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settlement prediction for Buildings Surrounding Foundation Pits Based on a Stationary Auto-regression Model

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Journal of China University of Mining and Technology 2007年第1期17卷 78-81页

作者： TIAN Lin-ya HUA Xi-sheng College of Civil Engineering Hohai University Nanjing Jiangsu 210098 China

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.

关键词： foundation pit building settlement monitoring datum stability stationary auto-regression model settlement prediction

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

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Frontiers of Structural and Civil Engineering 2023年第12期17卷 1934-1948页

作者： Yu DIAO Yiming XUE Weiqiang PAN Gang ZHENG Ying ZHANG Dawei ZHANG Haizuo ZHOU Tianqi ZHANG Key Laboratory of Coastal Civil Engineering Structure and Safety of Ministry of Education Tianjin UniversityTianjin 300072China Department of Civil Engineering Tianjin UniversityTianjin 300072China Shanghai Tunnel Engineering Construction Co. Ltd.Shanghai 200032China Tianjin Municipal Engineering Design&Research Institute Tianjin 300392China

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 ***,the pipe roofing method has rarely been applied in soft ground,where the prediction and control of the ground settlement play important *** study proposes a sliced-soil-beam(SSB)model to predict the settlement of ground due to tunnelling using the pipe roofing method in soft *** model comprises a sliced-soil module based on the virtual work principle and a beam module based on structural *** part of this work,the Peck formula was modified for a square-section tunnel and adopted to construct a deformation mechanism of soft *** pipe roofing system was simplified to a threedimensional Winkler beam to consider the interaction between the soil and pipe *** model was verified in a case study conducted in Shanghai,China,in which it provided the efficient and accurate prediction of ***,the parameters affecting the ground settlement were *** was clarified that the stiffness of the excavated soil and the steel support are the key factors in reducing ground settlement.

关键词： pipe roofing method soft ground numerical simulation settlement prediction simplified calculation parametric analysis

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Efficient machine learning model for settlement prediction of large diameter helical pile in c—Φsoil

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AI in Civil Engineering 2024年第1期3卷 236-261页

作者： Nur Mohammad Shuman Mohammad Sadik Khan Farshad Amini Department of Civil and Environmental Engineering Jackson State University1400 J.R.Lynch StreetJSU Box 17068JacksonMS 39217‑0168USA

Machine learning is frequently used in various geotechnical applications *** study presents a statistics and machine learning model for settlement prediction of helical piles that relates compressive service load and soil parameters as a group with the pile *** learning algorithms such as Decision Trees,Random Forests,AdaBoost,and Artificial Neural Networks(ANN)were used to develop the predictive *** models were validated using cross-validation techniques and tested on an independent dataset to assess their accuracy and *** investigation is used here to supplement the field data by simulating various soil conditions and pile geometries that have not been tested in the *** study compiled numerical results of 3600 *** the models are well-calibrated and validated,the data from these models can be reasonably assumed to simulate the ground *** the end of this study,a comparative analysis of statistic learning and machine learning(ML)was done using the field axial load tests database and numerical investigation on helical *** is observed that ML models like Decision Trees and Random Forests provided the better model with R-squared values of 0.92 and 0.96,respectively,for large *** authors believe this study will permit engineers and state agencies to understand this prediction model’s efficacy better,resulting in a more resilient approach to designing large-diameter helical piles for the compressive load.

关键词： Large diameter Helical pile Numerical analysis Machine learning model settlement prediction

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

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第1期16卷 317-332页

作者： Qi Zhang Qian Su Zongyu Zhang Zhixing Deng De Chen School of Civil Engineering Southwest Jiaotong UniversityChengduChina MOE Key Laboratory of High Speed Railway Engineering Southwest Jiaotong UniversityChengduChina

Reliable long-term settlement prediction of a high embankment relates to mountain infrastructure *** 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)***-embankment field measurements were preprocessed using the joint denoising technique,which in-cludes complete ensemble empirical mode decomposition,singular value decomposition,and wavelet packet ***,high-embankment settlements were predicted using the EGWO-n-SVR *** this method,the standard gray wolf optimizer(GWO)was improved to obtain the EGWO to better tune the n-SVR model *** proposed NHM was then tested in two case ***,the influences of the data division ratio and kernel function on the EGWO-n-SVR forecasting performance and prediction efficiency were *** results indicate that the NHM suppresses noise and restores details in high-embankment field ***,the NHM out-performs other alternative prediction methods in prediction accuracy and *** demonstrates that the proposed NHM is effective in predicting high-embankment settlements with noisy field ***,the appropriate data division ratio and kernel function for EGWO-n-SVR are 7:3 and radial basis function,respectively.

关键词： High embankment settlement prediction Joint denoising technique Enhanced gray wolf optimizer Support vector regression

A simplified method for prediction of embankment settlement in clays

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Journal of Rock Mechanics and Geotechnical Engineering 2014年第1期6卷 61-66页

作者： Chunlin Li Institute of Civil Engineering Tongling University

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,*** initial time *** difference of the initial time point determined by different designers can de?nitely induce errors in prediction of embankment *** paper proposed a concept named"potential settlement"and a simpli?ed method based on the in situ *** key parameter"b"in the proposed method was veri?ed using theoretical method and?eld ***,an example was used to demonstrate the advantages of the proposed method by comparing with other methods and the observation data.

关键词： Simplified method settlement prediction Embankment Consolidation theory Clayey soil

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

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Water Science and Engineering 2023年第4期16卷 390-398页

作者： Yan-long Li Qiao-gang Yin Ye Zhang Heng Zhou State Key Laboratory of Eco-hydraulics in Northwest Arid Region Xi'an University of TechnologyXi'an 710048China Power China Northwest Engineering Corporation Limited Xi'an 710065China

The unique structure and complex deformation characteristics of concrete face rockfill dams(CFRDs)create safety monitoring *** 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 *** 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 ***,a deformation prediction model of CFRDs was established using the IRF *** ten-fold cross-validation method was used to quantitatively analyze the parameters affecting the IRF *** 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 *** 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 *** 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 *** study has developed a novel method for forecasting and analyzing dam settlements with practical ***,the established IRF model can also provide guidance for modeling health monitoring of other structures.

关键词： Dam health monitoring M5'model tree IRF Monitoring models settlement prediction

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

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Railway Engineering Science 2020年第2期28卷 129-145页

作者： Shunhua Zhou Binglong Wang Yao Shan Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji UniversityShanghai 200092China Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety Tongji UniversityShanghai 200092China

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 *** 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 *** this work,the significant issues of high-speed railway subgrades in soft soil regions are *** theoretical and experimental studies on foundation treatment methods for ballasted and ballastless tracks are *** settlement evolution and the settlement control effect of different treatment methods are *** technologies of subgrade differential settlement are subsequently briefly *** calculation algorithms of foundations reinforced by different treatment methods are discussed in *** defects of existing prediction methods and the challenges faced in their practical applications are ***,the guidance on future improvement in control theories and technologies of subgrade settlement for high-speed railway lines and the corresponding challenges are provided.

关键词： High-speed railway Subgrade Soft soil foundation Pile-supported embankment Soil arching settlement prediction

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

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Structural Durability & Health Monitoring 2024年第3期18卷 341-360页

作者： Baojun Qin Guangwei Zhang Wei Zhang CCCC Rail Transit Branch Beijing101300China School of Civil Engineering Fujian University of TechnologyFuzhou350118China

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.

关键词： Shield construction complex strata finite element method mechanical properties of surrounding rock segment deformation settlement prediction