This paper summarizes the different activities undertaken in order to identify the main causes of the collapse of the Space Building, a 27-story reinforced concrete framed building in Medellín, Colombia. Site investigations, field and laboratory tests, and a detailed analytical work including structural analysis under different considerations were carried out. The main cause of the collapse was associated with the deficient structural capacities of the columns of the building in relation to the dead and service loads imposed at the time of the collapse. The occurrence of high differential settlements, the high compressive stresses in main columns, the repair works that were carried out minutes before the collapse, and the probable shear failure of beams and compressive failure of partitions walls during repair works were identified as probable secondary causes and detonating factors of the building collapse. The authors of this investigation considered that if the building had been designed adequately using the Colombian building design codes available at that time, it would not have collapsed, even if most of the aforementioned detonating events had occurred.

Early disaster responses in damaged areas after a large earthquake are indispensable for stakeholders to assess and grasp the impacts such as building and infrastructure damage and disrupted community functionality as soon as possible. This study introduces a quick estimation model for mapping seismic intensities and building losses in Bogotá, the capital city of Colombia. The model uses ground motion records in the seismic network, soil maps of average shear-wave velocity in the upper 30 m (Vs30) with site amplifications, building inventory, and vulnerability functions for all building types. The spatial distribution of ground motion intensities, including spectral accelerations, was estimated by interpolating the observed seismic intensities with the Vs30-based site amplifications. The losses (repair cost) for all the buildings were evaluated by integrating the estimated spectral accelerations, the building inventory, and the vulnerability functions. The spatial distributions of seismic intensities and building losses can be computed within a few minutes immediately after triggering earthquake motions in the seismic network. The proposed model demonstrates evaluations of the impacts for the Mw6.0 earthquake that occurred on December 2019 and an earthquake scenario with Mw7.0 from an active fault near the Bogotá region.

Structures and infrastructure are often densely distributed in coastal regions and highly susceptible to hurricane-induced stressors. Evidence from past hurricane events and experimental studies demonstrates that neighboring structures significantly alter the structure-to-structure demand induced by hurricane-related stressors, such as storm surge and wave loads, thereby influencing the damage pattern observed across a region. Capturing this modified load requires detailed computational fluid dynamics (CFD) analysis, making it difficult to incorporate such effects into current regional risk assessments. This paper proposes a framework for incorporating the modified loads directly into the formulation of parameterized fragility functions in a practical way. Our methodology uses a set of parameters to describe the portfolio layout, and its associated effects on the load modifications (i.e., shielding or channeling) are captured from detailed CFD analyses. The influence of the modified loads on the structural failure probabilities is embedded within the fragility model by considering the portfolio layout parameters during the model fitting procedure. To reduce the high computational expense of CFD analysis, active learning algorithms are leveraged to selectively choose only the most critical parameter combinations for numerical analysis. The proposed framework is demonstrated for aboveground storage tanks (ASTs) located within a tank farm, as a representative structural portfolio. The derived parameterized fragility models reveal that failure probability estimates vary significantly depending on the spatial arrangement of structures within the portfolio. Subsequent scenario-based risk estimates leveraging these fragilities show differences of 21.5% to 54.5% in failure probability and 31.4% in expected volume of hazardous material spills, compared to estimates obtained without considering the influence of neighboring structures. The proposed framework offers a path to improve risk assessment of densely arranged portfolios. We expect our contribution to render tailored structural interventions by revealing heterogeneous impacts, thus mitigating (or reducing) investments when dealing with coastal structural portfolios.