Prediction Of Ground Settlement Research Articles

Ground movement and settlement prediction induced by double-track curvature shield tunneling

Ground movement and settlement prediction induced by double-track curvature shield tunneling

Ground settlement prediction for open caisson shafts in sand using a neural network constrained by empiricism

Accurate prediction of soil settlements induced by open caisson construction in sand is essential for safe and reliable delivery of critical underground urban infrastructure. This paper presents a novel prescriptive design approach using a neural network (NN) constrained by empirical relationships, referred to as an ’empiricism-constrained neural network’. The proposed approach is benchmarked using a traditional closed-form empirical expression. Both methods are calibrated using experimental data from reduced-scale laboratory testing for the prediction of surface and subsurface settlement trough shape and magnitude. The outcomes demonstrate that while both methods accurately capture the primary effect of caisson depth on surface and subsurface soil settlements, the NN approach exhibits superior prediction accuracy. These methods are developed in a form amenable for routine design use in industry and have the potential for broader applicability in other design scenarios, such as building damage assessment and risk-assessment exercises.

Prediction of Ground Settlement of a Shield Tunnel Based on the Maxwell–Betti Power Interchange Theorem

Prediction of Ground Settlement of a Shield Tunnel Based on the Maxwell–Betti Power Interchange Theorem

Transformer based neural network for daily ground settlement prediction of foundation pit considering spatial correlation.

Deep foundation pit settlement prediction based on machine learning is widely used for ensuring the safety of construction, but previous studies are limited to not fully considering the spatial correlation between monitoring points. This paper proposes a transformer-based deep learning method that considers both the spatial and temporal correlations among excavation monitoring points. The proposed method creates a dataset that collects all excavation monitoring points into a vector to consider all spatial correlations among monitoring points. The deep learning method is based on the transformer, which can handle the temporal correlations and spatial correlations. To verify the model's accuracy, it was compared with an LSTM network and an RNN-LSTM hybrid model that only considers temporal correlations without considering spatial correlations, and quantitatively compared with previous research results. Experimental results show that the proposed method can predict excavation deformations more accurately. The main conclusions are that the spatial correlation and the transformer-based method are significant factors in excavation deformation prediction, leading to more accurate prediction results.

Theoretical prediction of ground settlements due to shield tunneling in multi-layered soils considering process parameters

This paper conducts a theoretical analysis of ground settlements due to shield tunneling in multi-layered soils which are usually encountered in urban areas. The proposed theoretical solution which is based on the general form of the Mindlin's solution and Loganathan-Poulos formula can comprehensively consider the in-process tunneling parameters including: unbalanced face pressure, shield-soil friction, unbalanced tail grouting pressure, unbalanced secondary grouting pressure, overloading during tunneling and the ground volume loss. The method is verified by comparing with the field data from the Qinghuayuan Tunnel Project in terms of the ground surface settlements along the longitudinal and transverse direction. Due to the local settlement or heave caused by the certain tunneling parameters, the ground surface settlements calculated using current solution along the longitudinal direction presents an irregular S-shaped curve instead of the traditional S-shaped curve. Results also find that the effect of the unbalanced secondary grouting pressure and the overloading during tunneling cannot be ignored.

Study on Ground Settlement Patterns and Prediction Methods in Super-Large-Diameter Shield Tunnels Constructed in Composite Strata

This study focuses on investigating the surface settlement characteristics induced by the construction of a super-large-diameter shield tunnel in composite strata. By utilizing a combination of field monitoring and numerical simulation analysis, the surface settlement patterns encountered during the construction process in horizontally distributed typical soil–rock composite strata were summarized based on the 16.03 m super-large-diameter shield tunnel project in the southerly extension of He’ping Avenue in Wuhan. In addition, the collected data were used to enhance the Peck empirical formula. The results of the study show the following: (1) Significant non-uniform settlement occurs along the tunneling direction when the shield machine passes through soil–rock composite strata. The range of non-uniform settlement is approximately 3.1 times the tunnel diameter (D) in soil sections and 1.9 times the tunnel diameter (D) in rock sections. (2) The impact of composite strata on the maximum settlement is greater than its effect on the settlement trough width, with a larger impact within the soil sections compared to the rock sections. (3) The Peck correction formula, which takes into account the distance between the monitoring cross-section and the composite interface, provides more accurate predictions than the original Peck empirical formula.

A Study of Prediction of Ground Settlement of Shield Tunnel Based on EEMD-BO-GRU Algorithm

Because the finite element method cannot meet the needs of shield tunnel engineering, multiple linear regression and traditional machine learning algorithms are difficult to effectively solve the shield tunnel’s nonlinear and non-stationary ground surface settlement data. Based on the Gated Recurrent Unit (GRU) algorithm of Ensemble empirical mode decomposition (EEMD) and Bayesian optimization (BO), this paper puts forward a prediction method of ground surface settlement during shield tunneling. Taking the shield tunnel construction of Zhijiang Road in Hangzhou as an example, by comparing with the traditional machine learning algorithm, the results show that RMSE, MAE, and MAPE of this algorithm are increased by 1.9313 mm, 1.7526 mm, and 4.9553%. It can be explained that this method has higher prediction accuracy and a more stable prediction effect, and the EEMD algorithm can effectively improve the generalization ability of the algorithm.

A hybrid data-driven model for geotechnical reliability analysis

• A hybrid data-driven model is proposed to quantify risk under uncertain parameters • during a tunnel's excavation process. • A real case is used to evaluate the effectiveness and feasibility of our proposed • approach. • A Markov-chain-based importance sampling is used to analyze settlement reliability. Tunnel boring machines are widely used to construct underground rail networks in urban areas. However, ground settlement due to complex geological conditions is an ever-present reality requiring continuous monitoring and management of risks. This paper addresses the following research question: How can we predict tunnel-induced ground settlement with engineering parameters, improve its predictive ability, and quantify its risks under uncertain parameters in complex geological conditions ? To this end, we develop a hybrid data-driven model that considers prior domain knowledge to effectively and accurately quantify risk under uncertain parameters during a tunnel's excavation process. Our model comprises: (1) a deep neural network (DNN) to construct a ground settlement prediction model; (2) the incorporation of physical knowledge into the DNN-based prediction model; and (3) a Markov-chain-based importance sampling to analyze settlement reliability. We use the San-yang Road tunnel project in Wuhan, China, to evaluate the effectiveness and feasibility of our proposed approach. The results demonstrate that our hybrid data-driven model can accurately predict tunnel-induced ground settlement and quantify failure probability for geotechnical reliability under uncertain parameters during a tunnel's excavation process.

Hybrid Random Forest-Based Models for Earth Pressure Balance Tunneling-Induced Ground Settlement Prediction

Construction-induced ground settlement is a serious hazard in underground tunnel construction. Accurate ground settlement prediction has great significance in ensuring the surface building’s stability and human safety. To that end, 148 sets of data were collected from the Singapore Circle Line rail traffic project containing seven defining parameters to create a database for predicting ground settlement. These parameters are the tunnel depth (H), the tunnel advance rate (AR), the EPB earth pressure (EP), the mean SPTN value from the soil crown to the surface (Sm), the mean water content of the soil layer (MC), the mean modulus of elasticity of the soil layer (E), and the grout pressure used for injecting grout into the tail void (GP). Three hybrid models consisting of random forest (RF) and three types of meta-heuristics, Ant Lion Optimizier (ALO), Multi-Verse Optimizer (MVO), and Grasshopper Optimization Algorithm (GOA), were developed to predict ground settlement. Furthermore, the mean absolute error (MAE), the mean absolute percentage error (MAPE), the coefficient of determination (R2) and the root mean square error (RMSE) were used to assess predictive performance of the constructed models for predicting ground settlement. The evaluation results demonstrated that the GOA-RF with a population size of 10 has achieved the most outstanding predictive capability with the indices of MAE (Training set: 2.8224; Test set: 2.3507), MAPE (Training set: 40.5629; Test set: 38.5637), R2 (Training set: 0.9487; Test set: 0.9282), and RMSE (Training set: 4.93; Test set: 3.1576). Finally, the sensitivity analysis results indicated that MC, AR, Sm, and GP have a significant impact on ground settlement prediction based on the GOA-RF model.

Numerical Investigation and Prediction of Side-By-Side Tunneling Effects on Buried Pipelines

With the fast development of underground space engineering, it is inevitable for buried pipelines to be crossed by twin tunnels. Previous studies mainly focused on the single-tunneling effects on pipelines. To emphasize the twin-tunneling effects on buried pipelines, we first examined the effectiveness of the ground settlement prediction method under twin-tunneling conditions. Then, the estimated ground settlement boundary condition was applied to the beam-on-spring finite element model. The numerical results show that with the decrease in tunnel depth and twin tunnel space, the values and positions of the maximum ground settlement and longitudinal pipe bending behavior both changed significantly. The biased distance of the maximum settlement position and the distance to the inflection point of the final ground settlement curve can be obtained by curve fitting. Based on that, a semi-empirical prediction method for the longitudinal pipe bending strain was proposed. The predicted values matched quite well with the numerical results, which can thus provide a quick and effective structural safety and integrity assessment approach for buried pipelines subjected to twin-tunneling conditions.

Prediction of ground settlement and liquefaction countermeasure by groundwater lowering using shallow subsurface model in Osaka and Kobe areas

近年，液状化対策として地下水位低下工法の採用事例が増えてきている。大阪・神戸地域は，表層より沖積砂層が緩く堆積し，地下水位も高いため地震時には液状化による被害が予想される。ただし，沖積砂層の下部には過去の地下水汲み上げによって大きな沈下が生じた沖積粘土層が厚く堆積している。関西圏地盤情報データベースおよび独自に実施した地盤調査結果を用いて構築した浅層地盤モデルを用いて，地下水位を低下させた場合の地盤沈下量を予測した。また，現況地下水位および許容沈下量内で地下水位を低下させた場合の液状化予測を行った。その結果，大阪・神戸地域においては地下水位を低下させることにより，液状化対策効果が得られることがわかった。

A practical approach for modeling twin-tunnel excavation in Ho chi minh city

The prediction of ground settlement under tunnel excavation is still challenge. Almost engineer uses Mohr-Coulomb model in practice due to the conventional geotechnical investigation data. This paper describes the study of tunnel lining behaviors and ground surface settlement under tunneling process with a typical case study of twin tunnels excavation in Ho Chi Minh city, Vietnam. The advanced material model namely Hardening Soil model is used to investigate the proposing twin-tunnel with numerical approach. The internal forces of tunnel lining and ground settlement, which achieved from Hardening Soil model and the available results from Mohr-Coulomb model, are then made comparison between two models which yields some important differences for analysis. Since the experimental works for qualifying stiffness parameters in Hardening Soil model are missed in the Metro Line 1 project in Ho Chi Minh city, an empirical formula is proposed in the paper as a guide for estimating the required data in modelling process

Application of regularized ELM optimized by sine algorithm in prediction of ground settlement around foundation pit

Application of regularized ELM optimized by sine algorithm in prediction of ground settlement around foundation pit

Effectiveness of predicting tunneling-induced ground settlements using machine learning methods with small datasets

Prediction of tunneling-induced ground settlements is an essential task, particularly for tunneling in urban settings. Ground settlements should be limited within a tolerable threshold to avoid damages to aboveground structures. Machine learning (ML) methods are becoming popular in many fields, including tunneling and underground excavations, as a powerful learning and predicting technique. However, the available datasets collected from a tunneling project are usually small from the perspective of applying ML methods. Can ML algorithms effectively predict tunneling-induced ground settlements when the available datasets are small? In this study, seven ML methods are utilized to predict tunneling-induced ground settlement using 14 contributing factors measured before or during tunnel excavation. These methods include multiple linear regression (MLR), decision tree (DT), random forest (RF), gradient boosting (GB), support vector regression (SVR), back-propagation neural network (BPNN), and permutation importance-based BPNN (PI-BPNN) models. All methods except BPNN and PI-BPNN are shallow-structure ML methods. The effectiveness of these seven ML approaches on small datasets is evaluated using model accuracy and stability. The model accuracy is measured by the coefficient of determination ( R 2 ) of training and testing datasets, and the stability of a learning algorithm indicates robust predictive performance. Also, the quantile error (QE) criterion is introduced to assess model predictive performance considering underpredictions and overpredictions. Our study reveals that the RF algorithm outperforms all the other models with the highest model prediction accuracy (0.9) and stability (3.02 × 10 −27 ). Deep-structure ML models do not perform well for small datasets with relatively low model accuracy (0.59) and stability (5.76). The PI-BPNN architecture is proposed and designed for small datasets, showing better performance than typical BPNN. Six important contributing factors of ground settlements are identified, including tunnel depth, the distance between tunnel face and surface monitoring points (DTM), weighted average soil compressibility modulus (ACM), grouting pressure, penetrating rate and thrust force.

ANN-Based Dynamic Prediction of Daily Ground Settlement of Foundation Pit Considering Time-Dependent Influence Factors

Accurately and dynamically predicting ground settlements during the construction of foundation pits is pivotal to the understanding of the potential risk of foundation pits and, therefore, enables constructors to take timely and effective actions to ensure the construction safety of foundation pits. Existing settlement prediction methods mainly focus on the prediction of the maximum ground settlements based on static influence factors, such as soil properties and the geometry of foundation pits. However, these methods are unable to be applied to the prediction of daily ground settlements in a direct way because daily ground settlements can be affected by many time-dependent influence factors, and an accurate prediction of daily ground settlements should take into consideration such factors. To address this problem, this paper proposes an artificial neural network-based daily ground settlement prediction method, where both static and time-dependent influence factors, as well as previous settlement monitoring data, are considered in the optimum artificial neural network. The proposed method is validated using data collected from a real cut-and-cover highway tunnel project in western Hangzhou, China. The results demonstrate that time-dependent influence factors and previous settlement monitoring data play vital roles in establishing an optimum artificial neural network for the accurate prediction of daily ground settlement.

Pareto-optimal performance-based robust design of braced excavations in soft clay with response surface methodology

This paper presents a methodology for robust design optimization of braced excavations in soft clay with response surface methodology based on the pareto-optimal trade-off between system performance and cost. As prediction of maximum ground settlement induced by excavation is generally accompanied by an implicit numerical model, the response surface methodology is employed to build the performance function with respect to both design parameters and uncertain geotechnical parameters. Then a pareto-optimal performance-based robust design framework of braced excavations in soft clay is formulated to find the optimal design that meets the design requirements while optimizing the system performance and construction cost. With the aid of a multi-objective optimization approach, trade-off relationship between performance robustness and cost efficiency are developed to help identify the most robust design based on the targeted performance levels. A design example of braced excavation in Taipei soft clay is used to illustrate the significance of the proposed methodology.

Analysis of ground deformation development and settlement prediction by air-boosted vacuum preloading

Vacuum preloading has been widely used to improve soft soils in coastal areas of China. An increasing amount of evidence from field operations has shown that conventional vacuum preloading is prone to clogging in prefabricated vertical drains (PVDs) and demands a large volume of sand fills. In recent years, air-boosted vacuum preloading has been developed to overcome these limitations; however, this method still requires more data to verify its performance. In this study, a field test for air-boosted vacuum preloading was conducted, and a large-strain two-dimensional (2D) finite element (FE) model was developed and validated against the field test data. Then, a series of FE parametric analyses was performed to assess key factors, i.e. the air injection pressure, the injection spacing, and the characteristics of cyclic injection, which affect the performance of the air-boosted vacuum preloading. The results showed that the ground settlement and lateral displacement of the soils increased due to an increase in the injection pressure, a decrease in the injection spacing, or increases in the number and duration of the injection cycles. Based on the parametric analyses, an empirical formula for ground settlement prediction was proposed and compared with a case history reported in the literature, showing good agreement.

The Prediction of Ground Settlement of a Box Culvert Jacked Under the Action of an Ultra-Shallow Buried Pipe Curtain

The Prediction of Ground Settlement of a Box Culvert Jacked Under the Action of an Ultra-Shallow Buried Pipe Curtain

A Simplified Method for Predicting Tunneling-Induced Ground Movement considering Nonuniform Deformation Boundary

Stochastic medium (SM) theory is a practical method in ground settlement prediction, while its nonintegrable double integral form makes the solution process complicated. A simplified analytical solution based on the SM theory is developed to predict the ground movement in tunneling excavation. With the simplified solution, the ground movement for single tunnel and twin tunnels could be predicted based on the gap parameter G and influence angle β. A feasible approach is developed to estimate these two parameters using the maximum ground settlement Smax and tunnel design parameters, including tunnel depth H and diameter R. The proposed approach can be used to predict the ground movement curve for both circular and noncircular cross section tunnels. To validate its accuracy, the results predicted by the simplified procedure are compared with those obtained by the SM theory and measured in situ. The comparisons show that the current results agree well with those obtained by the SM theory and measured in situ. The comparison of five tunnels in literature illustrates that the simplified method can provide a more reasonable prediction for the ground movement induced by tunneling.

Comparison of machine learning methods for ground settlement prediction with different tunneling datasets

This study integrates different machine learning (ML) methods and 5-fold cross-validation (CV) method to estimate the ground maximal surface settlement (MSS) induced by tunneling. We further investigate the applicability of artificial intelligent (AI) based prediction through a comparative study of two tunnelling datasets with different sizes and features. Four different ML approaches, including support vector machine (SVM), random forest (RF), back-propagation neural network (BPNN), and deep neural network (DNN), are utilized. Two techniques, i.e. particle swarm optimization (PSO) and grid search (GS) methods, are adopted for hyperparameter optimization. To assess the reliability and efficiency of the predictions, three performance evaluation indicators, including the mean absolute error (MAE), root mean square error (RMSE), and Pearson correlation coefficient ( R ), are calculated. Our results indicate that proposed models can accurately and efficiently predict the settlement, while the RF model outperforms the other three methods on both datasets. The difference in model performance on two datasets (Datasets A and B) reveals the importance of data quality and quantity. Sensitivity analysis indicates that Dataset A is more significantly affected by geological conditions, while geometric characteristics play a more dominant role on Dataset B.