Byung Kwan Oh

GA-Based Multi-Objective Optimizationźfor Retrofit Design on a Multi-Core PC Cluster

This article presents a distributed nondominated sorting genetic algorithm II NSGA-II for optimal seismic retrofit design using buckling restrained braces BRBs on a cluster of multi-core PCs. In the formulation, two conflicting objective functions of the initial BRB installation cost required for seismic retrofitting and damage cost that can be incurred by earthquakes expected during the life cycle of the structure were minimized. Because time-consuming nonlinear structural analyses are required for fitness evaluations of individuals in every generation, parallelism at candidate design level or individual level is exploited by assigning fitness evaluations for individuals to slave core processors evenly. The distributed algorithm is applied to seismic retrofit design of 2D steel frame structure and 3D irregular reinforced concrete structure. The performance of the distributed NSGA-II was assessed based on three criteria: convergence of the distributed algorithm, efficiency of distributed computing, and quality of optimal solutions. Implementation of the distributed algorithm on the multi-core cluster consisting of up to 64 core processors resulted in relatively high speedups or efficiencies of the distributed optimization without deteriorating the quality of the optimal solutions.

Model Updating Technique Based on Modal Participation Factors for Beam Structures

This article proposes a model updating technique based on modal participation factors for a beam structure. In this model updating technique, the error functions of the dynamic characteristic differences between measurement and model are generated as the number of modes under consideration and minimized using the multiobjective optimization techniques. A modal influence factor defined by modal participation factors for each mode is presented for the selection of the best solution from among Pareto solutions. The selection rule represented in this article makes it possible to reflect the contributions of each mode on the behavior of a structure. The model is updated using natural frequencies measured in an impact hammer test of a beam structure and the validity of the updated model is confirmed by the strain responses measured from the test. It is found that the bending stiffness of the beam structure as the parameter for model updating can be identified by the proposed techniques. Furthermore, through comparing the models updated by the simple sum model updating and the technique in this research, it is verified that the proposed technique is more appropriate for the model updating.

Modal Response-Based Visual System Identification and Model Updating Methods for Building Structures

This article proposes a new system identification SI method using the modal responses obtained from the dynamic responses of a structure for estimating modal parameters. Since the proposed SI method visually extracts the mode shape of a structure through the plotting of modal responses based on measured data points, the complex calculation process for the correlation and the decomposition for vibration measurements required in SI methods can be avoided. Also, without dependence on configurations of SI methods inducing variations of modal parameters, mode shapes and modal damping ratios can be stably extracted through direct implementation of modal response. To verify the feasibility of the proposed method, the modal parameters of a shear frame were extracted from modal displacement data obtained from a vibration test, and the results were compared with those obtained from the existing frequency domain SI method. The proposed method introduces the maximum modal response ratio of each mode computed by modal displacement data, and from this, the contribution of each mode and each measured location to the overall structural response is indirectly evaluated. Moreover, this article proposes a model updating method establishing the error functions based on the differences between the analytical model and measurement for the natural frequencies and the modal responses reflecting both mode shape and modal contribution. The validity of the proposed method is verified through the response prediction and modal contributions of the models obtained from model updating based on dynamic displacement from a shaking table test for a shear-type test frame.

Evolutionary learning based sustainable strain sensing model for structural health monitoring of high-rise buildings

Abstract Strain sensor network-based structural health monitoring systems have been used to assess the safety of high-rise buildings. In consideration of life cycle of high-rise buildings, long-term measurement by sensors should be required. However, because of unpredictable problems such as the lack of durability of sensors and data loggers, disruption in communication, and loss of data, long-term strain measurement of major structural members is currently infeasible. For sustainable safety assessment of high-rise buildings, this paper presents a sustainable strain-sensing model that employs an artificial neural network (ANN) to estimate the strain responses of columns depending on the wind-induced behavior of high-rise buildings. The ANN model used in the paper is based on evolutionary learning consists of training in radial basis function neural network (RBFN) and evolving in genetic algorithm. In this evolutionary RBFN (ERBFN). Weights between layers are trained and variables of Gaussian function in the RBFN are evolved to estimate strain responses of the column of the high-rise building structure. A wind tunnel test was performed to produce wind data and strains in column members in a high-rise building model. In the wind tunnel test, a specimen consisting of a core, perimeter columns, and outriggers is used to simulate the conditions of typical high-rise buildings with a slenderness ratio of 5.0. The proposed model is trained and verified by using the wind data such as wind speeds and directions and the corresponding strains measured with fiber optic grating sensors. In addition to estimation of the maximum and minimum values of strains in vertical members in a high-rise building, it is found that the proposed model can build a relationship between the wind data and strain of vertical members.

Sensor-Free Stress Estimation Model for Steel Beam Structures Using a Motion Capture System

As a new paradigm for safety assessment of beam structures, this paper proposes a sensor-free monitoring model to estimate the stress distribution of steel beam structures with uncertain loads. The model consists of three steps: the use of a sensor- and wire-free measurement step using a motion capture system; a deformed shape estimation step employing cubic smoothing spline interpolation; and a stress distribution and maximum stress estimation step using the radius of curvature of the estimated deformed shape or a finite-element method. Therefore, unlike the conventional sensor-based structural health monitoring with lengthy cables, the model can determine the maximum stress in a non-contact manner without the deterioration of the measurement accuracy. The sensor-free stress estimation method was applied to estimate the stress distribution of a steel beam structure subjected to static loads applied at the mid-span of the beam by a hydraulic jack. The validity of the proposed method was confirmed by comparing the estimated stresses with stresses measured in static loading tests.

A strain measurement model using a limited number of sensors for steel beam structures subjected to uncertain loadings

The maximum stress of a structural member has been extensively adopted as a safety assessment indicator in structural health monitoring. Due to construction errors in the field and changes in the loading conditions during or after construction, it is impractical to accurately predict the location and magnitude of the maximum strain of a member a priori. To avoid the dependency of strain sensing methods on information of the structural and loading conditions, this paper proposes a strain distribution measurement model for steel beam structures subjected to uncertain loadings with uncertainties in magnitudes and shapes. With strains measured from a limited number of sensors, a general form equation of the strain distribution is determined for the estimation of the strain distribution. The performance of the strain distribution measurement model is verified by comparing estimated strain values from the proposed method and measured strains directly from fiber Bragg grating sensors or electrical strain gauges during static loading tests on single- and multi-span beam structures.

Analytical models for estimation of the maximum strain of beam structures based on optical fiber Bragg grating sensors

AbstractThe structural safety of a beam structure is assessed by a comparison between the maximum stress measured during monitoring and the allowable stress of the beam. However, the strain directly measured from a fiber Bragg grat- ing (FBG) strain sensor may not be identical with the actual maximum strain induced in the structural member. Unless a FBG strain sensor is installed exactly on where maximum strain occurs, the reliability of the evaluated safety based on the measured strain depends on the number and location of sensors. Therefore, in this paper, analytical models are presented for estimation of the maximum values of strains in a linear elastic beam using the local strains measured from FBG sensors. The model is tested in an experiment by comparing estimated maximum strain from FBG sensors and directly measured strain from electrical gages. For the assessment of safety of typical beam structures in buildings and infrastructures, analytical models for various loading and boundary conditions are...

Damage Detection Technique for Cold-Formed Steel Beam Structure Based on NSGA-II

Cold-formed steel is uniform in quality, suitable for mass production, and light in weight. It is widely used for both structural and nonstructural members in buildings. When it is used in a bending structural member, damage such as local buckling is considered to be more important than general steel members in terms of failure mode. However, preceding studies on damage detection did not consider the failure characteristics of cold-formed beam members. Hence, this paper proposes a damage detection technique that considers the failure mode of local buckling for a cold-formed beam member. The differences between the dynamic characteristics from vibration-based measurements and those from finite element model are set to error functions. The error functions are minimized by the optimization technique NSGA-II. In the damage detection, the location of local damage and the severity of damage are considered variables. The proposed technique was validated through a simulation of damage detection for a cold-formed steel beam structure example.

Maximum Stress Estimation Model for Multi-Span Waler Beams with Deflections at the Supports Using Average Strains

The safety of a multi-span waler beam subjected simultaneously to a distributed load and deflections at its supports can be secured by limiting the maximum stress of the beam to a specific value to prevent the beam from reaching a limit state for failure or collapse. Despite the fact that the vast majority of accidents on construction sites occur at waler beams in retaining wall systems, no safety monitoring model that can consider deflections at the supports of the beam is available. In this paper, a maximum stress estimation model for a waler beam based on average strains measured from vibrating wire strain gauges (VWSGs), the most frequently used sensors in construction field, is presented. The model is derived by defining the relationship between the maximum stress and the average strains measured from VWSGs. In addition to the maximum stress, support reactions, deflections at supports, and the magnitudes of distributed loads for the beam structure can be identified by the estimation model using the average strains. Using simulation tests on two multi-span beams, the performance of the model is evaluated by estimating maximum stress, deflections at supports, support reactions, and the magnitudes of distributed loads.

A Proposal of the Gage-Free Safety Assessment Technique for the Steel Beam Structure Under Uncertain Loads and Support Conditions Using Motion Capture System

Estimating the maximum stress through stress distribution of a structure is an important indicator for structural safety evaluation. Structural health monitoring can be used to do this with a variety of measuring equipment such as strain gage, LVDT, LDS. All the measuring equipment, however, has some weakness in the configuration of complex wire network and some inconvenience of replacing faulty sensors. Therefore, this paper suggests a technique that can estimate stress distribution of steel beam structure under uncertain load and support conditions by using motion capture system (MCS). MCS is a Vision-based Monitoring System, which measures 3D coordinates of multiple markers attached to the surface of steel beam without installing the complex wire network. In this study, the stress distribution is estimated from an analytic model by using displacement values measured by MCS. For the evaluation of the estimated stress distribution, comparing with the measured stress from ESG is performed.