Robustness Assessment of Redundant Structural Systems Based on Design Provisions and Probabilistic Damage Analyses

• Main Authors: Panagiotis Spyridis, Alfred Strauss
• Format: Article
• Language: English
• Published: MDPI AG 2020-11-01
• Series: Buildings
• Subjects: robustness; redundancy; stochastic analysis; truss structures
• Online Access: https://www.mdpi.com/2075-5309/10/12/213

Typically in structural design, foreseeable loads are assumed in a structural design and dimensioning exercise and design material properties may be handled in a semi-probabilistic approach. Structures can, however, be exposed to largely unforeseeable events such as intense environmental phenomena, accidents, malicious acts, and planning or execution errors, in addition to degradation with time. Recent significant collapses have highlighted the fact that robustness is an indispensable integral part of the structural design and provisions in upcoming codes are currently expanding in this respect. The paper examines the practical significance of quantitative robustness indicators included in recent research and upcoming standards and it assesses their efficiency based on case studies. Moreover, it proposes a probabilistic numerical methodology for robustness assessment under uncertainty, and it demonstrates its practical applicability based on computations with indicative structural truss systems, i.e., multi-component systems. The proposed method allows for quantifiable and comparable robustness measures, which can be integrated in reliability-based design and structural health monitoring of engineering systems. The redundancy aspect of robustness is pronounced as a plausible quantitative performance indicator for multi-component systems. In particular, the robustness index combining reliability and redundancy of the elements is proven to be the most useful one out of the examined approaches. This probabilistic elaboration does not only account for the reasonable treatment of variability and randomness, but it allows for an inverse identification of the critical failure paths and the characterization of weak links in the systems.