Settlement Prediction for Buildings Surrounding Foundation Pits Based on a Stationary Auto-regression Model

To ensure the safety of buildings surrounding foundation pits, a study was made on a settlement monitoring and trend prediction method. A statistical testing method for analyzing the stability of a settlement monitoring datum has been discussed. According to a comprehensive survey, data of 16 stages at operating control point, were verified by a standard t test to determine the stability of the operating control point. A stationary auto-regression model, AR(p), used for the observation point settlement prediction has been investigated. Given the 16 stages of the settlement data at an observation point, the applicability of this model was analyzed. Settlement of last four stages was predicted using the stationary auto-regression model AR (1); the maximum difference between predicted and measured values was 0.6 mm, indicating good prediction results of the model. Hence, this model can be applied to settlement predictions for buildings surrounding foundation pits.

Settlement Prediction of Dredger Fill with the Optimal Combination Model

Post-construction settlement has gained increasing attention because it frequently causes engineering problems. A combined model is a commonly used prediction model that overcomes the difficulty of a single model( i. e., cannot reflect various regulations of settlement at some stages or the entire process). In this study,the correlation coefficient,maximum error values,and other values were obtained according to the fitting and predicted results of a single model. The coefficient of variation was then introduced to determine the weight of each model forming the combination. The proposed model was used to fit and predict for settlement and overcome the issue of utilizing a single model while determining the weight. The fitting predictive effect was also analyzed using the settlement fitting precision results. The fitting precision of optimizing the combination model is high. The predicted data of the post-construction settlement are closer to the calculated value of the settlement monitoring data. Moreover,the proposed model has good practicability,does not require the interval data of settlement,and restricts the model number. Thus,this model can be applied in the engineering field.

Settlement prediction model of slurry suspension based on sedimentation rate attenuation

This paper introduces a slurry suspension settlement prediction model for cohesive sediment in a still water environment. With no sediment input and a still water environment condition, control forces between settling particles are significantly different in the process of sedimentation rate attenuation, and the settlement process includes the free sedimentation stage, the log-linear attenuation stage, and the stable consolidation stage according to sedimentation rate attenuation. Settlement equations for sedimentation height and time were established based on sedimentation rate attenuation properties of different sedimentation stages. Finally, a slurry suspension settlement prediction model based on slurry parameters was set up with a foundation being that the model parameters were determined by the basic parameters of slurry. The results of the settlement prediction model show good agreement with those of the settlement column experiment and reflect the main characteristics of cohesive sediment. The model can be applied to the prediction of cohesive soil settlement in still water environments.

Prediction of high-embankment settlement combining joint denoising technique and enhanced GWO-v-SVR method

Reliable long-term settlement prediction of a high embankment relates to mountain infrastructure safety.This study developed a novel hybrid model(NHM)that combines a joint denoising technique with an enhanced gray wolf optimizer(EGWO)-n-support vector regression(n-SVR)method.High-embankment field measurements were preprocessed using the joint denoising technique,which in-cludes complete ensemble empirical mode decomposition,singular value decomposition,and wavelet packet transform.Furthermore,high-embankment settlements were predicted using the EGWO-n-SVR method.In this method,the standard gray wolf optimizer(GWO)was improved to obtain the EGWO to better tune the n-SVR model hyperparameters.The proposed NHM was then tested in two case studies.Finally,the influences of the data division ratio and kernel function on the EGWO-n-SVR forecasting performance and prediction efficiency were investigated.The results indicate that the NHM suppresses noise and restores details in high-embankment field measurements.Simultaneously,the NHM out-performs other alternative prediction methods in prediction accuracy and robustness.This demonstrates that the proposed NHM is effective in predicting high-embankment settlements with noisy field mea-surements.Moreover,the appropriate data division ratio and kernel function for EGWO-n-SVR are 7:3 and radial basis function,respectively.

A 3D sliced-soil-beam model for settlement prediction of tunnelling using the pipe roofing method in soft ground

The pipe roofing method is widely used in tunnel construction because it can realize a flexible section shape and a large section area of the tunnel,especially under good ground conditions.However,the pipe roofing method has rarely been applied in soft ground,where the prediction and control of the ground settlement play important roles.This study proposes a sliced-soil-beam(SSB)model to predict the settlement of ground due to tunnelling using the pipe roofing method in soft ground.The model comprises a sliced-soil module based on the virtual work principle and a beam module based on structural mechanics.As part of this work,the Peck formula was modified for a square-section tunnel and adopted to construct a deformation mechanism of soft ground.The pipe roofing system was simplified to a threedimensional Winkler beam to consider the interaction between the soil and pipe roofing.The model was verified in a case study conducted in Shanghai,China,in which it provided the efficient and accurate prediction of settlement.Finally,the parameters affecting the ground settlement were analyzed.It was clarified that the stiffness of the excavated soil and the steel support are the key factors in reducing ground settlement.

Shield Excavation Analysis: Ground Settlement & Mechanical Responses in Complex Strata

This study delves into the effects of shield tunneling in complex coastal strata, focusing on how this constructionmethod impacts surface settlement, the mechanical properties of adjacent rock, and the deformation of tunnelsegments. It investigates the impact of shield construction on surface settlement, mechanical characteristics ofnearby rock, and segment deformation in complex coastal strata susceptible to construction disturbances. Utilizingthe Fuzhou Binhai express line as a case study, we developed a comprehensive numerical model using theABAQUS finite element software. The model incorporates factors such as face force, grouting pressure, jack force,and cutterhead torque. Its accuracy is validated against field monitoring data from engineering projects. Simulationswere conducted to analyze ground settlement and mechanical changes in adjacent rock and segments acrossfive soil layers. The results indicate that disturbances are most significant near the excavation zone of the shieldmachine, with a prominent settlement trough forming and stabilizing around 2.0–3.0 D from the excavation. Theexcavation face compresses the soil, inducing lateral expansion. As grouting pressure decreases, the segmentexperiences upward buoyancy. In mixed strata, softer layers witness increased cutting, intensifying disturbancesbut reducing segment floatation. These findings offer valuable insights for predicting settlements, ensuring segmentand rock safety, and optimizing tunneling parameters.

Short-term tunnel-settlement prediction based on Bayesian wavelet:a probability analysis method

As urbanization accelerates,the metro has become an important means of transportation.Considering the safety problems caused by metro construction,ground settlement needs to be monitored and predicted regularly,especially when a new metro line crosses an existing one.In this paper,we propose a settlement-probability prediction model with a Bayesian emulator(BE)based on the Gaussian prior(GP),that is,a GPBE.In addition,considering the distortion characteristics of monitoring data,the data is denoised using wavelet decomposition(WD),so the final prediction model is WD-GPBE.In particular,the effects of different prediction ratios and moving windows on prediction performance are explored,and the optimal number of moving windows is determined.In addition,the predicted value for GPBE based on the original data is compared with the predicted value for WD-GPBE based on the denoised data.One year of settlement-monitoring data collected by a structural health monitoring(SHM)system installed on the Nanjing Metro is used to demonstrate the effectiveness of WDGPBE and GPBE for predicting settlement.

A simplified method for prediction of embankment settlement in clays

The prediction of embankment settlement is a critically important issue for the serviceability of subgrade projects,especially the post-construction settlement.A number of methods have been proposed to predict embankment settlement;however,all of these methods are based on a parameter,i.e.the initial time point.The difference of the initial time point determined by different designers can de?nitely induce errors in prediction of embankment settlement.This paper proposed a concept named"potential settlement"and a simpli?ed method based on the in situ data.The key parameter"b"in the proposed method was veri?ed using theoretical method and?eld data.Finally,an example was used to demonstrate the advantages of the proposed method by comparing with other methods and the observation data.

Deformation prediction model of concrete face rockfill dams based on an improved random forest model

The unique structure and complex deformation characteristics of concrete face rockfill dams(CFRDs)create safety monitoring challenges.This study developed an improved random forest(IRF)model for dam health monitoring modeling by replacing the decision tree in the random forest(RF)model with a novel M5'model tree algorithm.The factors affecting dam deformation were preliminarily selected using the statistical model,and the grey relational degree theory was utilized to reduce the dimensions of model input variables.Finally,a deformation prediction model of CFRDs was established using the IRF model.The ten-fold cross-validation method was used to quantitatively analyze the parameters affecting the IRF algorithm.The performance of the established model was verified using data from three specific measurement points on the Jishixia dam and compared with other dam deformation prediction models.At point ES-10,the performance evaluation indices of the IRF model were superior to those of the M5'model tree and RF models and the classical support vector regression(SVR)and back propagation(BP)neural network models,indicating the satisfactory performance of the IRF model.The IRF model also outperformed the SVR and BP models in settlement prediction at points ES2-8 and ES4-10,demonstrating its strong anti-interference and generalization capabilities.This study has developed a novel method for forecasting and analyzing dam settlements with practical significance.Moreover,the established IRF model can also provide guidance for modeling health monitoring of other structures.

Review of research on high-speed railway subgrade settlement in soft soil area

Construction issues of high-speed rail infrastructures have been increasingly concerned worldwide,of which the subgrade settlement in soft soil area becomes a particularly critical problem.Due to the high compressibility and low permeability of soft soil,the post-construction settlement of the subgrade is extremely difficult to control in these regions,which seriously threatens the operation safety of high-speed trains.In this work,the significant issues of high-speed railway subgrades in soft soil regions are discussed.The theoretical and experimental studies on foundation treatment methods for ballasted and ballastless tracks are reviewed.The settlement evolution and the settlement control effect of different treatment methods are highlighted.Control technologies of subgrade differential settlement are subsequently briefly presented.Settlement calculation algorithms of foundations reinforced by different treatment methods are discussed in detail.The defects of existing prediction methods and the challenges faced in their practical applications are analyzed.Furthermore,the guidance on future improvement in control theories and technologies of subgrade settlement for high-speed railway lines and the corresponding challenges are provided.

Ground movement induced by triple stacked tunneling with different construction sequences

This study tried to explore the ground movement induced by triple stacked tunneling(TST) with different construction sequences. A case study in Tianjin, China was used to investigate the ground movement during the TST(upper tunneling(UT)). For this, a modified Peck formula was proposed to predict the surface settlement induced by TST. Next, three sets of finite element analyses(FEA) were used to compare the effects of construction sequences(i.e. UT, middle tunneling(MT), and lower tunneling(LT)) on vertical and lateral ground displacements. The results of Tianjin case and UT reveal that compared to a Gaussian distribution for a single tunnel, the surface settlement curve of triple stacked tunnels is a bimodal distribution. It seems that the proposed modified Peck formula can effectively predict the surface settlement induced by TST. The results of the three sets of FEA demonstrate that the construction sequence has a significant influence on the ground movement. Among the three construction sequences, the largest lateral displacement is observed in the MT and the smallest one in UT.The existing tunnel has an inhibitory effect on the vertical displacement. The maximum value of the lateral displacement occurs at the depth of the new tunnel in each construction sequence.