Development of an Efficient Maintenance Strategy for Corroded Steel Bridge Infrastructures

Appuhamy, J. M. R. S.; Ohga, Mitao; Kaita, Tatsumasa; Chun, Pang‐jo

doi:10.1061/(asce)be.1943-5592.0000381

Cited by 11 publications

(2 citation statements)

References 7 publications

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“…This study dealt with floating and delamination of concrete, however, since civil engineering structures frequently use not only concrete but also steel members, we would like to link the developed method in this study to the detection of damage in steel members. For example, the authors have shown in [22]- [25] that corrosion and fatigue of steel members have a significant effect on the safety of structures, but an effective method to evaluate them from a distance has not been established, which is a topic that needs to be solved. Since corrosion and fatigue may affect the temperature distribution, our method using infrared images may be useful to detect them.…”

Section: Discussionmentioning

confidence: 99%

Development of a Concrete Floating and Delamination Detection System Using Infrared Thermography

Chun

Hayashi

2021

IEEE/ASME Trans. Mechatron.

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Spalling of concrete fragments due to the deterioration of concrete structures can cause property damage or serious and even fatal accidents; thus, there is a need to detect such deterioration. Generally, the hammering test is employed as the main inspection method to prevent such concrete spalling; however, it requires close contact with the structure being tested. Getting close to the structure for inspection is expensive and time consuming, and if the structure is high up, there is a risk of falling. Therefore, in this study, we developed a system for inspecting concrete structures without approaching them, using infrared thermography. In order to detect floating and delamination using infrared thermography, it is necessary to find temperature irregularities caused by such damage from an infrared image, but such an inspection method has not been realized so far. There are two main reasons for this. First, it is difficult to evaluate whether the concrete structure is in an appropriate temperature condition suitable for detecting the floating and delamination. Second, it is difficult to detect temperature irregularities caused by floating and delamination among the various causes of temperature irregularities. In this study, we resolved these issues by developing equipment to investigate whether the object is in an appropriate temperature condition for proper photography and by developing a machine learning-based method to automatically detect only the temperature irregularities caused by floating and delamination. By resolving these issues, we have developed a promising novel inspection method for the prevention of concrete spalling, which is reported in this article.

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Section: Discussionmentioning

confidence: 99%

Development of a Concrete Floating and Delamination Detection System Using Infrared Thermography

Chun

Hayashi

2021

IEEE/ASME Trans. Mechatron.

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“…In recent years, the problem of bridge deterioration has become increasingly serious in various parts of the world. For example, in Japan a large number of bridges that were built during the high economic growth period of the 1960s and 1970s are approaching the age of 50 years, which is generally considered the point at which deterioration begins [ 1 , 2 , 3 ]. In the United States, according to the Infrastructure Report Card by the American Society of Civil Engineers, in 2017 approximately 4/10 of 614,387 bridges were more than 50 years old [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Development of a Machine Learning-Based Damage Identification Method Using Multi-Point Simultaneous Acceleration Measurement Results

Chun

Yamane

Izumi

et al. 2020

Sensors

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It is necessary to assess damage properly for the safe use of a structure and for the development of an appropriate maintenance strategy. Although many efforts have been made to measure the vibration of a structure to determine the degree of damage, the accuracy of evaluation is not high enough, so it is difficult to say that a damage evaluation based on vibrations in a structure has not been put to practical use. In this study, we propose a method to evaluate damage by measuring the acceleration of a structure at multiple points and interpreting the results with a Random Forest, which is a kind of supervised machine learning. The proposed method uses the maximum response acceleration, standard deviation, logarithmic decay rate, and natural frequency to improve the accuracy of damage assessment. We propose a three-step Random Forest method to evaluate various damage types based on the results of these many measurements. Then, the accuracy of the proposed method is verified based on the results of a cross-validation and a vibration test of an actual damaged specimen.

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Protesting the Decline While Predicting the Demise of Clinical Psychology: Can We Avoid a Total Collapse?

Overholser

2014

J Contemp Psychother

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Development of an Efficient Maintenance Strategy for Corroded Steel Bridge Infrastructures

Cited by 11 publications

References 7 publications

Development of a Concrete Floating and Delamination Detection System Using Infrared Thermography

Development of a Concrete Floating and Delamination Detection System Using Infrared Thermography

Development of a Machine Learning-Based Damage Identification Method Using Multi-Point Simultaneous Acceleration Measurement Results

Protesting the Decline While Predicting the Demise of Clinical Psychology: Can We Avoid a Total Collapse?

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