Integrated condition rating and forecasting method for bridge decks using Visual Inspection and Ground Penetrating Radar

Alsharqawi, Mohammed; Zayed, Tarek; Dabous, Saleh Abu

doi:10.1016/j.autcon.2018.01.016

Cited by 41 publications

(14 citation statements)

References 17 publications

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“…Once the process starts, the mechanical actions previously mentioned cannot be avoided [2], so the solution to maintain the resistance and durability of the reinforced concrete structure is to have an early detection of corrosion and its effects. However, visual inspection is only effective once the presence of corrosion is evident from the surface of the material [3], so the only actions that may be carried out will be repairing actions instead of preventive. There are different testing methods to evaluate corrosion, which offer different accuracy levels and produce more or less deterioration in the material.…”

Section: Introductionmentioning

confidence: 99%

Assessing Rebar Corrosion through the Combination of Nondestructive GPR and IRT Methodologies

Solla

Lagüela

Fernández³

et al. 2019

Remote Sensing

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Corrosion is one of the pathologies that most affects the resistance of reinforced concrete. There are numerous ancient structures still in use affected by corrosion that need proper evaluation and remedial treatment for their maintenance. In this sense, there has been an increasing tendency to use nondestructive testing techniques that do not alter the reinforcement elements of such vulnerable structures. This work presents a combined methodology by using ground penetrating radar (GPR) and infrared thermography (IRT) techniques for the detection and evaluation of corrosion. The methodology was applied to the case study of an old construction that belongs to the abandoned military battery of Cabo Udra (Galicia, Spain). The combination of these complementary techniques allowed for the identification of areas with different dielectric and thermal conductivity, as well as different reflection patterns and intensity of the GPR waves. Thus, from the analysis of the GPR signals and IRT images acquired, it was possible to interpret corroded areas and moisture, along with inner damages such as cracking and debonding. These pathologies have a direct effect on the durability and sustentation of a structure, while the knowledge of their existence might be useful for engineers engaged in the design of maintenance works.

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Section: Introductionmentioning

confidence: 99%

Assessing Rebar Corrosion through the Combination of Nondestructive GPR and IRT Methodologies

Solla

Lagüela

Fernández³

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The structural responses of bridges during operation can be used for condition diagnosis, wherein the main tasks are evaluating the safety status of bridges and identifying potential damage. Visual inspection [1][2][3][4][5], which manually checks the safety status of bridges by measuring cracks, defects and leakage, is an easy and popular way to assess the condition of bridges, however, the accuracy and quality of visual inspection diagnosis results are subjective and dependent on the diligence of the inspectors. Compared with visual inspection, structural health monitoring is a technique that continuously provides large amounts of reliable structural response data [6][7][8][9][10][11], thus, this technique is an effective way to assess the condition of bridges.…”

Section: Introductionmentioning

confidence: 99%

A Kalman Filter-Based Method for Diagnosing the Structural Condition of Medium- and Small-Span Beam Bridges during Brief Traffic Interruptions

Gao

Xiang²,

Liu

2020

Sensors

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Load tests are a popular way to diagnose the structural condition of bridges, however, such tests usually interrupt traffic for many hours. To address this issue, a Kalman filter-based method is proposed to diagnose the structural condition of medium- and small-span beam bridges by using the acceleration responses obtained from the bridge during a brief traffic interruption. First, a condition diagnosis feature based on the Kalman filter innovation (i.e., the optimal difference between the filter predictions and measured responses) is presented. Second, a condition diagnosis index, which is the energy ratio between the innovation and the measured acceleration, is generated by calculating the null space of the Hankel matrix consisting of condition diagnosis features. Then, on the basis of the novel detection, a method is used to diagnose the structural condition of a bridge during a brief traffic interruption. Finally, the validity and dependability of the proposed method is demonstrated through experimental tests with a model bridge and field tests on an actual bridge. Using the proposed method, the long-time interruption of traffic flow and the reliance on finite element model are effective avoided during the process of condition diagnosis of bridges.

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“…The efficient use of public funds for repairing and maintaining bridges requires an effective bridge asset management framework. Transportation management agencies worldwide have begun to adopt bridge management systems (BMS) to determine the optimum future bridge maintenance, repair, and rehabilitation (MRR) strategy at the lowest possible life-cycle cost based on forecasted bridge conditions [1][2][3][4]. Use of various forecasting models has played a critical role in predicting future bridge conditions for decision makers.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the accuracy of the decision-making relies heavily on the outcomes of a reliable bridge condition forecasting model ( [2,15]; Lu and Zheng 2017). Many recent researches focus on improving model forecasting accuracy and shed some light on improving forecasting accuracy [2,4,[12][13][14][15]. However, many of the researches ignore the forecasting reliability and provide incomplete picture regarding forecasting accuracy.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Bridge Component Ratings Using Ordinal Logistic Regression Model

Wang

Tolliver

2019

Mathematical Problems in Engineering

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Prediction of bridge component condition is fundamental for well-informed decisions regarding the maintenance, repair, and rehabilitation (MRR) of highway bridges. The National Bridge Inventory (NBI) condition rating is a major source of bridge condition data in the United States. In this study, a type of generalized linear model (GLM), the ordinal logistic statistical model, is presented and compared with the traditional regression model. The proposed model is evaluated in terms of reliability (the ability of a model to accurately predict bridge component ratings or the agreement between predictions and actual observations) and model fitness. Five criteria were used for evaluation and comparison: prediction error, bias, accuracy, out-of-range forecasts, Akaike’s Information Criteria (AIC), and log likelihood (LL). In this study, an external validation procedure was developed to quantitatively compare the forecasting power of the models for highway bridge component deterioration. The GLM method described in this study allows modeling ordinal and categorical dependent variable and shows slightly but significantly better model fitness and prediction performance than traditional regression model.

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Integrated condition rating and forecasting method for bridge decks using Visual Inspection and Ground Penetrating Radar

Cited by 41 publications

References 17 publications

Assessing Rebar Corrosion through the Combination of Nondestructive GPR and IRT Methodologies

Assessing Rebar Corrosion through the Combination of Nondestructive GPR and IRT Methodologies

A Kalman Filter-Based Method for Diagnosing the Structural Condition of Medium- and Small-Span Beam Bridges during Brief Traffic Interruptions

Prediction of Bridge Component Ratings Using Ordinal Logistic Regression Model

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