• Earthquakes and Structures
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• v.25 no.1
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• pp.15-26
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• 2023

It is of great importance to assess the residual displacement demand in the performance-based seismic design. In this paper, a hybrid deep learning model for predicting the residual displacement spectra under near-fault (NF) ground motions is proposed by combining the long short-term memory network (LSTM) and back-propagation (BP) network. The model is featured by its capacity of predicting the residual displacement spectrum under a given NF ground motion while considering the effects of structural parameters. To construct this model, 315 natural and artificial NF ground motions were employed to compute the residual displacement spectra through elastoplastic time history analysis considering different structural parameters. Based on the resulted dataset with a total of 9,450 samples, the proposed model was finally trained and tested. The results show that the proposed model has a satisfactory accuracy as well as a high efficiency in predicting residual displacement spectra under given NF ground motions while considering the impacts of structural parameters.

• Geomechanics and Engineering
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• v.20 no.2
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• pp.147-154
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• 2020

A novel approach for predicting lateral displacement caused by pile installation in anisotropic clay is presented, on the basis of the cylindrical and spherical cavities expansion theory. The K₀-based modified Cam-clay (K₀-MCC) model is adopted for the K₀-consolidated clay and the process of pile installation is taken as the cavity expansion problem in undrained condition. The radial displacement of plastic region is obtained by combining the cavity wall boundary and the elastic-plastic (EP) boundary conditions. The predicted equations of lateral displacement during single pile and multi-pile installation are proposed, and the hydraulic fracture problem in the vicinity of the pile tip is investigated. The comparison between the lateral displacement obtained from the presented approach and the measured data from Chai et al. (2005) is carried out and shows a good agreement. It is suggested that the presented approach is a useful tool for the design of soft subsoil improvement resulting from the pile installation.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.109-117
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• 2008

In this study, a method predicting the displacement responseof structures from the measured dynamic strain signal is proposed by using a mode decomposition technique. Dynamic loadings including wind and seismic loadings could be exerted to the bridge. In order to examine the bridge stability against these dynamic loadings, the prediction of displacement response is very important to evaluate bridge stability. Because it may be not easy for the displacement response to be acquired directly on site, an indirect method to predict the displacement response is needed. Thus, as an alternative for predicting the displacement response indirectly, the conversion of the measured strain signal into the displacement response is suggested, while the measured strain signal can be obtained using fiber optic Bragg-grating (FBG) sensors. To overcome such a problem, a mode decomposition technique was used in this study. The measured strain signal is decomposed into each modal component by using the empirical mode decomposition(EMD) as one of mode decomposition techniques. Then, the decomposed strain signals on each modal component are transformed into the modal displacement components. And the corresponding mode shapes can be also estimated by using the proper orthogonal decomposition(POD) from the measured strain signal. Thus, total displacement response could be predicted from combining the modal displacement components.

• Computers and Concrete
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• v.32 no.5
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• pp.513-525
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• 2023

Recently, research on predicting the behavior of reinforced concrete (RC) columns using machine learning methods has been actively conducted. However, most studies have focused on predicting the ultimate strength of RC columns using a regression algorithm. Therefore, this study develops a successive machine learning process for predicting multiple nonlinear behaviors of rectangular RC columns. This process consists of three stages: single machine learning, bagging ensemble, and stacking ensemble. In the case of strength prediction, sufficient prediction accuracy is confirmed even in the first stage. In the case of displacement, although sufficient accuracy is not achieved in the first and second stages, the stacking ensemble model in the third stage performs better than the machine learning models in the first and second stages. In addition, the performance of the final prediction models is verified by comparing the backbone curves and hysteresis loops obtained from predicted outputs with actual experimental data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4A
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• pp.507-515
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• 2008

In this study, a method predicting the displacement response of structures from the measured dynamic strain signal is proposed by using mode decomposition technique. Evaluation of bridge stability is normally focused on the bridge completed. However, dynamic loadings including wind and seismic loadings could be exerted to the bridge under construction. In order to examine the bridge stability against these dynamic loadings, the prediction of displacement response is very important to evaluate bridge stability. Because it may be not easy for the displacement response to be acquired directly on site, an indirect method to predict the displacement response is needed. Thus, as an alternative for predicting the displacement response indirectly, the conversion of the measured strain signal into the displacement response is suggested, while the measured strain signal can be obtained using fiber optic Bragg-grating (FBG) sensors. As previous studies on the prediction of displacement response by using the FBG sensors, the static displacement has been mainly predicted. For predicting the dynamic displacement, it has been known that the measured strain signal includes higher modes and then the predicted dynamic displacement can be inherently contaminated by broad-band noises. To overcome such problem, a mode decomposition technique was used. Mode decomposition technique estimates the displacement response of each mode with mode shape estimated to use POD from strain signal and with the measured strain signal decomposed into mode by EMD. This is a method estimating the total displacement response combined with the each displacement response about the major mode of the structure. In order to examine the mode decomposition technique suggested in this study model experiment was performed.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• Wind and Structures
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• v.36 no.6
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• pp.379-392
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• 2023

Structural health monitoring is used to ensure the well-being of civil structures by detecting damage and estimating deterioration. Wind flow applies external loads to high-rise buildings, with the horizontal force component of the wind causing structural displacements in high-rise buildings. This study proposes a deep learning-based predictive model for measuring lateral displacement response in high-rise buildings. The proposed long short-term memory model functions as a sequence generator to generate displacements on building floors depending on the displacement statistics collected on the top floor. The model was trained with wind-induced displacement data for the top floor of a high-rise building as input. The outcomes demonstrate that the model can forecast wind-induced displacement on the remaining floors of a building. Further, displacement was predicted for each floor of the high-rise buildings at wind flow angles of 0° and 45°. The proposed model accurately predicted a high-rise building model's story drift and lateral displacement. The outcomes of this proposed work are anticipated to serve as a guide for assessing the overall lateral displacement of high-rise buildings.

• Computers and Concrete
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• v.33 no.5
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• pp.481-496
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• 2024

Internal erosion around pipes can lead to the failure of earth dams through various mechanisms. This study investigates the displacement patterns in earth dam models under three different failure modes due to internal erosion, using digital image correlation (DIC) methods. Three failure modes—erosion along a pipe (FM1), pipe leakage leading to soil erosion (FM2), and erosion in a pipe due to defects (FM3)—are analyzed using two- and three-dimensional image- processing techniques. The internal displacement of the cross-sectional area and the surface displacement of the downstream slope in the dam models are monitored using an image acquisition system. Physical model tests reveal that FM1 exhibits significant displacement on the upper surface of the downstream slope, FM2 shows focused displacement around the pipe defect, and FM3 demonstrates increased displacement on the upstream slope. The variations in internal and surface displacements with time depend on the segmented area and failure mode. Analyzing the relationships between internal and surface displacements using Pearson correlation coefficients reveals various displacement patterns for the segmented areas and failure modes. Therefore, the image-based characterization methods presented in this study may be useful for analyzing the displacement distribution and behavior of earth dams around pipes, and further, for understanding and predicting their failure mechanisms.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.129-141
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• 2003

The precise prediction of ground displacement plays an important role in planning and constructing tunnels. In this study, an equation for predicting the surface and crown settlement is suggested by examining the theories of ground movement caused by tunnel excavation. From the 3D numerical modeling, the reinforcement effect of UAM (Umbrella Arch Method) is quantitatively analyzed with respect to deformation modulus and overburden. By using a regression technique for the numerical results, an equation for predicting the settlement is suggested.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.143-150
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• 2021

To develop the technique for predicting the horizontal displacement of a retaining wall induced by an earthquake, an equation of motion that depicts the retaining wall-soil vibrating system was derived. The resulting differential equation was solved using the Runge-Kutta-Nystr?m method. Considering the pre-mentioned derivation process, the analysis procedures for obtaining horizontal displacement induced by an earthquake were programmed. The core algorithm of the displacement-force relationship, which is the main engine of the developed program, was suggested. Considering the results obtained by adopting the developed program to the assumed retaining wall under an earthquake, the relationships between the time-displacement, time-force, and displacement-force were reasonable. According to the results computed by the program, the displacements to the front direction of the wall occurred, and the displacement per cycle converged after some cycles elapsed. Displacements with a natural period were calculated, which showed that the maximum displacement was observed when the natural frequency was slightly different from the excitation frequency rather than the same values of the two frequencies. This happens because the vibrating system was modeled by two springs with different stiffness.