Robust design optimization to account for uncertainty in the structural design process

Abstract

Structural systems are subject to inherent uncertainties due to the variability in many hard-to-control 'noise factors' including but not limited to external loads, material properties, and construction workmanship. Two design methodologies were developed to quantify the variability associated with the design process: Allowable Stress Design (ASD) and Load and Resistance Factor Design (LRFD). These traditional approaches explicitly recognize the presence of uncertainty, however they do not take robustness against this uncertainty into consideration. Overlooking robustness against uncertainty in the structural design process has two main problems. First, the design may not satisfy the safety requirements if the actual uncertainties in the noise factors are underestimated. Thus, the safety requirements can easily be violated because of the high variation of the system response due to noise factors. Second, to guarantee safety in the presence of this high variability of the system response, the structural designer may be forced to choose an overly conservative, inefficient and thus costly design. When the robustness against uncertainty is not treated as one of the design objectives, this trade-off between the over-design for safety and the under-design for cost-savings is exacerbated. This paper demonstrates that safe and cost-effective designs are achievable by implementing Robust Design concepts originally developed in manufacturing engineering to proactively consider the robustness against uncertainty as one of the design objectives. Robust Design concepts can be used to formulate structural designs which are insensitive to inherent variability in the design process, thus save cost, and exceed the main objectives of user safety and serviceability. This paper presents two methodologies for the application of Robust Design principles to structural design utilizing two optimization schemes: one-at-a-time optimization method and Particle Swarm Optimization (PSO) method. © The Society for Experimental Mechanics, Inc. 2012.