• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.1202-1205
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• 2009

Embankment preloading, in conjunction with prefabricated vertical (PV) drains, was used to accelerate consolidation of marine clays in Pusan New Harbour project. UP to eightteen settlement plates were installed at the ground reclamated site under the embankment fill to monitor the preload performance. This analysis is carried out by five settlement prediction methods including the Asaoka, Hyperbolic, Hoshino, and back-analysis method based on optimization. The field settlement data can be analysed by settlement prediction methods to predict the ultimate settlement and the degree of consolidation of the reclaimed land under charge fill. The authors compared with the analyzed results of the methods.

• Journal of the Korean GEO-environmental Society
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• v.15 no.12
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• pp.97-107
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• 2014

Settlement prediction has been conducted using Hyperbolic, Hoshino, and Monden methods, etc in the fields. These methods are only able to predict settlement after finishing the final filling stage. A new method is proposed to make up for such a weak point. This method was named as SPSFC (Settlement Prediction for Staged Filling Construction) method, which can be able to predict the settlement both the final filling stage and the staged filling from the initial filling stage in soft ground. To verify the applicability of the SPSFC method, firstly. The settlement predicted by the existed methods are compared with that obtained by the SPSFC method. The comparison results indicate the SPSFC has enough reliability to use for prediction of settlement. Secondly. by analyzing the settlement data measured during the initial filling stage, the soil parameters which need to predict the settlement are obtained by the SPSFC method. Then using the obtained soil parameters the time-settlement curve is predicted and compared. The predicted settlement is well matched with the measured one. From the study, the SPSFC method can be possible to predict settlement during the staged filling with only the initial settlement data.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.457-467
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• 2010

The settlement prediction is very important to preloading method for a construction site on a soft ground. At the design stage, however, it is hard to predict the settlement exactly due to limitations of the site survey. Most of the settlement prediction is performed by a regression settlement curve based on the field data during a construction. In Korea, hyperbolic method has been most commonly used to align the settlement curve with the field data, because of its simplicity and many application cases. The results from hyperbolic method, however, may be differed by data selections or data fitting methods. In this study, the analyses using hyperbolic method were performed about the field data of $\bigcirc\bigcirc$ site in Pusan. Two data fitting methods, using an axis transformation or an alternative method, were applied with the various data group. If data was used only after the ground water level being stabilized, fitting results using both methods were in good agreement with the measured data. Without the information about the ground water level, the alternative method gives better results with the field data than the method using an axis transformation.

• Journal of the Korean Geotechnical Society
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• v.39 no.4
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• pp.45-54
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• 2023

The settlement prediction during the design phase is primarily conducted using theoretical methods. However, measurement-based settlement prediction methods that predict future settlements based on measured settlement data over time are primarily used during construction due to accuracy issues. Among these methods, the hyperbolic method is commonly used. However, the existing hyperbolic method has accuracy issues and statistical limitations. Therefore, a weighted nonlinear regression hyperbolic method has been proposed. In this study, two weighting methods were applied to the weighted nonlinear regression hyperbolic method to compare and analyze the accuracy of settlement prediction. Measured settlement plate data from two sites located in Busan New Port were used. The settlement of the remaining sections was predicted by setting the regression analysis section to 30%, 50%, and 70% of the total data. Thus, regardless of the weight assignment method, the settlement prediction based on the hyperbolic method demonstrated a remarkable increase in accuracy as the regression analysis section increased. The weighted nonlinear regression hyperbolic method predicted settlement more accurately than the existing linear regression hyperbolic method. In particular, despite a smaller regression analysis section, the weighted nonlinear regression hyperbolic method showed higher settlement prediction performance than the existing linear regression hyperbolic method. Thus, it was confirmed that the weighted nonlinear regression hyperbolic method could predict settlement much faster and more accurately.

• Geomechanics and Engineering
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• v.20 no.5
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• pp.385-397
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• 2020

This paper presents an application of artificial neural networks (ANNs) in settlement prediction of a foundation on sandy soil. In order to train the ANN model, a wide experimental database about settlement of foundations acquired from available literatures was collected. The data used in the ANNs model were arranged using the following five-input parameters that covered both geometrical foundation and sandy soil properties: breadth of foundation B, length to width L/B, embedment ratio D_f/B, foundation net applied pressure q_net, and average SPT blow count N. The backpropagation algorithm was implemented to develop an explicit predicting formulation. The settlement results are compared with the results of previous studies. The accuracy of the proposed formula proves that the ANNs method has a huge potential for predicting the settlement of foundations on sandy soils.

• Magazine of the Korean Society of Agricultural Engineers
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• v.38 no.6
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• pp.35-41
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• 1996

The theory of consolidation has been achieved remarkable development in terms of theory such as finite consolidation theory, two dimensional Rendulic consolidation theory. Though those theories are well defined, the analysis is by no means straightforward, because associated properties are very difficult to determine in the laboratory, Therefore Terzaghi's one dimensional consolidation theory and Barron's cylindrical consolidation theory are still widely used in engineering practice. The theoretical shortcomings of those consolidation theories and uncertainties of associated properties make inevitably some discrepancy between theoretical and field settlements. Field settlement measurement by settlement plate is, therefore, widely used to overcome the discrepancy. Ultimate settlement is one of the most important factor of embankment construction on soft soils. Nowadays the ultimate settlement prediction methods using field settlement data are widely accepted as a helpful tool for field settlement analysis of embankment construction on soft soils. Among the various methods of ultimate settlement prediction, hyperbolic method and Asaoka's method are most commonly used because of their simplicity and ability to give a reasonable estimate of consolidation settlement. In this paper, the reliability of hyperbolic method and Asaoka's method has been examined using analytical methods. It is shown that both hyperbolic method and Asaoka's method are significantly affected by the direction of drainage.

• Smart Structures and Systems
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• v.33 no.1
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• pp.17-26
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• 2024

The new metro crossing the existing metro will cause the settlement or floating of the existing structures, which will have safety problems for the operation of the existing metro and the construction of the new metro. Therefore, it is necessary to monitor and predict the settlement of the existing metro caused by the construction of the new metro in real time. Considering the complexity and uncertainty of metro settlement, a Gaussian Prior Bayesian Emulator (GPBE) probability prediction model based on Bayesian optimization (BO) is proposed, that is, BO-GPBE. Firstly, the settlement monitoring data are analyzed to get the influence of the new metro on the settlement of the existing metro. Then, five different acquisition functions, that is, expected improvement (EI), expected improvement per second (EIPS), expected improvement per second plus (EIPSP), lower confidence bound (LCB), probability of improvement (PI) are selected to construct BO model, and then BO-GPBE model is established. Finally, three years settlement monitoring data were collected by structural health monitoring (SHM) system installed on Nanjing Metro Line 10 are employed to demonstrate the effectiveness of BO-GPBE for forecasting the settlement.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5C
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• pp.155-162
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• 2011

Settlement prediction based on field monitored data, which is used to control subsequent surcharges, is very important in construction management for soft ground improvement with the preloading method. Observational settlement prediction methods, which are suggested for an instantaneous loading, have been widely used in fields. However, they have difficulties in the settlement prediction with subsequent surcharge change. In this paper, a simple method to predict the settlement with subsequent surcharge change is suggested. The suggested method adopts assumptions to simplify the complex field condition and utilizes observational methods. The suggested method is applied to a large consolidation test result, FDM analysis results, and field monitored settlement data to confirm its practicability. From the applications, the suggested method produces reasonable prediction results with various subsequent surcharge changes.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.1405-1410
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• 2006

Purpose of this research is that offers basic data for optimized design using neural network method to calculate consolidation settlement in study area. In this research, preformed the neural network method that analyzed the settlement characteristics of soft ground nearby study area. Thus, data base established on ground properties and consolidation settlement of neighboring area. In addition, designed the optimum neural network model for prediction of settlement through network learning and consolidation settlement prediction using consolidation settlement DB and ground properties DB. Optimized neural network model decided by repeated learning for various case of hidden layers. In this study, proposed that the optimized consolidation settlement calculation method using neural network and verified which is the optimized consolidation settlement calculation method using neural network.

• Journal of the Korean GEO-environmental Society
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• v.16 no.11
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• pp.29-37
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• 2015

Non-excavation method is needed to secure the stability of existing structures during construction. Therefore, prediction of ground settlement is essential. Causes of settlement when using steel pipe indentation method are leading pipe-steel pipe gap, excessive excavation and soil-steel pipe friction etc. Also they are similar to the causes of settlement when using Shield TBM during construction. In this study, ground settlement during steel pipe indentation is predicted by the Gap Parameter Method and Volume Loss Method which are kinds of Shield TBM prediction Method. and compared with those of prediction methods by conducting field test. As a result, Volume Loss Prediction Method is the most similar to the field tests. However, It is needed to additional studies, such as decision of the factors and adaptability for total settlement predictions of non-excavation method.