Deformation Of Foundation Pit Research Articles

Force and Deformation Characteristics of Large-Scale Zoning Excavation in Soft Soil: A Case Study in Hangzhou

The zoning excavation method is fully employed to control the deformation of foundation pits constructed in urban soft soil areas. However, the similarities and differences in forces and deformations between foundation pits excavated by the zonal method and those excavated by the conventional method still need to be further explored. In this study, the deformation was monitored and analyzed by taking the zonal excavation of a foundation pit of the ‘New World’ project in Hangzhou City as the research object. The measured results showed that the pre-built diaphragm wall for the first excavated foundation pit restricted the deformation of the first excavated diaphragm wall. The presence of extensive construction and unloading activities also changed the deformation pattern of the soil. Further, finite element simulations were carried out. The simulation results revealed that excavating the foundation pit first caused displacements in the pre-built diaphragm wall. The displacements transmitted by non-adjacent pits through the pre-built diaphragm wall were small and were concentrated at the junction of the two sub-pits. Adjacent foundation pits caused large displacements of the pre-built diaphragm wall with similar displacement patterns. The results of the study can provide effective guidance for foundation pit excavation in soft soil areas in the future.

Failure Mechanism and Control Mechanism of Intermittent Jointed Rock Bridge Based on Acoustic Emission (AE) and Digital Image Correlation (DIC).

Deep foundation pit excavation is an important way to develop underground space in congested urban areas. Rock bridges prevent the interconnection of joints and control the deformation and failure of the rock mass caused by excavation for foundation pits. However, few studies have considered the acoustic properties and strain field evolution of rock bridges. To investigate the control mechanisms of rock bridges in intermittent joints, jointed specimens with varying rock bridge length and angle were prepared and subjected to laboratory uniaxial compression tests, employing acoustic emission (AE) and digital image correlation (DIC) techniques. The results indicated a linear and positive correlation between uniaxial compressive strength and length, and a non-linear and negative correlation with angle. Moreover, AE counts and cumulative AE counts increased with loading, suggesting surges due to the propagation and coalescence of wing and macroscopic cracks. Analysis of RA-AF values revealed that shear microcracks dominated the failure, with the ratio of shear microcracks increasing as length decreased and angle increased. Notably, angle exerted a more significant impact on the damage form. As length diminished, the failure plane's transition across the rock bridge shifted from a complex coalescence of shear cracks to a direct merger of only coplanar shear cracks, reducing the number of tensile cracks required for failure initiation. The larger the angle, the higher the degree of coalescence of the rock bridge and, consequently, the fewer tensile cracks required for failure. The decrease of length and the increase of angle make rock mass more fragile. The more inclined the failure mode is to shear failure, the smaller the damage required for failure, and the more prone the areas is to rock mass disaster. These findings can provide theoretical guidance for the deformation and control of deep foundation pits.

Research on Deformation Safety Risk Warning of Super-Large and Ultra-Deep Foundation Pits Based on Long Short-Term Memory

This paper proposes transforming actual monitoring data into risk quantities and establishing a Long Short-Term Memory (LSTM) safety risk warning model for predicting the deformation of super-large and ultra-deep foundation pits in river–round gravel strata based on safety evaluation methods. Using this model, short-term deformation predictions at various monitoring points of the foundation pits are made and compared with monitoring data. The results from the LSTM safety risk warning model indicate an absolute error range between the predicted deformation values and on-site monitoring values of −0.24 to 0.16 mm, demonstrating the model’s accuracy in predicting pit deformation. Additionally, calculations reveal that both the overall risk level based on on-site monitoring data and the overall safety risk level based on predicted data are classified as level four. The acceptance criteria for the overall risk level of the foundation pit are defined as “unacceptable and requiring decision-making”, with the risk warning control scheme being “requiring decision-making, formulation of control, and warning measures”. These research findings offer valuable insights for predicting and warning about safety risks in similar foundation pit engineering projects.

Deformation and reinforcement effects of soft soil deep foundation pits for shield tunnel launching

Deformation and reinforcement effects of soft soil deep foundation pits for shield tunnel launching

Study on Influence of Tension Height of Steel Strand on Displacement of Foundation Pit Supported by Fish-bellied beams

The steel support system of prestressed fish-bellied beams can reliably control the deformation of foundation pit and provide open excavation space for foundation pit excavation, in which the triangle plate plays an important role in the fish-bellied beams system. But the deformation of the triangle plate and the steel strand is too large is a common problem in the foundation pit support, through the analysis of the stability of the triangle plate, it is found that the tension point height is an important factor leading to the deformation of too large. Through MIDAS/GEN analysis, it is found that the increase of the tension point height of the strand is conducive to the control of the displacement of the connecting part, and with the increase of the tension point of the strand, the anti-impact stability of the fish-bellied beams system is gradually enhanced. Therefore, by changing the tension height of the steel strand, the supporting system of the fish-bellied beams is optimized.

Machine-Learning-Based Deformation Prediction Method for Deep Foundation-Pit Enclosure Structure

During the construction of deep foundation pits in subways, it is crucial to closely monitor the horizontal displacement of the pit enclosure to ensure stability and safety, and to reduce the risk of structural damage caused by pit deformations. With advancements in machine-learning (ML) techniques and correlation analysis in engineering, data-driven methods that combine ML with engineering monitoring data have become increasingly popular. These methods offer benefits such as high prediction accuracy, efficiency, and cost effectiveness. The main goal of this study was to develop a machine-learning method for predicting the enclosure deformation of deep foundation pits. This was achieved by analyzing the factors influencing deep foundation-pit enclosure deformation and incorporating historical cases and monitoring reports. The performance of each machine-learning prediction model was systematically analyzed and evaluated using K-Fold cross validation. The results revealed that the random forest model outperformed the other models. The result of the test data showed that the random forest model achieved an R2 of 0.9905, an MAE of 0.8572 mm, and an RMSE of 1.9119 mm. Feature importance analysis identified the depth of enclosure structure, water level, surface settlement, axial force, and exposure time as the most critical factors for accurate prediction. The depth of the enclosure structure had an especially significant impact on the prediction of enclosure deformation.

Prediction of retaining structure deformation of ultra-deep foundation pit by empirical mode decomposition with recurrent neural networks

Prediction of retaining structure deformation of ultra-deep foundation pit by empirical mode decomposition with recurrent neural networks

Experimental Study of Hardening Small Strain Model Parameters for Strata Typical of Zhengzhou and Their Application in Foundation Pit Engineering

The hardening small strain (HSS) model, which considers the non-linear characteristics of the shear modulus of soil in the small strain range, is commonly utilized in the numerical analysis of excavations in sensitive environments. In order to ascertain the parameters of an HSS model for strata typical of Zhengzhou, an extensive series of laboratory tests was executed. Subsequently, a statistical analysis was utilized to establish the relationships between the principal parameters. A three-dimensional finite element model was established based on the excavation of representative strata in Zhengzhou. The accuracy and applicability of the model parameters were verified using the field measurement data. In addition, an analysis of the sensitivity of the main HSS model parameters to the foundation pit deformation was also carried out. The results show that the HSS model can analyze excavation deformation with greater precision than the hardening soil (HS) model or the Mohr–Coulomb (MC) model. The small strain parameters G0ref and γ0.7 have a great influence on the horizontal displacement of the support and the vertical displacement of soil behind the wall, respectively. The research results serve as a reliable foundation for both the analysis of excavation deformation and the determination of HSS model parameters for strata typical of Zhengzhou.

Construction pit deformation measurement technology based on neural network algorithm

Abstract The current technology of foundation pit deformation measurement is inefficient, and its accuracy is not ideal. Therefore, an intelligent prediction model of foundation pit deformation based on back propagation neural network (BPNN) is proposed to predict the foundation pit deformation intelligently, with high accuracy and efficiency, so as to improve the safety of the project. Firstly, to address the shortcomings of BPNNs, which rely on the initial parameter settings and tend to fall into local optimum and unstable performance, this study adopts the modified particle swarm optimization (MPSO) to optimise the parameters of BPNNs and constructs a pit deformation prediction model based on the MPSO–BP algorithm to achieve predictive measurements of pit deformation. After training and testing the data samples, the results show that the prediction accuracy of the MPSO–BP pit deformation prediction model is 99.76%, which is 2.25% higher than that of the particle swarm optimization–back propagation (PSO–BP) pit deformation prediction model and 3.01% higher than that of the BP pit deformation prediction model. The aforementioned results show that the MPSO–BP pit deformation prediction model proposed in this study can effectively predict the pit deformation variables of construction projects and provide data support for the protective measures of the staff, which is helpful for the cause of construction projects in China.

Application of soil parameter inversion method based on BP neural network in foundation pit deformation prediction

Application of soil parameter inversion method based on BP neural network in foundation pit deformation prediction

Study of Improved Grey BP (Back Propagation) Neural Network Combination Model for Predicting Deformation in Foundation Pits

Deep excavation engineering is a comprehensive discipline that involves multiple fields such as engineering geology, hydrogeology, and foundation engineering. With the improvement of the utilization rate of underground space, the demand for the construction of large-scale underground structural engineering is growing, making the excavation of underground soil become increasingly frequent, which also brings about the safety problems of deep foundation pit engineering and the surrounding environment. Prediction of foundation pit deformation is an important research direction with diverse historical developments, but it is also facing a series of difficulties and challenges. In order to solve these problems, this article proposes an improvement plan, establishes a prediction model based on the combination model of grey BP (back propagation) neural network, and verifies its effectiveness through experiments. The results show that the average error of the new model’s prediction of horizontal deformation is about 0.31, which is about 32% lower than the traditional model’s prediction error. The difference between the vertical deformation prediction and actual monitoring results is also controlled. The vertical deformation predicted by wavelet transform is 7% to 9% larger than the actual monitoring results, meeting the prediction requirements. Finally, this article explores the research on the prediction of foundation pit deformation in deep excavation engineering, An improved grey BP neural network combination model was proposed and its effectiveness was verified through experiments. This article has important reference value for the study of deformation prediction in deep excavation engineering.

Fluid-Solid Coupling Effect on Numerical Simulation of Deep Foundation Pit Deformation in Soft Soil Area

Groundwater is abundant in soft soil areas, which has a significant impact on the excavation deformation of foundation pits. In this paper, based on the monitoring of deep foundation pits with waterproof curtains in Shanghai deep foundation pits, COMSOL Multiphysics is used to model the layers of the foundation pits and establish a two-dimensional seepage-consolidation coupled model for hierarchical dewatering excavation. The feasibility of numerical simulation of regional foundation pits, the modeling method of the foundation pit seepage model is explored, and the presence or absence of waterproof curtains, and the influence of aquitards on the horizontal displacement of foundation pits and surface settlement outside the pits is analyzed. The research shows that the simulated foundation pit deformation values are in good agreement with the actual monitoring values and that the effect of dewatering and seepage has a great influence on the foundation pit deformation. The waterproof curtain has a significant effect on reducing the drop in the water level outside the pit and controlling the surface settlement. After installing a waterproof curtain, the amount of ground settlement is reduced, but the disadvantage is that the deformation of the enclosure structure increases. Finally, the influence of aquitard on the deformation of foundation pit excavation is simulated, and the distribution characteristics of the flow network diagram under different permeability coefficients are analyzed. According to the analysis of the foundation pit deformation law and flow network diagram, it is considered that the waterproof curtain can effectively reduce the influence of aquitard on foundation pit deformation.

Dynamic back analysis of soil deformation during the construction of deep cantilever foundation pits

Field monitoring of foundation pits alone cannot predict the future deformation of retaining structures. Numerical simulations can predict the deformation of foundation pits and the working state of retaining structures to avoid the risk of foundation pit damage in advance. Accurate inversion of the soil parameters used for simulation and prediction is a key step. The associated multivariable problem is transformed into a single-variable problem by using the interval influence coefficient. Soil layer weightings and excavation step weightings are introduced and exploited to optimize the calculation process, and the soil parameters are calculated through inversion based on the least squares method. Based on actual engineering, the excavation sequence is regarded as a progressive sequence for back analysis, and the parameters of each soil layer are calculated through dynamic calculations with the excavation process in a cycle comprising inversion, prediction, reinversion and reprediction. The soil parameters after inversion are used to predict the maximum value and the depth of the deep horizontal displacement of the retaining structure, which verified the feasibility of the back-analysis method. Compared with the results before inversion, after the final inversion, t the overall error of section 2 is reduced by 67.24%, the overall error of section 3 is reduced by 40.5%, and the overall error of section 4 is reduced by 35%. The prediction curves are all close to the monitoring displacement curves, which plays a good guiding role and ensures the safe construction of the foundation pit. A new effective idea is proposed for the inverse analysis of the composite formation parameters of the deep foundation pit engineering.

Calculation of Displacement-Dependent Active Earth Pressure for Deep Excavations in Soft Soil

In the urban environment, there are strict control requirements for deep foundation pit deformation, resulting in earth pressure on the flexible supports often being in a nonlimiting state. Therefore, it is important to consider displacement when calculating earth pressure. In this study, lateral unloading stress path triaxial compression tests were performed to investigate the radial stress–strain relationship of soft clay in an active region. Herein, a displacement-dependent earth pressure model is proposed with the assumption of the soil strain distribution in the disturbed area. From the surface of the ground to the deepest part of the support structure, the sufficient active displacement inversed by the proposed model decreased, which confirmed that the earth pressure along the entire wall could not achieve its active conditions simultaneously. The efficacy of the proposed model is demonstrated through a comparison of the predicted earth pressure with the experimental results reported in the published literature.

Research and Application of Deformation Prediction Model for Deep Foundation Pit Based on LSTM

Deep foundation pit is a door with a long history, but it has new disciplines; in this paper, firstly, the modeling method and process of LSTM (long short-term memory) network are discussed in detail, then the optimization algorithm used in the model is described in detail, and the parameter selection methods such as initial learning rate, activation function, and iteration number related to LSTM network training are introduced in detail. LSTM network is used to process the deformation data of deep foundation pit, and random gradient descent, momentum, Nesterov, RMSProp, AdaGmd, and Adam algorithms are selected in the same example for modeling prediction and comparison. Two examples of horizontal displacement prediction of pile and vertical displacement prediction of column in deep foundation pit show that the LSTM network model established by different optimization algorithms has different prediction accuracy, and the LSTM network model established by Adam optimization algorithm has the highest accuracy, which proves that the selection of optimization algorithm plays an important role in LSTM and also verifies the feasibility of LSTM network in the data processing and prediction of deep foundation pit deformation.

Case Study on the Deformation Coupling Effect of a Deep Foundation Pit Group in a Coastal Soft Soil Area

Simultaneous construction of adjacent projects may lead to emergencies in a foundation pit group, which significantly affects the deformation and safety of foundation pits. In this study, the deformation characteristics of a deep foundation pit group and the mutual interactions among the adjacent foundation pits were observed by a monitoring system during excavation. Field data of the foundation pit group, including the lateral deflections of the enclosure pile, the ground subsidence, as well as the vertical column movements, were analyzed and compared with individual excavations in Shanghai. The field data showed that the excavation of the adjacent foundation pit reduced the lateral deformation of the enclosure structure, caused by the reduction of active earth pressure acting on the retaining pile. Furthermore, the foundation pit excavated later caused upward movements of the soil between them. However, the foundation pit excavated earlier had a negligible influence on the vertical column movements of the foundation pit excavated later. Due to the optimized excavation sequence of the deep foundation pit group, the deformation of this special excavation was well controlled.

Intelligent Prediction Model (IPM) of Foundation Pit Displacement Based on Extreme Learning Machine (ELM) and Its Application

In order to effectively predict the dynamic displacement and disaster, according to the analysis of the influencing parameters affecting the deformation of a subway foundation pit supported by piles (walls), the rough set attribute reduction method (RSARM) and the average influence value algorithm (AIVA) are used to simplify the influencing factors of foundation pit deformation. Those simplified factors are taken as the input of the ELM, with the output being the displacement of the foundation pit. Finally, the IPM of foundation pit displacement derived from the ELM is obtained, which is finally used for engineering practice. The results show that it is feasible to simplify the influencing factors of the deformation of the foundation pit by RSARM and AIVA. The proposed IPM of foundation pit displacement has high accuracy and good generalization ability, which can be used for the deformation prediction.

Analysis of the influence of groundwater seepage on the deformation of deep foundation pit with suspended impervious curtain

Taking the deep foundation pit of a metro station with rich water environment as the research background, a three-dimensional model of seepage-stress coupling is established. Numerical simulation is used to study the deformation process and influencing factors of deep foundation pit dewatering and excavation of the station, and the influence of dfferent pre-dewatering conditions and different depths of the water-stop curtain on the deformation of the foundation pit is analyzed. The results show that during the dewatering and excavation of foundation pits, seepage-stress has an obvious coupling effect in dewatering and excavation. The stress concentration position of retaining structure and the maximum displacement of lateral displacement are about 0.5–0.6 excavation depth of pile body. With the depth of precipitation increases, the seepage effect in the soil layer is obviously enhanced, which is not conducive to the stability of the foundation pit.

Analysis of the Deformation Law of Deep and Large Foundation Pits in Soft Soil Areas

Taking a deep excavation in Suzhou soft soils adopting three support schemes as the background, the excavation performance metrics, including the heave and lateral deformation of diaphragm walls, surface vertical deformation, vertical deformation of surrounding buildings, and earth pressure, are thoroughly investigated based on 15 excavation cases collected in the soft soil area of Suzhou. Based on the analysis of monitoring data, some findings were achieved: the foundation pit deformation is greatly affected by the spatial effect. The existing station can constrain the foundation pit deformation. Benefiting from the combination of various support solutions, the average maximum deflection of the diaphragm wall is 0.10% He. The maximum lateral movement depth of the diaphragm wall (δhm) is mainly located at (He-7, He+12.5). The vertical deformation of the wall top is greatly affected by the excavation exposure time and soil conditions. The heave range of the wall top is (−0.08∼0.26%) He. Under the action of the displacement of the diaphragm wall to outside the pit and the upward displacement of the wall top, the ground surface is uplifted, and the maximum uplift is (0.02∼0.14%) He, ranging from 0.12δhm to 1.13δhm. The maximum surface settlement is (−0.01% ∼ −0.15%) He, ranging from −0.22δhm to −3.11δhm. The form of building heave is mainly affected by the surface heave and the distance from the diaphragm wall (d). When d is within a certain range, there is a heave settlement difference between the adjacent side and the opposite side of the excavation, and the adjacent side undergoes mostly subsidence, while the opposite side undergoes mostly uplift. The peak value of the apparent earth pressure (AEP) envelope is 0.59γHe, which falls within (0.47∼0.78) He. The calculation scheme proposed by Kim can be used to predict the AEP for multiple soil types.

Soil Pressure and Deformation Analysis of Small Size Deep Foundation Pit

In this paper, the influence of space effect on soil pressure and deformation of deep foundation pit was considered, and the finite soil pressure calculation model was established. The soil pressure of deep foundation pit was calculated by assuming the slip surface and using the finite soil limit equilibrium theory. Then, PLAXIS 3D finite element software was used to establish finite element models of different plane sizes and depths. The distribution regulation of side wall soil pressure and deformation of deep foundation pit was calculated. Finally, the results of finite soil pressure calculation was compared with finite element method. The results shown that: The soil pressure of small deep foundation pit was affected by space effect, and the soil pressure and deformation decrease significantly along the foundation pit depth. Shear fracture Angle was related to the ratio of width to depth of foundation pit, and it was no longer a constant value of 45°+φ/2. Therefore, the spatial effect should be considered in the calculation of soil pressure of small deep foundation pit. The research results can provide some guidance for the design and calculation of similar small size deep foundation pit.