Dookie Kim

Utilization relevance vector machine for slope reliability analysis

This paper examines the potential of relevance vector machine (RVM) for slope reliability by using first order second moment method (FOSM). The FOSM demands the values and partial derivatives of the performance function with respect to the design random variables. Such calculations could be time-consuming or cumbersome when the performance functions are implicit. The analysis of slope by limit equilibrium method gives implicit performance functions. Here, RVM has been used to predict implicit performance functions. RVM rely on the Bayesian concept and utilize an inductive modelling procedure that allows incorporation of prior knowledge in the estimation process. In this paper, an example is given regarding how the proposed RVM-based FOSM analysis can be carried out. This study shows that the proposed RVM-based FOSM is viable alternative for slope reliability analysis.

De-noising of GPS structural monitoring observation error using wavelet analysis

In the process of the continuous monitoring of the structures state properties such as static and dynamic responses using Global Positioning System (GPS), there are unavoidable errors in the observation data. These GPS errors and measurement noises have their disadvantages in the precise monitoring applications because these errors cover up the available signals that are needed. The current study aims to apply three methods, which are used widely to mitigate sensor observation errors. The three methods are based on wavelet analysis, namely principal component analysis method, wavelet compressed method, and the de-noised method. These methods are used to de-noise the GPS observation errors and to prove its performance using the GPS measurements which are collected from the short-time monitoring system designed for Mansoura Railway Bridge located in Egypt. The results have shown that GPS errors can effectively be removed, while the full-movement components of the structure can be extracted from the original signals using wavelet analysis.

Least square support vector machine and multivariate adaptive regression spline for modeling lateral load capacity of piles

This article adopts least square support vector machine (LSSVM) and multivariate adaptive regression spline (MARS) for prediction of lateral load capacity (Q) of pile foundation. LSSVM is firmly based on the theory of statistical learning, uses regression technique. MARS is a nonparametric regression technique that models complex relationships. Diameter of pile (D), depth of pile embedment (L), eccentricity of load (e), and undrained shear strength of soil (Su) have been used as input parameters of LSSVM and MARS. Equations have been presented from the developed MARS and LSSVM. This study also presents a comparative study between the developed MARS and LSSVM.

MODELING OF NONLINEAR CYCLIC LOAD BEHAVIOR OF I-SHAPED COMPOSITE STEEL-CONCRETE SHEAR WALLS OF NUCLEAR POWER PLANTS

In recent years steel-concrete composite shear walls have been widely used in enormous high-rise buildings. Due to high strength and ductility, enhanced stiffness, stable cycle characteristics and large energy absorption, such walls can be adopted in the auxiliary building; surrounding the reactor containment structure of nuclear power plants to resist lateral forces induced by heavy winds and severe earthquakes. This paper demonstrates a set of nonlinear numerical studies on I-shaped composite steel-concrete shear walls of the nuclear power plants subjected to reverse cyclic loading. A three-dimensional finite element model is developed using ABAQUS by emphasizing on constitutive material modeling and element type to represent the real physical behavior of complex shear wall structures. The analysis escalates with parametric variation in steel thickness sandwiching the stipulated amount of concrete panels. Modeling details of structural components, contact conditions between steel and concrete, associated boundary conditions and constitutive relationships for the cyclic loading are explained. Later, the load versus displacement curves, peak load and ultimate strength values, hysteretic characteristics and deflection profiles are verified with experimental data. The convergence of the numerical outcomes has been discussed to conclude the remarks.

Application of Structural Health Monitoring System for Reliable Seismic Performance Evaluation of Infrastructures

In this study, the useful application of an instrumented structural health monitoring (SHM) system is proposed for the reliable seismic performance evaluation based on measured response data. A seismic fragility is chosen as a key index for probabilistic seismic performance assessment on an infrastructure. The seismic performance evaluation procedure consists of the following five main steps; (1) measuring ambient vibration of a bridge under general traveling vehicles; (2) identifying modal parameters including natural frequencies and mode shapes from the measured acceleration data by output-only modal identification method; (3) updating linear structural parameters in a preliminary finite element (FE) model using the identified modal parameters; (4) analyzing nonlinear response time histories of the structure using nonlinear seismic analysis program; and finally (5) evaluating the probabilistic seismic performance in terms of seismic fragility. In the present study, the seismic fragility curves are represented by a log-normal distribution function. An instrumented highway bridge is utilized to demonstrate the proposed evaluation procedure and it is found that the seismic fragility of a highway bridge can be reliably evaluated by combining the modal information obtained from the instrumented SHM system and FE model updating by using the information.

A probabilistic capacity spectrum strategy for the reliability analysis of bridge pile shafts considering soil structure interaction

This paper presents a probabilistic capacity spectrum strategy for the reliability analysis of a bridge pile shaft, accounting for uncertainties in design factors in the analysis and the soil-structure interaction (SSI). Monte Carlo simulation method (MCS) is adopted to determine the probabilities of failure by comparing the responses with defined limit states. The analysis considers the soil structure interaction together with the probabilistic application of the capacity spectrum method for different types of limit states. A cast-in-drilledhole (CIDH) extended reinforced concrete pile shaft of a bridge is analysed using the proposed strategy. The results of the analysis show that the SSI can lead to increase or decrease of the structures probability of failure depending on the definition of the limit states.

Active Control of 3-D Irregular Building by using Energy Based Neuro-Controller

In this paper, a methodology of active control of structures based on modal energy is extended to control the response of irregular structures. The nonlinearities of material and geometry are taken into account in the training algorithm. A three-dimensional (3-D) multi-story irregular building is employed to scrutinize the performance of the improved control strategy. The torsional and lateral responses of the structure and the dynamic behavior of actuators have been incorporated in the control model simultaneously. Thus, the lateral-torsional coupling as well as the structure-actuator interaction is considered. The incident angle of ground motion and the time delay of actuators are also included in the model. El-Centro 1940 earthquake is used to train the algorithm, and then five different earthquake records have been used to evaluate the trained control system. The results of controlled structural responses in each case proved that the proposed control algorithm may be promising in vibration control of 3-D irregular structures.

Modal energy balance approach for seismic performance evaluation of building structures considering nonlinear behaviour

Abstract This paper presents an improved seismic evaluation method based on modal energy balance concept. The capacity spectrum for the structure is converted into energy capacity curve and the demand spectrum for the structure is converted into energy demand curve. The energy capacity curve is combined with energy demand curve and energy performance points are obtained at their intersections. Seismic evaluation of the one-storey, three-storey, five-storey, and seven-storey reinforced concrete building frames has been carried out using the proposed method. The results obtained from the proposed method are compared with those obtained from conventional capacity spectrum method as well as time-history analysis. The results show that the proposed method can be used effectively for seismic evaluation of structures. Like Leelataviwat’s method, the proposed method is also based on energy concept; the advantage of the proposed method over the Leelataviwat’s method is that the proposed method can be more comprehensively and easily applied to the practical analysis.

Spatially Varying Ground Motion Effects on Seismic Response of Adjacent Structures Considering Soil- Structure Interaction

Spatial variation of seismic ground motions is caused by incoherence effect, wave passage, and local site conditions. This study focuses on the effects of spatial variation of earthquake ground motion on the responses of adjacent reinforced concrete (RC) frame structures. The adjacent buildings are modeled considering soil-structure interaction (SSI) so that the buildings can be interacted with each other under uniform and non-uniform ground motions. Three different site classes are used to model the soil layers of SSI system. Based on fast Fourier transformation (FFT), spatially correlated non-uniform ground motions are generated compatible with known power spectrum density function (PSDF) at different locations. Numerical analyses are carried out to investigate the displacement responses and the absolute maximum base shear forces of adjacent structures subjected to spatially varying ground motions. The results are presented in terms of related parameters affecting the structural response using three different types of soil site classes. The responses of adjacent structures have changed remarkably due to spatial variation of ground motions. The effect can be significant on rock site rather than clay site.

SPT-based liquefaction potential assessment by relevance vector machine approach

This article adopts Relevance Vector Machine (RVM) for the determination of the liquefaction potential of soil based on Standard Penetration Test (SPT) data from Chi-Chi earthquake. RVM is a probabilistic sparse kernel model. This study uses RVM for solving binary classification problem. Two models (MODEL I and MODEL II) have been developed. Cyclic Stress Ratio and SPT blow count (N) have been used as input variables for MODEL I. MODEL II uses Peak Ground Acceleration (PGA) and N as input variables. The developed RVM model gives equations for the prediction of the liquefaction potential of soil. A comparative study has been presented between the developed RVM and the Artificial Neural Network (ANN). The results confirm that the developed RVM is a robust model for the prediction of the liquefaction potential of soil.