Vibration-Based Structural Damage Identification under Varying Temperature Effects

Huang, Minshui; Gül, Mustafa; Zhu, Hongping

doi:10.1061/(asce)as.1943-5525.0000829

Cited by 77 publications

(37 citation statements)

References 32 publications

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“…In order to predict the effect of groove shape and size on the burst pressure of notched HDPE pipes, it is necessary to establish a unified damage model considering the notch effect mentioned above. Until now, many damage models of metallic or non-metallic materials have been established in many published works [29,30,31,32,33,34]. One of the most famous damage model is the Linear Miner Damage (LMD) model [29], which defines the damage factor as (2)…”

Section: Damage Model Of Hdpe Pipes With Various Notchesmentioning

confidence: 99%

Burst Pressures of High-Density Polyethylene Pipes Considering the Notch Effect: Testing and Prediction

Zheng

Wang

Chen

2020

Journal of Testing and Evaluation

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：This work aims at testing and predicting burst pressures of HDPE pipes with various groove conditions. Four types of notches including U-type, V-type, L-Type (linear type) and R-type (rectangular type) with different depth ratios are discussed. A unified damage model is proposed to predict the damage behaviors of notched HDPE pipes for different notch shapes. Results indicate that the notch shape has an

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Section: Damage Model Of Hdpe Pipes With Various Notchesmentioning

confidence: 99%

Burst Pressures of High-Density Polyethylene Pipes Considering the Notch Effect: Testing and Prediction

Zheng

Wang

Chen

2020

Journal of Testing and Evaluation

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“…1), which assumes that elastic modulus of concrete and steel decreases with the increase of temperature. 24,33,34 Assume that material elastic modulus is temperature-dependent:…”

Section: Thermal-elastic Modulusmentioning

confidence: 99%

Structural Damage Identification Under Temperature Variations Based on PSO–CS Hybrid Algorithm

Huang

Cheng

Zhang

et al. 2019

Int. J. Str. Stab. Dyn.

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Using the variations in parameters to detect structural damages has been widely used in damage identi¯cation of structures. When exposed to varying temperatures, not only the displacements and stresses of a structure will change, but also the elastic modulus of the materials, such as concrete and steel, of which the structure is made. Since the variation in elastic modulus will result in the variation of the sti®ness of the structure, a damage identi¯cation method without considering the temperature e®ects is, in principle, unacceptable. In this study, a damage identi¯cation method using the particle swarm optimization combined with the cuckoo search (PSO-CS) under the noise and temperature environment is proposed. First, the temperature variations are combined with the elastic modulus variation for addressing the temperature e®ects in¯nite element model. Second, a PSO-CS hybrid algorithm is adopted, which applies the updated mechanism of PSO in CS. Third, objective functions comprised of di®erent modal messages with diverse weight coe±cients are constructed for the damage identi¯cation and validated by numerical analysis of a simply supported beam. The results show that the performance of the PSO-CS is better than either PSO or CS individually. Finally, the PSO-CS is applied to the damage identi¯cation of ASCE Benchmark frame, for which the results indicate a satisfactory accuracy of the e®ectiveness of the proposed scheme.

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“…In this section, to compare the computing performance of BBPSODJ, three common algorithms, such as BBPSO, PSO, and genetic algorithm (GA) [17], are introduced. ree test functions, Ackley, Schwefel, and Schaffer (Figure 3), are utilized to test the algorithms.…”

Section: Performance Evaluation Of the Proposed Algorithmsmentioning

confidence: 99%

“…e model-based method, such as model updating technology [14], is a conventional way to solve the problem of structural damage identification and location [15]. Even though this approach is limited by the inaccurate finite element model of the structure, it can obtain accurate damage identification results in location and extent, which have been proved by numerous experiments and research studies [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Identification Based on l₁Regularization and Bare Bones Particle Swarm Optimization with Double Jump Strategy

Huang

Lei

2019

Mathematical Problems in Engineering

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Structural damage identification (SDI) plays a major role in structural health monitoring (SHM), which has been demanded by researchers to better face the challenges in the aging civil engineering, such as bridge structure and building structure. Many methods have been developed for the application to the real structures, but there are still some difficulties which result in inaccurate, even false damage identification. As a variant of particle swarm optimization (PSO), bare bones particle swarm optimization (BBPSO) is a simple but very powerful optimization tool. However, it is easy to be trapped in the local optimal state like other PSO algorithms, especially in SDI problems. In order to improve its performance in SDI problems, this paper aims to propose a novel optimization algorithm which is named as bare bones particle swarm optimization with double jump (BBPSODJ) for finding a new solution to the SDI problem in SHM field. To begin with, after the introduction of sparse recovery theory, the mathematical model for SDI is established where an objective function based on l1 regularization is constructed. Secondly, according to the basic theory of the BBPSODJ, a double jump strategy based on the BBPSO is designed to enhance the dynamic of particles, and it is able to make a large change in particle searching scopes, which can improve the search behaviour of BBPSO and prevent the algorithm from being trapped into local minimum state. Thirdly, three optimization test functions and a numerical example are utilized to validate the optimization performance of BBPSO, traditional PSO, and genetic algorithm (GA) comparatively; it is obvious that the proposed BBPSODJ shows great self-adapting property and good performance in the optimization process by introducing the novel double jump strategy. Finally, in the laboratory, an experimental example of steel frame with 4 damage cases is implemented to further assess the damage identification capability of the BBPSODJ with l1 regularization. From the damage identification results, it can be seen that the proposed BBPSODJ algorithm, which is efficient and robust, has great potential in the field of SHM.

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Vibration-Based Structural Damage Identification under Varying Temperature Effects

Cited by 77 publications

References 32 publications

Burst Pressures of High-Density Polyethylene Pipes Considering the Notch Effect: Testing and Prediction

Burst Pressures of High-Density Polyethylene Pipes Considering the Notch Effect: Testing and Prediction

Structural Damage Identification Under Temperature Variations Based on PSO–CS Hybrid Algorithm

Structural Damage Identification Based on l₁Regularization and Bare Bones Particle Swarm Optimization with Double Jump Strategy

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Vibration-Based Structural Damage Identification under Varying Temperature Effects

Cited by 77 publications

References 32 publications

Burst Pressures of High-Density Polyethylene Pipes Considering the Notch Effect: Testing and Prediction

Burst Pressures of High-Density Polyethylene Pipes Considering the Notch Effect: Testing and Prediction

Structural Damage Identification Under Temperature Variations Based on PSO–CS Hybrid Algorithm

Structural Damage Identification Based on l1Regularization and Bare Bones Particle Swarm Optimization with Double Jump Strategy

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Structural Damage Identification Based on l₁Regularization and Bare Bones Particle Swarm Optimization with Double Jump Strategy