• Computational Structural Engineering
• /
• v.18 no.3 s.69
• /
• pp.39-45
• /
• 2005

• Computers and Concrete
• /
• v.4 no.3
• /
• pp.167-186
• /
• 2007

Ductility based design of reinforced concrete structures implicitly assumes certain damage under the action of a design basis earthquake. The damage undergone by a structure needs to be quantified, so as to assess the post-seismic reparability and functionality of the structure. The paper presents an analytical method of quantification and location of seismic damage, through system identification methods. It may be noted that soft ground storied buildings are the major casualties in any earthquake and hence the example structure is a soft or weak first storied one, whose seismic response and temporal variation of damage are computed using a non-linear dynamic analysis program (IDARC) and compared with a normal structure. Time period based damage identification model is used and suitably calibrated with classic damage models. Regenerated stiffness of the three degrees of freedom model (for the three storied frame) is used to locate the damage, both on-line as well as after the seismic event. Multi resolution analysis using wavelets is also used for localized damage identification for soft storey columns.

• Smart Structures and Systems
• /
• v.13 no.5
• /
• pp.821-848
• /
• 2014

This paper presents a novel method to carry out monitoring of transport infrastructure such as pavements and bridges through the analysis of vehicle accelerations. An algorithm is developed for the identification of dynamic vehicle-bridge interaction forces using the vehicle response. Moving force identification theory is applied to a vehicle model in order to identify these dynamic forces between the vehicle and the road and/or bridge. A coupled half-car vehicle-bridge interaction model is used in theoretical simulations to test the effectiveness of the approach in identifying the forces. The potential of the method to identify the global bending stiffness of the bridge and to predict the pavement roughness is presented. The method is tested for a range of bridge spans using theoretical simulations and the influences of road roughness and signal noise on the accuracy of the results are investigated.

• International Journal of High-Rise Buildings
• /
• v.4 no.1
• /
• pp.9-25
• /
• 2015

Field measurements of various structures have been conducted for many purposes. Measurement data obtained by field measurement is very useful to determine vibration characteristics including dynamic characteristics such as the damping ratio, natural frequency, and mode shape of a structure. In addition, results of field measurements and modal identification can be used for modal updating of FEM analysis, for checking the efficiency of damping devices and so on. This paper shows some examples of field measurements and modal identification for structural health monitoring. As the first example, changes of dynamic characteristics of a 15-story office building in four construction stages from the foundation stage to completion are described. The dynamic characteristics of each construction stage were modeled as accurately as possible by FEM, and the stiffness of the main structural frame was evaluated and the FEM results were compared with measurements performed on non-load-bearing elements. Simple FEM modal updating was also applied. As the next example, full-scale measurements were also carried out on a high-rise chimney, and the efficiency of the tuned mass damper was investigated by using two kinds of modal identification techniques. Good correspondence was shown with vibration characteristics obtained by the 2DOF-RD technique and the Frequency Domain Decomposition method. As the last example, the wind-induced response using RTK-GPS and the feasibility of hybrid use of FEM analysis and RTK-GPS for confirming the integrity of structures during strong typhoons were shown. The member stresses obtained by hybrid use of FEM analysis and RTK-GPS were close to the member stresses measured by strain gauges.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.36 no.12
• /
• pp.1635-1642
• /
• 2012

The identification of the dynamic properties of structural joints is important for predicting the dynamic behavior of assembled systems. However, the identification of the properties using analytical or experimental approaches is extremely difficult or even impossible. Several studies have proposed hybrid or synthesis methods that simultaneously used analytical and experimental approaches to identify the dynamic properties of a joint. However, among the many types of joints, only the bolt joint was treated as a practical example in these studies. In this study, for a simple assembly system comprising two plates and one hinge joint, a simple methodology involving the use of the static-based subpart analysis method to identify the dynamic properties is proposed. Finally, the proposed method is applied to a glove box in a passenger vehicle that includes hinge joints.

• Smart Structures and Systems
• /
• v.17 no.6
• /
• pp.935-956
• /
• 2016

With the help of advanced image acquisition and processing technology, the vision-based measurement methods have been broadly applied to implement the structural monitoring and condition identification of civil engineering structures. Many noncontact approaches enabled by different digital image processing algorithms are developed to overcome the problems in conventional structural dynamic displacement measurement. This paper presents three kinds of image processing algorithms for structural dynamic displacement measurement, i.e., the grayscale pattern matching (GPM) algorithm, the color pattern matching (CPM) algorithm, and the mean shift tracking (MST) algorithm. A vision-based system programmed with the three image processing algorithms is developed for multi-point structural dynamic displacement measurement. The dynamic displacement time histories of multiple vision points are simultaneously measured by the vision-based system and the magnetostrictive displacement sensor (MDS) during the laboratory shaking table tests of a three-story steel frame model. The comparative analysis results indicate that the developed vision-based system exhibits excellent performance in structural dynamic displacement measurement by use of the three different image processing algorithms. The field application experiments are also carried out on an arch bridge for the measurement of displacement influence lines during the loading tests to validate the effectiveness of the vision-based system.

• Structural Engineering and Mechanics
• /
• v.29 no.2
• /
• pp.155-169
• /
• 2008

A MOM-based algorithm (MOMA) has been developed for moving force identification from dynamic responses of bridge in the companion paper. This paper further evaluates and investigates the properties of the developed MOMA by experiment in laboratory. A simply supported bridge model and a few vehicle models were designed and constructed in laboratory. A series of experiments have then been conducted for moving force identification. The bending moment and acceleration responses at several measurement stations of the bridge model are simultaneously measured when the model vehicle moves across the bridge deck at different speeds. In order to compare with the existing time domain method (TDM), the best method for moving force identification to date, a carefully comparative study scheme was planned and conducted, which includes considering the effect of a few main parameters, such as basis function terms, mode number involved in the identification calculation, measurement stations, executive CPU time, Nyquist fraction of digital filter, and two different solutions to the ill-posed system equation of moving force identification. It was observed that the MOMA has many good properties same as the TDM, but its CPU execution time is just less than one tenth of the TDM, which indicates an achievement in which the MOMA can be used directly for real-time analysis of moving force identification in field.

• Structural Engineering and Mechanics
• /
• v.50 no.1
• /
• pp.105-119
• /
• 2014

This study presents a steel container crane movement analysis and assessment based on structural health monitoring (SHM). The accelerometers are used to monitor the dynamic crane behavior and a 3-D finite element model (FEM) was designed to express the static displacement of the crane under the different load cases. The multi-input single-output nonlinear autoregressive neural network with external input (NNARX) model is used to identify the crane dynamic displacements. The FEM analysis and the identification model are used to investigate the safety and the vibration state of the crane in both time and frequency domains. Moreover, the SHM system is used based on the FEM analysis to assess the crane behavior. The analysis results indicate that: (1) the mean relative dynamic displacement can reveal the relative static movement of structures under environmental load; (2) the environmental load conditions clearly affect the crane deformations in different load cases; (3) the crane deformations are shown within the safe limits under different loads.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1997.04a
• /
• pp.89-96
• /
• 1997

Most civil engineering structures, such as highway bridges, towers, power plants and offshore structures suffer structural damages over their service lives caused by adverse loading such as heavy transportation loads, machine vibrations, earthquakes, wind and wave forces. Especially, if excessive load would be acted on the structure, general or partial stiffness should be degraded suddenly and service lives should be shortened eventually For realistic damage assessment of these civil structures, System Identification method using only structure dynamic response data with unknown input excitation is required and thus becoming more challenging problem. In this paper, an improved Iterative Least Squares method is proposed, which seems to be very efficient and robust method, because only the dynamic response data such as acceleration, velocity and displacement is used without input data, and no information on the modal properties is required. The efficiency and robustness of the proposed method is proved by numerical problems and real single span beam model test.

• Structural Engineering and Mechanics
• /
• v.10 no.4
• /
• pp.359-369
• /
• 2000

Random loading identification has long been a difficult problem for Multi-Input-Multi-Output (MIMO) structure. In this paper, the Pseudo Excitation Method (PEM), which is an exact and efficient method for computing the structural random response, is extended inversely to identify the excitation power spectral densities (PSD). This identified method, named the Inverse Pseudo Excitation Method (IPEM), resembles the general dynamic loading identification in the frequency domain, and can be used to identify the definite or random excitations of complex structures in a similar way. Numerical simulations are used to reveal the the difficulties in such problems, and the results of some numerical analysis are discussed, which may be very useful in the setting up and processing of experimental data so as to obtain reasonable predictions of the input loading from the selected structural responses.