Probabilistic Loss Assessment for the Typology of Non-Ductile Reinforced Concrete Structures with Flat Slabs, Embedded Beams, and Unreinforced Infill Masonry

• Published in: Buildings (Basel) 2024-10, Vol.14 (10), p.3158
• Main Authors: Guamán-Naranjo, Mauricio, Poveda-Hinojosa, José, Haro-Báez, Ana Gabriela
• Format: Article
• Language: English
• Subjects: annual loss ratio; Buildings; Cities; Computing time; Concrete; Concrete slabs; Concrete structures; Construction; Earthquakes; Ecuador; Failure; Fatalities; Fragility; Housing; infill masonry; Masonry; Policy and planning; Population growth; Probabilistic methods; Redevelopment; Reinforced concrete; Residential areas; Residential buildings; Risk assessment; Seismic activity; Seismic engineering; Seismic hazard; seismic risk; Slabs; Typology; Urban planning; vulnerability
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings14103158

Quito, the capital of Ecuador, a development pole, has experienced a population growth of 9% in the last five years. The structural system commonly chosen for housing is reinforced concrete frames with flat slabs, embedded beams, and masonry infill. This typology covers approximately 60% of the residential buildings in the city. Adding to the site’s seismic hazard, this fact results in a city with a high seismic risk. The research presented here is carried out within a probabilistic framework to determine the economic consequences of the main structural typology in the city. The methodology defines the seismic hazard by scaling a database of 200 records to the design spectrum. It models the typology to capture the variability between structures with a solid parametric study. Each capacity curve is analyzed through a nonlinear time history analysis using an equivalent one-degree-of-freedom system. The results show an average annual loss ratio of 0.16%. This metric indicates the vulnerability of the typology and the high repair costs of buildings that will be observed in case of an earthquake. The practical implications of these findings are significant as they contribute to urban planning and policy decisions. Finally, it is observed that the probabilistic method used efficiently generates fragility and vulnerability curves, saving computational time and obtaining expected results.