PSI - Issue 64

Konrad Bergmeister et al. / Procedia Structural Integrity 64 (2024) 14–20 Konrad Bergmeister / Structural Integrity Procedia 00 (2019) 000 – 000

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3.2. Assessing defect data to perform risk assessments

Delivering the damage data visually through a BIM representation effectively communicates the critical information that an engineer is interested in; however, the vast volume of inspections requires additional tools to facilitate current needs. For this reason, the proposed methodology attempts to automatically process the defect data and provide risk estimates for the bridge components based on simplified and intuitive engineering judgement that incorporates theoretical knowledge. Seven distinct causes (diseases) of bridge deterioration are assessed and briefly described hereafter. At the same time, risk scores for each are automatically calculated to facilitate the inspection process in prioritizing the bridges and components of primary interest. In particular, the risk arising from each defect of a bridge component is calculated. For example, in the case of a crack defect in a reinforced concrete bridge component, the risk is determined through the crack’s location within the component, its extent (i.e., size) and its orientation. This information is the input to engineering-based algorithms that are developed to provide a risk score Defect Risk i for each crack i , and for each of the following five possible diseases: 1) bending, 2) shear, 3) torsion, 4) concentrated loads and 5) bond-related split. Further elaborating the influence of crack location, width and orientation, and focusing on the calculation of Defect Risk i for the bending individual section force caused diseases, it is noted that a sounded engineering model is needed. Research has been carried out to generalize as much as possible and to describe in a transparent way the calculation of the Component and the Structure Risk. Following the calculation of Defect Risk i (separately for each crack i and each of these diseases), the overall Component Risk that originates from n cracks within each component is calculated following the assumption of independence between the cracks. In this way, the total risk (or probability) of the element being susceptible to each of the five diseases is given by equation 1, / = 1− ∏(1− ) =1 (1) In addition to the five structural-induced diseases mentioned above, carbonation and chloride content are similarly investigated. In the case of these two environmentally-induced causes of bridge deterioration, the exposure of a given bridge (and its components individually) plays a critical role, together with the bridge age, the material class and the concrete cover thickness. The simplified component risk score on carbonation and chloride is calculated by comparing the concrete cover thickness with the calculated carbonation or chloride depth. 4. Conclusions The need to inspect the infrastructure portfolio efficiently and reliably, particularly bridges, is increasingly critical nowadays as many assets exceed their projected (and designed) lifetime. To this end, many research efforts focus on deploying a higher level of automation than current inspection processes in search of efficiency, increased quality and reduced cost. The rapid technological advances in Unmanned Aerial Vehicles (UAV) and the quality of UAV-obtained images of such infrastructure raised the promise of bridge inspections relying on automated image recognition, 3D digital models and Building Information Modelling, under the notion that bridge defect data will be easier to access, store and assess. The current work discusses these advances and proposes the importance of automatically identifying bridge and component risk as a causal factor that explains the observable defects. As a starting point, six visually detectable defects can be automatically identified. Through engineering-based empirical models, seven causal conditions (diseases) are identified and assigned with a risk score according to which they are expected to contribute to the observable defects and overall bridge deterioration. Such a framework allows the engineering community to prioritize bridge inspection and maintenance, promoting safety and informed decision-making.