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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Metamodeling of Interdependent Systems: Application to Bridge Infrastructure Management</title>
      <link>https://trid.trb.org/View/1416263</link>
      <description><![CDATA[Recent advances in the area of modeling systems of systems have brought to the forefront the need to model transportation infrastructure through a new paradigm that would account for the diversity of involved stakeholders, objectives, and interdependencies. This article focuses on a bridge system of systems, and it presents a metamodeling approach in which shared state variables are initially used to link models that represent various subsystems, and subsequently used to provide a tool for coordinating decisions made at the level of the individual subsystems with the goal of obtaining bridge management strategies that are not only optimal from the perspective of a single subsystem, but are more sustainable from a global system of systems perspective.]]></description>
      <pubDate>Thu, 28 Jul 2016 10:45:46 GMT</pubDate>
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      <title>Highly Efficient Selective Assembly Method of Horizontal Stabilizer based on Metamodeling and Particle Swarm Optimization</title>
      <link>https://trid.trb.org/View/1317264</link>
      <description><![CDATA[Selective assembly is a cost-effective approach for reducing the overall variation and thus improving the quality of an assembled product. Traditional selective assembly requires quite a large number of finite-element analysis (FEA) runs and consequently is rather time consuming when considering every part’s deviations. The paper presents the highly efficient selective assembly method that assists operators in horizontal stabilizer assembling while considering the parts’ fabrication deviations. Metamodeling is employed to build the input–output functions from FEA results. The hybrid metamodel consisted of four metamodels and is constructed to express the mapping relationship between fabrication deviations and assembly distortions. Cross validation of metamodels is conducted using 27 FEA simulations. The quantitative selective assembly suggestion, that is, the selective assembly table, is obtained using the hybrid metamodels and particle swarm optimization (PSO) algorithm. The optimal assembly of the trailing edge is considered to illustrate the computational efficiency of the method. The results have shown that the proposed method preserve final assembly precision with high efficiency. The selective assembly procedures outlined in this paper will enhance the understanding of the compliant components deformation in assembly and help systematically improving the precision control efficiency in the civil aircraft industry.]]></description>
      <pubDate>Wed, 27 Aug 2014 10:54:51 GMT</pubDate>
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      <title>Engine block soundproofing: on meta–models of poroelastic materials for acoustic absorption and head injury criterion</title>
      <link>https://trid.trb.org/View/1246079</link>
      <description><![CDATA[The purpose of this work is the investigation of meta–models quantifying shock and acoustic indicators. The vehicle design of the engine block insulating poroelastic materials is considered. An analytical tool is proposed. It facilitates decision–making. Modelling tools (Design of Experiments techniques, kriging models,) are used to build empirical models expressing the Head Injury Criterion (HIC) and the acoustic absorption coefficient of poroelastic materials. Models defined here allow HIC and acoustic absorption predictions to be made for any porous material whose intrinsic characteristics are included in the design space. Such models offer effective alternative to finite–element formulations, with considerable time saving.]]></description>
      <pubDate>Mon, 15 Apr 2013 13:15:53 GMT</pubDate>
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      <title>Combining Optimizer and Metamodelling for Railcar Structural Optimization</title>
      <link>https://trid.trb.org/View/758474</link>
      <description><![CDATA[This article proposes a technique combining a sizing optimizer with metamodelling for railcar structural optimization.  The authors describe a two-level or hierarchical, interactive, and meta-model-based optimization (HIMO) approach.  At the lower level, a sizing optimizer finds feasible and optimal solutions in terms of sizing variables (plate thickness in continuum structures).  Performance constraints such as stress, displacement, and stability, are handled only at this level. At the upper level, a metamodel is built to fit all the optimal solutions found at the lower level and is optimized for topology design.  This results in the number of topology design variables being much smaller than those used in many other topology optimization approaches. The authors describe how this HIMO approach was applied to two boxcar design projects, resulting in 18 per cent and 36 per cent weight savings and significant reductions in manufacturing cost and total cost.  The authors conclude that both the pilot tests and the real applications demonstrate that the HIMO approach works for real layout optimization.]]></description>
      <pubDate>Thu, 28 Jul 2005 07:49:57 GMT</pubDate>
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      <title>Water Distribution System Optimization Using Metamodels</title>
      <link>https://trid.trb.org/View/756167</link>
      <description><![CDATA[Genetic algorithms (GAs) have been shown to apply well to optimizing the design and operations of water distribution systems (WDSs). The objective has usually been to minimize cost, subject to hydraulic constraints such as satisfying minimum pressure. More recently, the focus of optimization has expanded to include water quality concerns. This added complexity significantly increases the computational requirements of optimization. Considerable savings in computer time can be achieved by using a technique known as metamodeling. A metamodel is a surrogate or substitute for a complex simulation model. This research uses a metamodeling approach to optimize a water distribution design problem that includes water quality. The type of metamodels used are artificial neural networks (ANNs), as they are capable of approximating the nonlinear functions that govern flow and chlorine decay in a WDS. The ANNs were calibrated to provide a good approximation to the simulation model. In addition, two techniques are presented to improve the ability of metamodels to find the same optimal solution as the simulation model. Large savings in computer time occurred from training the ANNs to approximate chlorine concentrations (approximately 700 times faster than the simulation model) while still finding the optimal solution.]]></description>
      <pubDate>Fri, 27 May 2005 08:38:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/756167</guid>
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