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Objective When the sectional tunnel closely under-passes the existing metro station by cutting piles, it has a significant impact on the deformation of the upper station. In order to ensure the safety of construction, it is necessary to take effective measures to control the settlement of metro station. Method Based on the project of a sectional tunnel under-passing the existing metro station, a three-dimensional numerical model is established to analyze the station settlement when being under-passed by cutting piles without settlement control measures. The settlement control effects with measures such as addition of temporary inverted arches, staggered excavation, and grouting reinforcement are studied. A multiple measure combination control scheme is proposed, and its feasibility is verified by the measured engineering data. Result & Conclusion The research results indicate that when the tunnel underpasses the station directly by cutting piles without taking settlement control measures, the maximum settlement of the station will exceed the specified limit. The grouting reinforcement produces the best effect on settlement control with the settlement being reduced by 57.3%. Addition of temporary inverted arches follows next by 40.4%, and staggered excavation has the least effect by 20.8%. The settlement limit can be met by using the multiple measure combination control scheme, which is verified to be feasible by the monitoring data, and can provide reference for similar projects.

In order to solve the problems of low reliability and poor prediction accuracy in traditional building structure settlement monitoring, the author proposes a monitoring and analysis of high-rise building settlement deformation status based on nonlinear regression. The author collected and wirelessly transmitted building settlement information through various hardware devices such as sensors and GPRS communication modules. The monitoring data collected by sensors were compared and analyzed to determine the settlement situation of the building. An RBF neural network prediction model was constructed for possible settlement points. Then, the leapfrog algorithm is used to optimize the structural parameters of the RBF neural network. The experimental results show that this method can accurately evaluate the possible settlement of building structures in actual environments, and the prediction error is small, with a maximum relative error of 4.83 %, indicating good warning ability. This method achieved the best actual value fitting curve results, verifying its feasibility in settlement prediction. Subsequently, a more widely applicable settlement detection and prediction system for building complex structures will be established based on the proposed method, in order to promote its large-scale application.

Abstract The settlement is a critical factor in foundation selection. To ensure the safety and stability of a structure, engineers must accurately predict settlement and design the foundation accordingly. However, in cases where the soil stratigraphy is complex, a more detailed analysis is necessary to determine safe and cost-effective solutions for a building's foundation. This paper presents a case study in which settlement monitoring was performed on a 23-story building with spread footings laid on a stiff rock layer of limited thickness overlying more compressible layers of clay and sand. The building was monitored through four construction stages, during which data on vertical displacements and stress distribution on the soil were obtained. To improve the accuracy of the results, a methodology for calibrating the thicknesses of the soil strata was applied, based on the measured settlements, soil-structure interaction, and estimates from semi-empirical methods. Furthermore, a numerical analysis was performed using the finite element method, where more conservative displacements were observed. Finally, the safety of the building was evaluated by verifying angular distortions.

Granular pile is one of the proven soil improvement mechanisms applied especially for reinforcement of compressible soils supporting light structures. The improvement performance of granular piles is influenced by various factors including the dimension parameters (length, spacing and diameter of the piles). The influence of these three dimension parameters on deformation response of reinforced weak soil has widely been reported. However, the most influential parameter affecting deformation of the weak soil has not been clearly identified. Apparently, these dimension parameters do not equally influence the performance of stone columns in lessening soil deformation. Hence, comparative analysis was carried out in the current study to find out the dimension parameter to which deformation of stone column reinforced weak ground is most sensitive. Finite element based parametric numerical analysis was conducted to simulate the weak ground reinforced with a group of granular piles supporting light weight building. For the parametric study, five dimensions for each parameter (diameter, spacing and length) were considered. The dimensions were purposely varied by uniform percentage increase. Field settlement monitoring data was used to track deformation response of the site and validate the finite element results. The numerical analysis reveals that the simulation results have good agreement with the field settlement monitoring data. From sensitivity view point, column spacing was found to be the most controlling and influential parameter affecting deformation of the reinforced weak soil. Similarly, column length is the dimension parameter to which ground deformation is least sensitive.

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.

Continued settlement monitoring and modeling of landfills are critical for land redevelopment and safety assurance. This paper adopts a MTInSAR technique for time-series monitoring of the Xingfeng landfill (XFL) settlement. A major challenge is that the frequent and significant settlement in the initial stage after the closure of landfills can affect the coherence of interferograms, thus hindering the monitoring of settlement by MTInSAR. We analyzed the factors that can directly affect the temporal decorrelation of landfills and adopted a 3D phase unwrapping approach to correct the phase unwrapping errors caused by such deformation gradient. SAR images from four platforms, including 50 Sentinel-1A, 12 Radarsat-2, 4 ALOS-2, and 2 TerraSAR-X/TanDEM-X images, are collected to measure the settlement and thickness of the landfill. The settlement accuracy is evaluated by a cross-evaluation between Radarsat-2 and Sentinel-1A that have similar temporal coverages. We analyzed the spatial characteristics of settlement and the relationship between the settlement and thickness. Further, we modeled the future settlement of the XFL with a hyperbolic function model. The results showed that the coherence in the initial stage after closure of the XFL is primarily affected by temporal decorrelation caused by considerable deformation gradient compared with spatial decorrelation. Settlement occurs primarily in the forward slope of the XFL, and the maximum line-of-sight (LOS) settlement rate reached 0.808 m/year from August 2018 to May 2020. The correlation between the settlement and thickness is 0.62, indicating an obvious relationship between the two. In addition, the settlement of younger areas is usually greater than that of older areas.

Owing to the pavement cracking of heightening and thickening levees (HTL) caused by differential settlement, it is necessary to study the settlement prediction and differential settlement criterion for HTLs. In this study, an HTL was taken as the study object. First, leveling observation and layered settlement observation were adopted, and a generalized settlement prediction model (GSPM) that can describe both S-shaped and hyperbolic settlement⁻time curves was proposed. The physical meanings of GSPM parameters were analyzed. Moreover, the effectiveness of the GSPM was verified using field monitoring data. Second, finite element analysis based on a viscoelastic constitutive model was used to reveal the quantitative relationship between the differential settlement and horizontal stress of the pavement. On this basis, the differential settlement criterion was obtained. The results showed that the GSPM can unify the logistic model and hyperbolic model by introducing two morphological change parameters. In addition, the GSPM can be more widely used, more accurate, and more stable than the traditional logistic model and hyperbolic model. When an asphalt concrete pavement (whose ultimate tensile stress is 0.12 MPa) is used, the corresponding differential settlement criterion is approximately 4.3 cm. The research results can provide a reference for reasonable differential settlement control for HTLs in order to prevent pavement cracking.

Settlement is one of the most important deformation characteristics of high concrete faced rockfill dams (CFRDs, >100 m). High CFRDs safety would pose a great threat to the security of people’s lives and property downstream if this kind of deformation were not to be measured correctly, as traditional monitoring approaches have limitations in terms of durability, coverage, and efficiency. It has become urgent to develop new monitoring techniques to complement or replace traditional monitoring approaches for monitoring the safety and operation status of high CFRDs. This study examines the Shuibuya Dam (up to 233.5 m in height) in China, which is currently the highest CFRD in the world. We used space-borne Interferometric Synthetic Aperture Radar (InSAR) time series to monitor the surface deformation of the Shuibuya Dam. Twenty-one ALOS PALSAR images that span the period from 28 February 2007 to 11 March 2011 were used to map the spatial and temporal deformation of the dam. A high correlation of 0.93 between the InSAR and the in-situ monitoring results confirmed the reliability of the InSAR method; the deformation history derived from InSAR is also consistent with the in-situ settlement monitoring system. In addition, the InSAR results allow continuous investigation of dam deformation over a wide area that includes the entire dam surface as well as the surrounding area, offering a clear picture continuously of the dam deformation.