN model, the average compression modulus $E_{s1-2}$, the reference tangent stiffness modulus $E_{oed}^{ref}$, the reference secant stiffness modulus $E_{50}^{ref}$, and the reference unloading–reloading stiffness modulus $E_{ur}^{ref}$ for gravelly cohesive soil were determined as $E_{oed}^{ref}=0.83E_{s1-2}$ and $E_{ur}^{ref}={8.14E}_{50}^{ref}$; for fully weathered granite, $E_{oed}^{ref}=1.54E_{s1-2}$ and $E_{ur}^{ref}={5.51E}_{50}^{ref}$. Numerical simulations conducted with these stiffness modulus parameters show excellent agreement with monitoring data, effectively describing the deformation characteristics of the retaining structures. In situations where relevant mechanical tests are unavailable, the application of the GA-BPNN model for the inversion analysis of HSS model parameters is both rational and effective, offering a reference for similar engineering projects. Full article

Underground space development has significantly increased the depth, scale, and complexity of foundation pit engineering. However, monitoring systems lack mechanical analysis models and fail to predict and control construction risks. Additionally, the foundation pit model could not be updated based on on-site observed data, leading to inaccurate predictions. This study proposes a DT modeling framework for foundation pits, which is used to simulate, predict, and control the risks associated with the entire excavation process. Consequently, based on the DT modeling framework, a DT foundation pit model (DTFPM) was established using modeling and updating algorithms. This study summarizes and identifies the key modeling parameters of foundation pits. A parametric modeling algorithm based on ABAQUS (v2020) was developed to drive the excavation pit modeling process within seconds. Furthermore, an inverse analysis optimization algorithm based on genetic algorithms (GA) and real-time observed deformation was employed to update the elastic modulus of the soil. The algorithm supports parallel computing and can converge within 10 generations. The prediction error of the model after inverse analysis can be reduced to within 10%. Finally, the authors applied DTFPM to establish an intelligent monitoring system. The focus is on real-time and predictive warnings based on the monitoring deformation of the current construction step and the updated model. This study analyzes a Beijing project case to verify the effectiveness of the system, demonstrating the practical application of the proposed method. The results showed that the DTFPM could accurately simulate the deformation behavior of the foundation pit. The system could provide more timely and accurate safety warnings. The proposed method can potentially contribute to the intelligent construction of foundation pits in the future, both theoretically and practically. Full article

In deep foundation pit engineering, the soil undergoes a complex stress path, encompassing both loading and unloading phases. The Shanghai model, an advanced constitutive model, effectively accounts for the soil’s deformation characteristics under these varied stress paths, which is essential for accurately predicting the horizontal displacement and surface settlement of the foundation pit’s enclosure structure. This model comprises eight material parameters, three initial state parameters, and one small-strain parameter. Despite its sophistication, there is a scarcity of numerical studies exploring the correlation between these parameters and the deformation patterns in foundation pit engineering. This paper initially establishes the superiority of the Shanghai model in ultra-deep circular vertical shaft foundation pit engineering by examining a case study of a nursery circular ultra-deep vertical shaft foundation pit, which is part of the Suzhou River section’s deep drainage and storage pipeline system pilot project in Shanghai. Subsequently, utilizing an idealized foundation pit engineering model, a comprehensive sensitivity analysis of the Shanghai model’s multi-parameter values across their full range was performed using orthogonal experiments. The findings revealed that the parameter most sensitive to the lateral displacement of the underground continuous wall was κ, with an increase in κ leading to a corresponding increase in displacement. Similarly, the parameter most sensitive to surface subsidence outside the pit was λ, with an increase in λ resulting in greater subsidence. Lastly, the parameter most sensitive to soil uplift at the bottom of the pit was also κ, with an increase in κ leading to more significant uplift. Full article

In the simulation of foundation pit excavation, the traditional element birth–death method commonly used tends to encounter issues such as uncoordinated deformation and changes in the constitutive model, affecting the accuracy of the prediction results. To address these issues, this study proposes the use of element tracking. By duplicating elements for temporary supports or structures requiring changes in material properties and appropriately activating or deactivating them at the right moments, the simulation of the foundation pit excavation process can be achieved more precisely. Using the construction process of the Tangxi Passenger Transport Station’s comprehensive transportation hub foundation pit as an example, this study applied the proposed simulation method and compared the results with actual measurements, demonstrating its effectiveness. This research offers a more accurate approach for simulating foundation pit excavation and provides a reference for similar numerical simulation problems. Full article

Building a deep foundation pit in urban centers frequently confronts issues such as closeness to structures, high excavation depths, and extended exposure durations, making monitoring and prediction of the settlement and deformation of neighboring buildings critical. Machine learning and deep learning models are more popular than physical models because they can handle dynamic process data. However, these models frequently fail to establish an appropriate balance between accuracy and generalization capacity when dealing with multi-objective prediction. This work proposes a multi-objective prediction model based on the XGBoost algorithm and introduces the Random Forest Bayesian Optimization method for hyperparameter self-optimization and self-adaptation in the prediction process. This model was trained with monitoring data from a deep foundation pit at Luomashi Station of Chengdu Metro Line 18, which are characterized by a sand and pebble stratum, cut-and-cover construction, and a depth of 45.5 m. Input data of the model included excavation rate, excavation depth, construction time, shutdown time, and dewatering; output data included settlement, ground settlement, and pit deformation at an operating metro station only 5.7 m adjacent to the ongoing pits. The training effectiveness of the model was validated through its high R² scores in both training and test sets, and its generalization ability and transferability were evaluated through the R² calculated by deploying it on adjacent monitoring data (new data). The multi-objective prediction model proposed in this paper will be promising for monitoring the data processing and prediction of settlement of surrounding buildings for ultra-deep foundation pit engineering. Full article

Numerical simulation is an essential method for predicting soil deformation caused by foundation pit excavation. Its accuracy relies on the constitutive model and its parameters. However, obtaining these parameters through lab tests has limitations like long durations, high costs, and potential errors. To improve the simplicity and accuracy of the selection of constitutive model parameters, this study proposes a step-by-step inverse analytical method. Using the braced excavation project in Hangzhou City, China as a case study, the proposed method firstly determines the ratio of the important constitutive parameters then the values of the key constitutive parameters. The results show that the reference secant stiffness ($E_{50}^{ref}$) and shear strain (γ_0.7) of the hardening soil (HS) and hardening soil–small soil (HSS) models are the key constitutive parameters. The step-by-step inverse method not only reduces the number of parameters, but also improves the predicting accuracy. The established empirical relationship between the $E_{50}^{ref}$ and standard penetration test (SPT) blow counts exhibits a good linear correlation. The parameter selection method proposed in this study is an accurate, practical, and efficient method, which can effectively predict the horizontal displacement and surface settlement of the retaining structure in multiple excavation stages. Full article

In the context of increasing environmental awareness and the demand for sustainable construction practices, the use of recyclable materials in civil engineering projects has gained significant attention. This study focuses on the bearing characteristics and deformation behavior of recyclable steel-pipe piles during the excavation of foundation pits. Field experiments and numerical simulations were conducted to comprehensively analyze the stress characteristics and surface settlement patterns behind the piles. The results reveal critical insights into the interaction between the steel-pipe piles and the surrounding soil, providing a detailed understanding of the stress distribution and deformation mechanisms. An empirical method for calculating the surface settlement value, induced by foundation pit excavation under the support of steel-pipe slope protection piles, has been proposed. This method improves the accuracy of settlement predictions and enhances the reliability of foundation pit design. Full article