PSI - Issue 78

Carla Grandón-Soliz et al. / Procedia Structural Integrity 78 (2026) 1505–1512

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Social media platforms revealed a surprisingly coherent depiction of the bridge’s dimensions, with independent posts by agencies such as Administradora Boliviana de Carreteras (via Facebook/X) confirming the structure’s measurements (322 m long, 120 m tall), and local news channels (Red Uno Sur) sharing video footage of the bridge being used for bungee jumping shortly after its completion. Posts tagged with #PuenteFisculco on TikTok and Instagram displayed consistent information about the bridge's geometry, while also highlighting its emergence as a regional tourism hotspot. These platforms offer visual documentation that, if systematically archived, can be used to evaluate surface conditions (e.g., crack formation, rusting, barrier wear). Satellite-based geospatial platforms (Google Earth, Bing Maps, Yandex Maps) and open map datasets (Mapcarta, OpenStreetMap) were used to geolocate the bridge (18°55 ’ 01 ’’ S, 65°25 ’ 29 ’’ W) and assess its regional connectivity and terrain context. Though no recent Mapillary or OpenStreetCam imagery exists for the bridge deck or understructure, the platforms offer future opportunities for community-sourced visual inspection, particularly if travelers or engineers are mobilized to contribute regular imagery. In addition, a Scribd-hosted technical project document authored by ABC Chuquisaca confirms the engineering methods and design values, including use of 42 precast segments per side and post-tensioning systems. This provides a reference framework to validate dimensions cited in informal sources. The collected dataset enabled the creation of a Crowd-Based Inspection Table 1 , cataloging each platform’s content, data type, and the inferred engineering or social insight. Analysis revealed consistency across independent public accounts in terms of bridge geometry, construction timeline, and social usage. However, no evidence was found of deterioration reports, maintenance schedules, or public safety complaints, suggesting a lack of ongoing participatory monitoring. While the absence of complaint may imply satisfactory structural integrity, it also reveals an opportunity for improvement in participatory infrastructure auditing through incentivized digital engagement. The Fisculco Bridge demonstrates that crowd-based inspection models, when structured and validated with official data, can contribute significantly to infrastructure transparency, early-warning systems, and public engagement. Fisculco Bridge serves as a replicable model of how bridges in low- and middle-income countries can benefit from integrated, community-augmented observation frameworks, particularly when paired with open mapping and social media ecosystems. At the same time, the analysis revealed a series of data gaps and inconsistencies. While crowd-sourced posts confirm dimensions and show active public interactions with the bridge (e.g., via tourism and photography), they lack detailed reporting on post-construction conditions such as visible wear, deck surface integrity, expansion joint behavior, or railing deterioration. Likewise, no posts were found that systematically monitor environmental risks, such as riverbank erosion or landslide-prone slopes around the abutments. Furthermore, mapping platforms such as Mapillary and OpenStreetCam currently lack recent street-level imagery of the bridge, and satellite platforms show only macro-level overviews without targeted change detection or historical layering. These gaps highlight a fundamental limitation of data collection from public platforms: while useful for capturing high-level visibility, dimensions, and engagement patterns, crowd-based data may fall short in providing granular or diagnostic information necessary for thorough structural health evaluation. Despite these challenges, the overall consistency in basic structural data across independent, non-institutional sources demonstrates a promising baseline. With proper validation methods, such as comparing against original engineering specifications and applying AI-assisted image analysis, these sources can support early detection of anomalies, low-cost monitoring extensions, and broader transparency in infrastructure governance.