Inverse Analysis of a Supported Excavation in Chicago

Kim, Sangrae; Finno, Richard J.

doi:10.1061/(asce)gt.1943-5606.0002120

Cited by 30 publications

(8 citation statements)

References 22 publications

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“…Comparing the results of stiffness parameters estimated from both laboratory and field tests showed that the values from the laboratory data were significantly lower than those optimized from the field data, which could be due to the specimens used for the triaxial tests being taken from a thin wall tubes. Although sometimes parameter calibration may need to employ both laboratory test data and field measurements, the result of this study showed the applicability of using optimization algorithm like UCODE on efficiently calibrating model parameters at early stages of the project and provide a basis to achieve more reasonable predictions at the later stages ( Kim, 2018).…”

Section: Inverse Analysis Methods In Geotechnical Engineeringmentioning

confidence: 92%

Inverse-analysis of Compressibility Parameters for Fine-grained Soils in GTA

Ayizula

2024

Preprint

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<p>The Finite Element Method (FEM) has been routinely used in geotechnical engineering. However, its realistic simulation ofsoil behavior depends on the accurate model input parameters. This study aims to determine through an inverse analysis on the compressibility of fine-grained soils in the Greater Toronto Area (GTA) according to the Hardening Soil Model (HSM). A series of oedometer test results is collected from a local transit project and back analyzed by employing UCODE, auniversal inversemodeling tool,which can adjust model parametersto fit the simulated results with the test values. First, a sensitivity analysis is performed to select the most critical model parameters to simplify the problem. Second, the selected HSM parameters are calibrated by combining UCODE with geotechnical FEM software, PLAXIS. Third, a statistical analysis is conducted on the compressibility parameters according to the soil types. In the end, a series of correlation formulas are derived to estimate the compressibility properties from soil indices.</p>

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Section: Inverse Analysis Methods In Geotechnical Engineeringmentioning

confidence: 92%

Inverse-analysis of Compressibility Parameters for Fine-grained Soils in GTA

Ayizula

2024

Preprint

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“…[4][5][6] The effectiveness of inverse analysis depends on the forward calculation model, the field monitoring data, the data-interpretation approach and its associated optimization technique. 7 The available data-interpretation approaches for inverse analysis of braced excavations include the least squares method, 8 the maximum likelihood method, 9,10 the optimization methods, [11][12][13][14][15] and the Bayesian method. 4,5,16 Among these inverse analysis methods, the Bayesian method is a probabilistic method as it tends to provide all the possible solutions associated with their possibilities.…”

Section: Introductionmentioning

confidence: 99%

A bi‐fidelity inverse analysis method for deep excavations considering three‐dimensional effects

Tao,

Pan,

Sun

et al. 2024

Num Anal Meth Geomechanics

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Inverse analysis methods are commonly used in braced excavations for improved deformation predictions. This paper proposes a bi‐fidelity ensemble randomized maximum likelihood (BF‐EnRML) method for efficient inverse analyses of deep excavations considering the three‐dimensional effects. The bi‐fidelity (BF) model is developed by the low‐fidelity model (i.e., two‐dimensional finite element model, 2D FEM) and the high‐fidelity model (i.e., 3D FEM) for a balance between efficiency and accuracy. A large number of 2D FEMs are first used to explore the relationship between soil parameters and wall deflections. A few 3D FEMs are then performed to calibrate the discrepancy between 2D‐3D deflections caused by the inability of 2D FEM to consider the three‐dimensional effects. The constructed BF model serves as the forward model in inverse analysis. The soil parameters are updated by incorporating the monitoring data based on EnRML and further used to predict wall deflections in later stages. A hypothetical excavation and a real project are studied to evaluate the performance of the proposed method. The results show that the BF model can provide wall deflection predictions close to those calculated from 3D FEM while using a computational cost of 2D FEM. The BF‐EnRML method can efficiently update the soil parameters and improve the wall deflection predictions. Moreover, factors affecting the accuracy of the BF model are studied, including the number of required 3D FEMs, the distance from the evaluated wall section to the excavation corner, the number of data points along the wall depth, and the number of excavation stages.

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“…including the inverse analysis is also limited because it relies on those monitoring points [1,2]. Consequently, a technique for measuring the displacements induced by excavation work in the entire range or full field is crucial for ensuring construction safety [3].…”

Section: Introductionmentioning

confidence: 99%

Detecting and Evaluating Displacement of Paving Blocks Adjacent to Deep Excavation Sites Using Terrestrial Photogrammetry

Kim¹,

Choi²,

Jung³

et al. 2023

Preprint

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In urban areas, deep excavation-induced ground deformation may damage adjacent existing structures and is conventionally evaluated by levelling at installed settlement points. However, a small number of measurements cannot represent the total change in ground deformation adjacent to excavation sites. Furthermore, significant local subsidence may occur in places where settlement points have not been installed and only noticed after an accident. For deep excavation sites located in urban areas, paved pedestrian sidewalks are often located adjacent to sites, and construction activity can cause these paving blocks to displace. This study introduces a method to detect paving block displacement adjacent to deep excavation sites using terrestrial photogrammetry. A digital camera creating point cloud data (PCD) and an acquisition method satisfying the frontal and side overlap requirements were demonstrated. To investigate the displacement detection and measurement capabilities by PCD analysis, an experimental program was conducted, including a PCD comparison containing the uplift, settlement, and horizontal paving block displacement and reference data. The cloud-to-cloud distance computation algorithm was adopted for PCD comparison. Paving block displacement was detected for displacements of 5, 7.5, and 10 mm in the uplift, settlement, and horizontal directions; however, the horizontal displacements were less clear. PCD analysis enabled satisfactory measurements between 0.024 and 0.881 mm for the vertical-displacement cases, but a significant error was observed for the horizontal-displacement cases owing to the cloud-comparison algorithm. The measurement blind spot of limited settlement points was overcome by the proposed method that detected and measured paving block displacement adjacent to excavation sites.

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Inverse Analysis of a Supported Excavation in Chicago

Cited by 30 publications

References 22 publications

Inverse-analysis of Compressibility Parameters for Fine-grained Soils in GTA

Inverse-analysis of Compressibility Parameters for Fine-grained Soils in GTA

A bi‐fidelity inverse analysis method for deep excavations considering three‐dimensional effects

Detecting and Evaluating Displacement of Paving Blocks Adjacent to Deep Excavation Sites Using Terrestrial Photogrammetry

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