• Smart Structures and Systems
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• v.25 no.3
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• pp.345-368
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• 2020

The present work proposes a self-controlled multi-stage optimization method for damage identification of structures utilizing standard particle swarm optimization (PSO) algorithm. Damage identification problem is formulated as an inverse optimization problem where damage severity in each element of the structure is considered as optimization variables. An efficient objective function is formed using the first few frequencies and mode shapes of the structure. This objective function is minimized by a self-controlled multi-stage strategy to identify and quantify the damage extent of the structural members. In the first stage, standard PSO is utilized to get an initial solution to the problem. Subsequently, the algorithm identifies the most damage-prone elements of the structure using an adaptable threshold value of damage severity. These identified elements are included in the search space of the standard PSO at the next stage. Thus, the algorithm reduces the dimension of the search space and subsequently increases the accuracy of damage prediction with a considerable reduction in computational cost. The efficiency of the proposed method is investigated and compared with available results through three numerical examples considering both with and without noise. The obtained results demonstrate the accuracy of the present method can accurately estimate the location and severity of multi-damage cases in the structural systems with less computational cost.

• Structural Engineering and Mechanics
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• v.87 no.6
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• pp.555-574
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• 2023

An efficient optimization algorithm and damage-sensitive objective function are two main components in optimization-based Finite Element Model Updating (FEMU). A suitable combination of these components can considerably affect damage detection accuracy. In this study, a new hybrid damage-sensitive objective function is proposed based on combining two different objection functions to detect the location and extent of damage in structures. The first one is based on Generalized Pseudo Modal Strain Energy (GPMSE), and the second is based on the element's Generalized Flexibility Matrix (GFM). Four well-known population-based metaheuristic algorithms are used to solve the problem and report the optimal solution as damage detection results. These algorithms consist of Cuckoo Search (CS), Teaching-Learning-Based Optimization (TLBO), Moth Flame Optimization (MFO), and Jaya. Three numerical examples and one experimental study are studied to illustrate the capability of the proposed method. The performance of the considered metaheuristics is also compared with each other to choose the most suitable optimizer in structural damage detection. The numerical examinations on truss and frame structures with considering the effects of measurement noise and availability of only the first few vibrating modes reveal the good performance of the proposed technique in identifying damage locations and their severities. Experimental examinations on a six-story shear building structure tested on a shake table also indicate that this method can be considered as a suitable technique for damage assessment of shear building structures.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.6
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• pp.678-685
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• 2012

The objective of this study is to monitor the health status of harbor caissons which have potential foundation damage. To obtain the objective, the following approaches are performed. Firstly, a structural damage monitoring(SDM) method is designed for interlocked multiple-caisson structures. The SDM method utilizes the change in modal strain energy to monitor the foundation damage in a target caisson unit. Secondly, a finite element model of a caisson system which consists of three caisson units is established to verify the feasibility of the proposed method. In the finite element simulation, the caisson units are constrained each other by shear-key connections. The health status of the caisson system against various levels of foundation damage is monitored by measuring relative modal displacements between the adjacent caissons.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.12
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• pp.2574-2580
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• 2002

In this paper, an experimental verification has been conducted for the frequency response function (FRF)-based structural damage identification method (SDIM) proposed for beam structures. The FRF-based SDIM requires the natural frequencies and mode shapes measured in the intact state and the FRF-data measured in the damaged state. Experiments are conducted for the cantilevered beam specimens with one slot and with three slots. It is shown that the proposed FRF-based SDIM provides damage identification results that agree quite well with true damage state.

• Computers and Concrete
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• v.16 no.5
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• pp.683-701
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• 2015

When approached using nonlinear finite element (FE) techniques, structural analyses generate, for real RC structures, large complex numerical problems. Damage is a major part of concrete behavior, and the discretization technique is critical to limiting the size of the problem. Based on previous work, the ${\mu}$ damage model has been designed to activate the various damage effects correlated with monotonic and cyclic loading, including unilateral effects. Assumptions are formulated to simplify constitutive relationships while still allowing for a correct description of the main nonlinear effects. After presenting classical 2D finite element applications on structural elements, an enhanced simplified FE description including a damage description and based on the use of multi-fiber beam elements is provided. Improvements to this description are introduced both to prevent dependency on mesh size as damage evolves and to take into account specific phenomena (permanent strains and damping, steel-concrete debonding). Applications on RC structures subjected to cyclic loads are discussed, and results lead to justifying the various concepts and assumptions explained.

• Journal of architectural history
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• v.24 no.5
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• pp.41-48
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• 2015

Quantitative analysis of termites damage is important in terms of conservation and maintenance of wooden cultural heritage buildings, because termites makes cavities and decreases the section area of wooden structural members. The purpose of this study is to forecast the range and spread of termites damage in the wooden structural members by using ultrasonic pulse velocity method. Ultrasonic pulse velocity has been used as one of non-destructive test to analysis the internal defect by using difference velocity between medium material and cavity. This method would be effective to analysis termites damages. From the result of the ultrasonic velocity test, the loss rate of area effected by termites damage had a strong correlation with ultrasonic velocity. And it is possible to predict the loss rate of area from by termites damage by using regression equation in the case of structural member of fine tree.

• Journal of the Korea institute for structural maintenance and inspection
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• v.3 no.1
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• pp.179-184
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• 1999

In this study, a method for damage assessment of existing structures is suggested using system identification approach. The natural frequencies of damaged structures are utilized to determine the size of damage. The SUMT algorithm is used to minimize error of the criterion function. The structural analysis is performed by using finite element method. Numerical examples are carried out to verify the validity of the proposed method and its computational procedures. And damage estimation of PSC beam is performed to demonstrate the effectiveness of the proposed method. From the results, it is found that the proposed SI method can be applied to estimate damage in existing structures accurately and rapidly.

• Journal of Korean Association for Spatial Structures
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• v.19 no.1
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• pp.93-100
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• 2019

There has been considerable recent interest in deep learning techniques for structural analysis and design. However, despite newer algorithms and more precise methods have been developed in the field of computer science, the recent effective deep learning techniques have not been applied to the damage detection topics. In this study, we have explored the structural damage detection method of truss structures using the state-of-the-art deep learning techniques. The deep neural networks are used to train knowledge of the patterns in the response of the undamaged and the damaged structures. A 31-bar planar truss are considered to show the capabilities of the deep learning techniques for identifying the single or multiple-structural damage. The frequency responses and the elasticity moduli of individual elements are used as input and output datasets, respectively. In all considered cases, the neural network can assess damage conditions with very good accuracy.

• Smart Structures and Systems
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• v.23 no.2
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• pp.107-122
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• 2019

As one of the most important parameters in structural health monitoring, structural frequency has many advantages, such as convenient to be measured, high precision, and insensitive to noise. In addition, frequency-change-ratio based method had been validated to have the ability to identify the damage occurrence and location. However, building a precise enough finite elemental model (FEM) for the test structure is still a huge challenge for this frequency-change-ratio based damage detection technique. In order to overcome this disadvantage and extend the application for frequencies in structural health monitoring area, a novel method was developed in this paper by combining the cross-model cross-mode (CMCM) model updating algorithm with the frequency-change-ratio based method. At first, assuming the physical parameters, including the element mass and stiffness, of the test structure had been known with a certain value, then an initial to-be-updated model with these assumed parameters was constructed according to the typical mass and stiffness distribution characteristic of shear buildings. After that, this to-be-updated model was updated using CMCM algorithm by combining with the measured frequencies of the actual structure when no damage was introduced. Thus, this updated model was regarded as a representation of the FEM model of actual structure, because their modal information were almost the same. Finally, based on this updated model, the frequency-change-ratio based method can be further proceed to realize the damage detection and localization. In order to verify the effectiveness of the developed method, a four-level shear building was numerically simulated and two actual shear structures, including a three-level shear model and an eight-story frame, were experimentally test in laboratory, and all the test results demonstrate that the developed method can identify the structural damage occurrence and location effectively, even only very limited modal frequencies of the test structure were provided.

• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.445-454
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• 2020

Structural damage identification (SDI) is a crucial step in structural health monitoring. However, some of the existing SDI methods cannot provide enough identification accuracy and efficiency in practice. A novel whale optimization algorithm (WOA) based method is proposed for SDI by weighting modal data and flexibility assurance criterion in this study. At first, the SDI problem is mathematically converted into a constrained optimization problem. Unlike traditional objective function defined using frequencies and mode shapes, a new objective function on the SDI problem is formulated by weighting both modal data and flexibility assurance criterion. Then, the WOA method, due to its good performance of fast convergence and global searching ability, is adopted to provide an accurate solution to the SDI problem, different predator mechanisms are formulated and their probability thresholds are selected. Finally, the performance of the proposed method is assessed by numerical simulations on a simply-supported beam and a 31-bar truss structures. For the given multiple structural damage conditions under environmental noises, the WOA-based SDI method can effectively locate structural damages and accurately estimate severities of damages. Compared with other optimization methods, such as particle swarm optimization and dragonfly algorithm, the proposed WOA-based method outperforms in accuracy and efficiency, which can provide a more effective and potential tool for the SDI problem.