• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Computers and Concrete
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• v.5 no.1
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• pp.37-60
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• 2008

Structural health monitoring of existing infrastructure is currently an important field of research, where elaborate experimental programs and advanced analytical methods are used in identifying the current state of health of critical and important structures. The paper outlines two methods of system identification of beam-like reinforced concrete structures representing bridges, through static measurements, in a distributed damage scenario. The first one is similar to the stiffness method, re-cast and the second one to flexibility method. A least square error (LSE) based solution method is used for the estimation of flexural rigidities and damages of simply supported, cantilever and propped cantilever beam from the measured deformation values. The performance of both methods in the presence of measurement errors is demonstrated. An experiment on an un-symmetrically damaged simply supported reinforced concrete beam is used to validate the developed method. A method for damage prognosis is demonstrated using a generalized, indeterminate, propped cantilever beam.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.443-450
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• 2003

System identification techniques can be used to build a baseline modal model for a flawed structure that has no modal information on its as-built state. The accuracy of a system identification proposed by Stubbs and Kim is analyzed for plate-girder bridges and its impact on the accuracy of damage detection in those structures is also analyzed. A laboratory-scale model plate-girder is experimentally tested and the initial four bending modes are examined for certain damage scenarios. The performance of individual baseline modal models is assessed by detecting damage in the model structure.

• Smart Structures and Systems
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• v.5 no.4
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• pp.427-442
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• 2009

In this paper, the acceptability of system identification results for health monitoring of instrumented bridges is addressed. This is conducted by comparing the confidence intervals of identified modal parameters for a bridge in California, namely Truckee I80/Truckee river bridge, with the change of these parameters caused by several damage scenarios. A challenge to the accuracy of the identified modal parameters involves consequences regarding the damage detection and health monitoring, as some of the identified modal information is essentially not useable for acquiring a reliable damage diagnosis of the bridge system. Use of strong motion data has limitations that should not be ignored. The results and conclusions underline these limitations while presenting the opportunities offered by system identification using strong motion data for better understanding and monitoring the health of bridge systems.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.56-64
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• 2008

To maintain effectively the functionality of major railroad facilities such as bridges, identifying and evaluating damage in a structure and taking appropriate action via continuous structural health monitoring are very important. However, most damage identification methods for structural health monitoring developed to date utilize modal domain responses which inevitably contain errors in transforming the domain of responses. In this paper, a damage identification method using time-domain operational deflection shapes is proposed. Since the proposed method utilizes time-domain responses, the error in the process of transformation to response domain can be avoided, and the accuracy of structural health evaluation can be improved. The feasibility of the proposed method is verified via a numerical example of a simple bridge structure.

• Smart Structures and Systems
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• v.8 no.2
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• pp.157-172
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• 2011

A new method for both local damage(s) identification and input excitation force identification of beam structures is presented using the dynamic response sensitivity-based finite element model updating method. The state-space approach is used to calculate both the structural dynamic responses and the responses sensitivities with respect to structural physical parameters such as elemental flexural rigidity and with respect to the force parameters as well. The sensitivities of displacement and acceleration responses with respect to structural physical parameters are calculated in time domain and compared to those by using Newmark method in the forward analysis. In the inverse analysis, both the input excitation force and the local damage are identified from only several acceleration measurements. Local damages and the input excitation force are identified in a gradient-based model updating method based on dynamic response sensitivity. Both computation simulations and the laboratory work illustrate the effectiveness and robustness of the proposed method.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.561-578
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• 2010

This study proposes a multi-level damage localization strategy to achieve an effective damage detection system for civil infrastructure systems based on wireless sensors. The proposed system is designed for use of distributed computation in a wireless sensor network (WSN). Modal identification is achieved using the frequency-domain decomposition (FDD) method and the peak-picking technique. The ASH (angle-between-string-and-horizon) and AS (axial strain) flexibility-based methods are employed for identifying and localizing damage. Fundamentally, the multi-level damage localization strategy does not activate all of the sensor nodes in the network at once. Instead, relatively few sensors are used to perform coarse-grained damage localization; if damage is detected, only those sensors in the potentially damaged regions are incrementally added to the network to perform finer-grained damage localization. In this way, many nodes are able to remain asleep for part or all of the multi-level interrogations, and thus the total energy cost is reduced considerably. In addition, a novel distributed computing strategy is also proposed to reduce the energy consumed in a sensor node, which distributes modal identification and damage detection tasks across a WSN and only allows small amount of useful intermediate results to be transmitted wirelessly. Computations are first performed on each leaf node independently, and the aggregated information is transmitted to one cluster head in each cluster. A second stage of computations are performed on each cluster head, and the identified operational deflection shapes and natural frequencies are transmitted to the base station of the WSN. The damage indicators are extracted at the base station. The proposed strategy yields a WSN-based SHM system which can effectively and automatically identify and localize damage, and is efficient in energy usage. The proposed strategy is validated using two illustrative numerical simulations and experimental validation is performed using a cantilevered beam.

• Smart Structures and Systems
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• v.15 no.3
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• pp.787-806
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• 2015

Due to corrosion, a large number of belt conveyors support structure in industrial plants have deteriorated. Severe corrosion may result in collapse of the structures. Therefore, practical and effective structural assessment techniques are needed. In this paper, damage identification methods based on two specific local vibration modes, named periodic and isolated local vibration modes, are proposed. The identification methods utilize the facts that support structures have many identical members repeated along the belt conveyor and there exist some local modes within a small frequency range where vibrations of these identical members are much larger than those of the other members. When one of these identical members is damaged, this member no longer vibrates in those modes. Instead, the member vibrates alone in an isolated mode with a lower frequency. A damage identification method based on frequencies comparison of these vibration modes and another method based on amplitude comparison of the periodic local vibration mode are explained. These methods do not require the baseline measurement records of undamaged structure. The methods is capable of detecting multiple damages simultaneously. The applicability of the methods is experimentally validated with a laboratory model and a real belt-conveyor support structure.

• Structural Engineering and Mechanics
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• v.44 no.5
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• pp.585-600
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• 2012

This paper presents a Modified Tikhonov Regularization (MTR) method in model updating for damage identification with model errors and measurement noise influences consideration. The identification equation based on sensitivity approach from the dynamic responses is ill-conditioned and is usually solved with regularization method. When the structural system contains model errors and measurement noise, the identified results from Tikhonov Regularization (TR) method often diverge after several iterations. In the MTR method, new side conditions with limits on the identification of physical parameters allow for the presence of model errors and ensure the physical meanings of the identified parameters. Chebyshev polynomial is applied to approximate the acceleration response for moderation of measurement noise. The identified physical parameter can converge to a relative correct direction. A three-dimensional unsymmetrical frame structure with different scenarios is studied to illustrate the proposed method. Results revealed show that the proposed method has superior performance than TR Method when there are both model errors and measurement noise in the structure system.

• Proceedings of the Korea Concrete Institute Conference
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• 1996.10a
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• pp.486-491
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• 1996

The purpose of present study is to propose a improved damage detection and assessment algorithm that has its basis on the method of system identification. This method allows the use of composite data which is constitute of static displacements and eigenmodes. In the dynamic test, thecurvature and slope of mode shape are introduced to formulate the error responses. The effectiveness of the proposed staristical system identification method is investigated through simulated and experimental studies. Real test data obtained from measurements are used to identify the actual location of damage and to revise the design variables in a concrete structure.