• Geomechanics and Engineering
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• v.5 no.6
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• pp.541-564
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• 2013

In this study, artificial neural networks (ANNs) were used to predict the settlement of pad footings on cohesionless soils based on standard penetration test. To achieve this, a computer programme was developed to calculate the settlement of pad footings from five traditional methods. The footing geometry (length and width), the footing embedment depth, $D_f$, the bulk unit weight, ${\gamma}$, of the cohesionless soil, the footing applied pressure, Q, and corrected standard penetration test, $N_{cor}$, varied during the settlement analyses and the settlement value of each footing was calculated for each method. Then, an ANN model was developed for each traditional method to predict the settlement by using the results of the analyses. The settlement values predicted from the ANN model were compared with the settlement values calculated from the traditional method for each method. The predicted values were found to be quite close to the calculated values. It has been demonstrated that the ANN models developed can be used as an accurate and quick tool at the preliminary designing stage of pad footings on cohesionless soils without a need to perform any manual work such as using tables or charts. Sensitivity analyses were also performed to examine the relative importance of the factors affecting settlement prediction. According to the analyses, for each traditional method, $N_{cor}$ is found to be the most important parameter while ${\gamma}$ is found to be the least important parameter.

• 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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• 2005.03a
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• pp.924-931
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• 2005

In this study, we intended to compare and examine several settlement management methods by analyzing measurement results of a site of the industrial complex at ${\bigcirc}{\bigcirc}$ province. We predicted and analyzed the amount of final settlement by using generally used final settlement methods as like Hyperbola method, Hoshino methods and Asaoka method. And then, We compared the predicted results with that of measurement. On the basis of comparison of the three methods, Hyperbola method was the most convenient and accurate method of the three methods and if a sufficient time was given enough after embankment construction, the use of Hoshino method was possible. In the case of the Asaoka methods, it was possible to know that it had an approaching tendency to the measured one with increasing time interval spent on analysis. Therefore, in order to predict settlement behavior more accurately it is needed to understand their advantages and shortcomings sufficiently and pay attention to application to the real site.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.993-998
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• 2009

This study was conducted to propose the model that is able to predict long-term settlement of sands containing carbonates. We can observe that in addition to the initial elastic compression, a considerable additional compression occurs with long-term period in some sands. The compressibility of sands is significantly dependent on particle characteristics. Some sands have many pores and particularly has an angular shape. To predict of long-term settlement of these sands which contain carbonates, first of all a variety of tests which are to assess chemical, physical and compression characteristics of these sands were conducted. Second, in order to know settlement with time, the time parameter was determined by analyzing the measured data obtained from $\bigcirc\bigcirc$ area. The measured settlement of this area shows that in addition to the initial elastic compression, a considerable additional compression occurring with time. It was caused by the crushing, shattering, and rearrangement of particles, which were real phenomena in sandy soils under loading condition. Based on this case study, we proposed that a simple method is able to predict settlement with time of sandy soil containing carbonates.

• Land and Housing Review
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• v.4 no.1
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• pp.125-131
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• 2013

To predict the real bearing capacity and settlement of the shallow foundation the plate load test results were used. But there is no field estimation method about igneous weathered soil and rock. Therefore, to predict the settlement equation, the plate load test about igneous weathered soil and rock was done in this study. To analyze the load ~ relative settlement curve by normalization, it did not use normal analysis method, but the load ~ relative settlement (s/B, s : settlement, B : breadth of plate) was used. As a result of normalization by load ~ relative settlement conception, the curve was regular regardless of plate diameter and it was suggested the relationship of in-situ soil condition and results.

• Journal of the Korean GEO-environmental Society
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• v.13 no.12
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• pp.35-42
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• 2012

There are many large-scale coastal region landfill and land development by loading to use territory efficiently, this regions are mostly soft clay ground. Constructing structures and road on the soft ground bring about engineering problems like ground shear fracture and a big amount of consolidation by bearing capacity. Improvement of soft soil is required to secure soil strength and settlement control. In improvement of soft soil, predict for the amount of settlement based on field surveyed reports are important element for estimating pre-loading banking height and the final point of consolidation. In this study, there is calculating theoretical settlement by analyzing field surveyed report and ground investigation to improvement of soft soil with pre-loading and vertical drain method. And present settlement prediction method reflect soil characteristics in Gimpo Hangang site by analysing prediction settlement and observational settlement during compaction using hyperbolic, ${\sqrt{s}}$, Asaoka method.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.4
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• pp.73-81
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• 2012

In this study, the settlement data of 32 center cored rockfill dams (total 39 monitored data) were collected and analyzed to develop the method to predict the crest settlement of a CCRD after impounding by using the internal settlement data occurred during construction. An artificial neural network (ANN) modeling was used in developing the method, which was considered to be a more reliable approach since in the ANN model dam height, core width, and core type were all considered as input variables in deriving the crest settlement, whereas in conventional methods, such as Clements's method, only dam height is used as a variable. The ANN analysis results showed a good agreement with the measured data, compared to those by the conventional methods using regression analysis. In addition, a simple procedure to use the ANN model for engineers in practice was provided by proposing the equations used for given input values.

• 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.

• Journal of the Korean GEO-environmental Society
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• v.20 no.10
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• pp.29-38
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• 2019

In stability and settlement management of soft ground, the settlement prediction technology has been continuously developed and used to reduce construction cost and confirm the exact land use time. However, the preexistence prediction methods such as hyperbolic method, Asaoka method and Hoshino method are difficult to predict the settlement accurately at the beginning of consolidation because the accurate settlement prediction is possible only after many measurement periods have passed. It is judged as the reason for estimating the future settlement through the proportionality assumption of the slope which the preexistence prediction method computes from the settlement curve. In this study, ARIMA technique is introduced among time series analysis techniques and compared with preexistence prediction methods. ARIMA method was predictable without any distinction of ground conditions, and the results similar to the existing method are predicted early (final settlement).