Extreme events (i.e. terrorist attacks, vehicle impacts, explosions, etc.) often cause local damage to building structures and pose a serious threat when one or more vertical load-bearing components fail, leading to the progressive collapse of the entire structure or a large part of it. Since the beginning of the 21st century there has been growing interest in the risks associated with extreme events, especially after the attacks on the Alfred P. Murrah Federal Building in Oklahoma in 1995 and on the World Trade Center in New York in 2001. The accent is now on achieving resilient buildings that can remain operational after such an event, especially when they form part of critical infrastructures, are occupied by a large number of people, or are open to the public. This paper presents an ambitious review that describes all the main advances that have taken place since the beginning of the 21st century in the field of progressive collapse and robustness of buildings.
The response of calcium silicate unreinforced masonry construction to horizontal cyclic loading has recently become the focus of experimental and numerical research, given its extensive use in some areas of the world that are now exposed to induced earthquakes (eg,...
Significant research effort has been devoted in recent years to the evaluation of the capacity of steel frame structures to resist progressive collapse after sudden column loss. Due to the complex load-structure interaction and material behaviour, it can be very difficult to evaluate the ultimate capacity of structural components using current analytical methods. Therefore considerable research effort has been directed to experimental testing and sophisticated numerical simulations. Although sudden column loss is a dynamic process, most experimental studies on fullscale or scaled down specimens were performed under quasi-static loads. This paper presents the results of a study devoted to the evaluation of steel frame response following the loss of a column. Advanced numerical models are calibrated using experimental test results and dynamic increase factors are studied. Several full-scale structures are investigated for a sudden column loss scenario.
Seismic assessment of existing heritage buildings remains a challenging task. There is a high level of complexity and uncertainty compared with the assessment of standard buildings. Heritage masonry churches are usually prone to partial collapses during earthquake due to local loss of stability, and exhibit particular seismic vulnerabilities. An important step in the seismic analysis of heritage masonry buildings is the detection of local mechanisms. The Italian Building Code provides a simplified approach (LV1-churches) to assess the vulnerability of heritage churches evaluating and comparing 28 potential mechanisms. A general index of vulnerability and hierarchy between mechanisms is thereby provided. Verification of safety against local mechanisms can also be carried out using the kinematic approach. This procedure is based on evaluating the horizontal action needed to activate out-of-plane collapse mechanisms. Based on a full-scale study (Sion Cathedral), this paper evaluates the reliability of the “LV1-church” approach and of the kinematic approach through a comparison with the results obtained with a complex 3D model using the Applied Element Method.
A large number of buildings in regions with low to medium seismic hazard have been designed without considering earthquake actions. Retrofitting of all buildings that fail to meet modern code requirements is economically, technically and environmentally unsustainable. Decision-making regarding retrofitting necessity and prioritization is complex. Ambient vibrations are non-destructive and easy to measure, and thus an attractive data source. However, ambient vibrations have very low amplitudes, which potentially lead to sensitivity to testing conditions and stiffness contributions from non-structural elements. Seismic assessment necessitates non-linear behavior extrapolation from linear measurements, which results in biased model predictions.
