Robust Design Optimization for Low-Cost Concrete Box-Girder Bridge

Abstract

The design of a structure is generally carried out according to a deterministic approach. However, all structural problems have associated initial uncertain parameters that can differ from the design value. This becomes important when the goal is to reach optimized structures, as a small variation of these initial uncertain parameters can have a big influence on the structural behavior. The objective of robust design optimization is to obtain an optimum design with the lowest possible variation of the objective functions. For this purpose, a probabilistic optimization is necessary to obtain the statistical parameters that represent the mean value and variation of the objective function considered. However, one of the disadvantages of the optimal robust design is its high computational cost. In this paper, robust design optimization is applied to design a continuous prestressed concrete box-girder pedestrian bridge that is optimum in terms of its cost and robust in terms of structural stability. Furthermore, Latin hypercube sampling and the kriging metamodel are used to deal with the high computational cost. Results show that the main variables that control the structural behavior are the depth of the cross-section and compressive strength of the concrete and that a compromise solution between the optimal cost and the robustness of the design can be reached.

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

“…Then using the function P tr , a probability is assigned to each group obtained from the clustering process. In this article, we use the linear function given in Equation (11). Where Clust (x i ) indicates the location of the group to which ∆ i (x) belongs.…”

“…From the research point of view, these problems present interesting challenges in the areas of operations research, computational complexity, and algorithm theory. Examples of combinatorial problems are found in, scheduling problems [1,2], transport [2], machine learning [3], facility layout design [4], logistics [5], allocation resources [6,7], routing problems [8,9], robotics applications [10], civil engineering problem [11][12][13], engineering design problem [14], fault diagnosis of machinery [15], and social sustainability of infrastructure projects [16], among others. Combinatorial optimization algorithms should explore the solutions space to find optimal solutions.…”

Probability-Based Wildfire Risk Measure for Decision-Making

“…Each set F 1 included in F M 1 (Figure 2a) and containing F m 1 (Figure 2b) is solution of (14). Therefore, there are multiple solution for F 1 , specifically there are 2 6 = 64 different set satisfying inequality (20).…”

“…[13] take additional considerations into account for preserving habitat quality. Finally, to obtain robust models against uncertainty, as can be seen for instance in [14] for other fields, stochasticity is included in the model. The complete study can be found in [15].…”

Life Cycle Sustainability Assessment for Multi-Criteria Decision Making in Bridge Design: A Review

“…Therefore, research has been conducted on the assessment of the impacts resulting from the construction and maintenance of bridges in each of the three dimensions of sustainability. Efforts have been made to reduce the economic impacts of bridges by optimising the costs associated both to construction (García-Segura et al, 2014;Penadés-Plà et al, 2020a) and to maintenance (Sabatino et al, 2016;Navarro et al, 2018a). The environmental dimension of sustainability has also been assessed in studies focused on finding the most favourable design geometry (García-Segura et al, 2017a;Yepes et al, 2015) and maintenance strategy (Navarro et al, 2018b(Navarro et al, , 2019a.…”