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In the recent times, many studies have been devoted toward rectangular excavations but only a few studies have considered the “corner effect” in the optimized design of soil nail wall-retaining pile-anchor cable supporting systems, especially in large-scale deep foundation pit environments excavated by the central-island technique. Corner effect not only increases the construction costs but also may pose a risk to the safety and stability of such pits. In this paper, changes in the lateral displacement of retaining pile, soil at 1 m away from the foundation pit, crown beam, and the settlement of ground surface and surrounding buildings were extensively investigated based on the field measurements and numerical simulations of a large-scale deep foundation pit in Gaoxin zone, Xi’an, China. In addition, the supporting structure was optimized by considering the lateral influence zone of the corner effect. The optimization scheme proposed in this study not only satisfies the safety requirements of foundation pit supports, but also reduces the construction costs.

The excavation of deep foundation pit could have a great impact on the adjacent existing structures. The proposed Ferris wheel is located in Jianghehui Block. The construction site is narrow and adjacent existing structures. In this paper, the finite element model was used to compare the influence of the underground separation wall protective measures on the deformation and internal force of adjacent existing tunnel. It was found that the foundation pit bottom presented a tendency of large deformation in the middle and small deformation on both sides. Based on the principle of soil-structure interaction, the optimization study of the underground separation wall was carried out, and it was found that the excessive thickness has little contribution to the reduction of lateral displacement.

There are many strongly developed karsts in South China, which bring great uncertainty to the construction stability of underground projects. This paper takes the deep foundation pit project of a station in a sandy soil area as the background, simulates the deep foundation pit excavation process based on finite element software, and comparatively analyzes the influence of the existence of karst holes on the foundation pit support structure. The results show that the effective treatment of solution holes in the process of foundation pit excavation can reduce the risk of surface settlement around the foundation pit but has less influence on the lateral displacement of the foundation pit support structure. The cavity grouting reinforcement technology is also introduced in detail in the paper, which can provide a reference for the reinforcement of deep foundation pit projects in sandy soil stratum containing strong development of large cavities.

PurposeThe outbreak of COVID-19 pandemic has posed severe challenges to infrastructure construction in China. Particularly, the complex technology and high process uncertainty of deep foundation pit construction make its safety risk identification a challenging issue of general concern. To address these challenges, Building Information Modeling (BIM) can be used as an important tool to enhance communication and decision-making among stakeholders during the pandemic. The purpose of this study is to propose a knowledge management and BIM-integrated safety risk identification method for deep foundation pit construction to improve the management efficiency of project participants.Design/methodology/approachThis paper proposes a risk identification method that integrates BIM and knowledge management for deep foundation pit construction. In the framework of knowledge management, the topological relationships between objects in BIM are extracted and visualized in the form of knowledge mapping. After that, formal expressions of codes are established to realize the structured processing of specification provisions and special construction requirements. A comprehensive plug-in for deep foundation pit construction is designed based on the BIM software.FindingsThe proposed method was verified by taking a sub-project in deep foundation pit project construction as an example. The result showed the new method can make full use of the existing specification and special engineering requirements knowledge. In addition, the developed visual BIM plug-in proves the feasibility and applicability of the proposed method, which can help to increase the risk identification efficiency and refinement.Originality/valueThe deep foundation pit safety risk identification is challenged by the confusion of deep foundation pit construction safety knowledge and the complexity of the BIM model. By establishing the standardized expression of normative knowledge and special construction requirements, the efficiency and refinement of risk identification are improved while ensuring the comprehensiveness of results. Moreover, the topology-based risk identification method focuses on the project objects and their relations in the way of network, eliminating the problem of low efficiency from the direct BIM-based risk identification method due to massive data.

It was repeated in the traditional design and construction process of the retaining wall and guide wall of diaphragm wall, resulting in prolonged construction period, increased the amount of temporary structural concrete pouring and demolition, and caused data waste and environmental pollution, which does not meet the requirements of green construction. In this paper, based on the deep foundation pit project of the standard underground station of rail transit, the structural form of the guide wall of diaphragm wall is reformed by coordinating the construction sequence of guide wall and retaining wall. The traditional scheme is optimized by calculating the stress on guide wall, which was doubled as a retaining wall. It reduces the dosage of concrete of guide wall and retaining wall engineering 18.16%. Further, we systematically evaluated in the economic and social benefits of the guide wall doubled as a retaining wall in this paper. The results show that the optimized scheme has perfect design function and safe and reliable structure. Compared with the traditional scheme, the optimized scheme can reduce the total cost of related projects by about 24.36% and shorten construction period. The research in this paper has significant engineering economic value and social benefit, which can provide reference for the design and construction of similar projects.

Deep foundation pit support systems are important for reducing construction risks, to ensure the effectiveness and safety of support engineering, so the selection of a suitable support program is the inevitable requirement for the smooth construction of a foundation pit project. In order to improve the rationality of the support scheme, the analytic hierarchy process and the improved Entropy method are comprehensively used to determine the subjective and objective weights of the indexes, and the comprehensive weights are corrected based on the idea of game theory. Subsequently, fuzzy comprehensive evaluation is used for scheme selection, thereby constructing a model for optimizing deep foundation pit support schemes. The model is applied to a municipal pipe gallery project in Area A and the optimal support scheme is determined to be the soil nail wall and supporting piles and anchor ropes. The safety of the support scheme and the effectiveness of the selection model are verified through simulation and construction monitoring. Practice has proved the applicability and superiority of the model in dealing with construction projects characterized by ambiguity and insufficient data. In addition, the advantages and disadvantages of the mainstream evaluation methods of the current deep foundation pit support selection, applicable situations, and the influence mechanism of the geological environment are discussed in this paper, which helps to establish a more comprehensive framework for the selection of the support schemes.

In the course of the construction of deep foundation pits during the winter in seasonally frozen areas, the pit wall soil is often unstable due to frost heave and thawing settlement, which leads to hidden safety hazards in engineering construction. Based on the analysis of the deformation data of a pile-anchor supporting a deep foundation pit in Harbin obtained from monitoring during the winter, the influence of freezing and thawing cycles was investigated. The results show that the horizontal displacement in the middle of the shallow layer of the foundation pit is significantly larger than that on both sides during the freeze–thaw cycles, and the spatial effect becomes noticeable. The stress concentration at the external corner of the foundation pit, coupled with the effects of atmospheric precipitation and freeze–thaw cycles, led to the maximum growth rate of horizontal displacement up to 1.40 mm·day . The external corner effect is evident from 1 m in the shallow layer of the pit to the depth H/2 of the foundation pit. The support scheme is generally feasible, and we can appropriately enhance the support of the shallow layer of the foundation pit during the freeze–thaw cycles. For similar projects experiencing freeze–thaw cycles, the safety reserve can be appropriately enhanced when carrying out support design.

As a key guarantee and cornerstone of building quality, the importance of deformation prediction for deep foundation pits cannot be ignored. However, the deformation data of deep foundation pits have the characteristics of nonlinearity and instability, which will increase the difficulty of deformation prediction. In response to this characteristic and the difficulty of traditional deformation prediction methods to excavate the correlation between data of different time spans, the advantages of variational mode decomposition (VMD) in processing non-stationary series and a gated cycle unit (GRU) in processing complex time series data are considered. A predictive model combining particle swarm optimization (PSO), variational mode decomposition, and a gated cyclic unit is proposed. Firstly, the VMD optimized by the PSO algorithm was used to decompose the original data and obtain the Internet Message Format (IMF). Secondly, the GRU model optimized by PSO was used to predict each IMF. Finally, the predicted value of each component was summed with equal weight to obtain the final predicted value. The case study results show that the average absolute errors of the PSO-GRU prediction model on the original sequence, EMD decomposition, and VMD decomposition data are 0.502 mm, 0.462 mm, and 0.127 mm, respectively. Compared with the prediction mean square errors of the LSTM, GRU, and PSO-LSTM prediction models, the PSO-GRU on the PTB0 data of VMD decomposition decreased by 62.76%, 75.99%, and 53.14%, respectively. The PTB04 data decreased by 70%, 85.17%, and 69.36%, respectively. In addition, compared to the PSO-LSTM model, it decreased by 8.57% in terms of the model time. When the prediction step size increased from three stages to five stages, the mean errors of the four prediction models on the original data, EMD decomposed data, and VMD decomposed data increased by 28.17%, 3.44%, and 14.24%, respectively. The data decomposed by VMD are more conducive to model prediction and can effectively improve the accuracy of model prediction. An increase in the prediction step size will reduce the accuracy of the deformation prediction. The PSO-VMD-GRU model constructed has the advantages of reliable accuracy and a wide application range, and can effectively guide the construction of foundation pit engineering.

This paper aims to investigate the stress and deformation characteristics of double-row piles in deep foundation pit excavation by selecting representative strata in the Changchun area. The changes in bending moment, displacement, and earth pressure of double-row piles during the excavation are established using the FALC 3D software. The findings indicate that the deformation of the front pile is larger than that of the back pile, and the earth pressure distribution of the fornt pile deviates from the conventional Rankine earth pressure theory. Moreover, there are noticeable variations in earth pressure at the soil layer interface of the rear pile.

Monitoring data from deep foundation pits exhibit significant nonlinear, nonstationary, and spatiotemporal coupling characteristics. Traditional methods struggle to simultaneously characterize their spatial correlations and temporal evolution patterns. To address these issues, on the basis of measured data from a typical deep foundation pit project in Beijing, this paper proposes a spatiotemporal feature-fused CNN-LSTM-Transformer prediction model for support pile deformation. By constructing a spatiotemporal matrix of monitoring data, the model achieves the synergistic fusion of spatial feature extraction, temporal dependency modeling, and global correlation perception. The results of the comparative analysis indicate that the proposed model demonstrates stable predictive performance across different pile locations and operating conditions. Its root mean square error (RMSE) and mean absolute error (MAE) are reduced by approximately 20% compared with those of the CNN and CNN-LSTM models. Particularly in areas with severe deep deformation and during high-fluctuation stages, the model effectively mitigates prediction lag and error accumulation, demonstrating a superior response capability to local abrupt changes. The findings suggest that this method can provide a reliable data-driven approach for the dynamic prediction of support structure deformation during deep foundation pit construction.