Structural Identification for Condition Assessment: Experimental Arts

Aktan, A. Emin; Farhey, Daniel N.; Helmicki, Arthur J.; Brown, David L.; Hunt, Victor J.; Lee, Kuo-Liang; Levi, A

doi:10.1061/(asce)0733-9445(1997)123:12(1674)

Cited by 149 publications

(60 citation statements)

References 12 publications

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“…Although some researchers have pointed out the intrinsic difficulties in performing vibration testing in large bridges, such as nonstationary excitation (Catbas et al 2007), nonlinear boundary or continuity conditions, and nonideal connections/interfaces between structural members and components (Farhey 2005), it is unquestionable that dynamic testing stands as an innovative way of great potential in the structural identification of bridges for condition assessment (Aktan et al 1997;DeWolf et al 2002). It becomes imperative when assessing the seismic vulnerability of a structure and corresponding retrofit design (Cheung et al 2007), and can play a decisive role in the rehabilitation of centenary steel bridges, both in supporting the project design and validating their performance for the new structural condition.…”

Section: Dynamic Testing In the Assessment Of Old Steel Bridgesmentioning

confidence: 99%

Modal Analysis for the Rehabilitation Assessment of the Luiz I Bridge

et al. 2014

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Dynamic testing in the condition assessment of existing bridges to support their rehabilitation and strengthening designs is becoming a common procedure, yet additional dynamic testing after rehabilitation is rare. Nevertheless, combining the results of dynamic testing before and after bridge rehabilitation produces unique and valuable information regarding changes occurred in a bridge's dynamic properties. Such dual testing would allow researchers to verify the behavior predicted at the design stage and to assess the stiffness variation, which would eliminate the need for mandatory field static testing. This article reports the modal analysis of a unique centenary steel-arch bridge that was recently rehabilitated and strengthened. The ambient vibration test conducted after the construction works is described, and the data are compared with those collected before the rehabilitation. Structural identification is completed by means of experimentally validated threedimensional (3D) finite-element (FE) models simulating both phases. The results bring to light important findings concerning variation in bridge stiffness, changes in the modal parameters, and the impact of support conditions and structure of decks in bridge dynamic properties.

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Section: Dynamic Testing In the Assessment Of Old Steel Bridgesmentioning

confidence: 99%

Modal Analysis for the Rehabilitation Assessment of the Luiz I Bridge

et al. 2014

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“…Solutions to monitor structures usually consists in embedded sensors to detect structural stiffness changes or corrosion initiation, e.g., vibrating wires [2] and optical fibres [3], to mention a few. The results from these are then associated with the presence of damages in concrete.…”

Section: Introductionmentioning

confidence: 99%

Concrete Cracks Detection Based on Deep Learning Image Classification

Silva

Lucena

2018

The 18th International Conference on Experimental Mechanics

151

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Abstract:This work aims at developing a machine learning-based model to detect cracks on concrete surfaces. Such model is intended to increase the level of automation on concrete infrastructure inspection when combined to unmanned aerial vehicles (UAV). The developed crack detection model relies on a deep learning convolutional neural network (CNN) image classification algorithm. Provided a relatively heterogeneous dataset, the use of deep learning enables the development of a concrete cracks detection system that can account for several conditions, e.g., different light, surface finish and humidity that a concrete surface might exhibit. These conditions are a limiting factor when working with computer vision systems based on conventional digital image processing methods. For this work, a dataset with 3500 images of concrete surfaces balanced between images with and without cracks was used. This dataset was divided into training and testing data at an 80/20 ratio. Since our dataset is rather small to enable a robust training of a complete deep learning model, a transfer-learning methodology was applied; in particular, the open-source model VGG16 was used as basis for the development of the model. The influence of the model's parameters such as learning rate, number of nodes in the last fully connected layer and training dataset size were investigated. In each experiment, the model's accuracy was recorded to identify the best result. For the dataset used in this work, the best experiment yielded a model with accuracy of 92.27%, showcasing the potential of using deep learning for concrete crack detection.

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“…A significant research effort has focussed on the condition assessment of existing bridges, and relevant research has accelerated in recent years (Aktan et al 1996(Aktan et al ,1997(Aktan et al ,1998Enright and Frangopol 1999;Wahab and Roeck 1999). However, most investigations were based on field testing and numerical analysis without attempts to quantify structural condition.…”

Section: Introductionmentioning

confidence: 99%

Bridge Structural Condition Assessment Using Systematically Validated Finite-Element Model

Xia

2004

J. Bridge Eng.

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Structural condition assessment of highway bridges is largely based on visual observations described by subjective indices, and it is necessary to develop methodology for accurate and reliable condition assessment of aging and damaged structures. This paper presents a method using a systematically validated finite element model for quantitative condition assessment of a damaged reinforced concrete bridge deck structure, including damage location and extent, residual stiffness evaluation and loadcarrying capacity assessment. In a trial of the method in a cracked bridge beam, the residual stiffness distribution was determined by model updating thereby locating the damage in the structure. Furthermore the damage extent was identified through a defined damage index and the residual load-carrying capacity was estimated.

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Structural Identification for Condition Assessment: Experimental Arts

Cited by 149 publications

References 12 publications

Modal Analysis for the Rehabilitation Assessment of the Luiz I Bridge

Modal Analysis for the Rehabilitation Assessment of the Luiz I Bridge

Concrete Cracks Detection Based on Deep Learning Image Classification

Bridge Structural Condition Assessment Using Systematically Validated Finite-Element Model

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