• Interaction and multiscale mechanics
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• v.1 no.1
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• pp.103-121
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• 2008

This paper presents an optimization-based method for computing a minimal bounding ellipsoid that contains the set of static responses of an uncertain braced frame. Based on a non-stochastic modeling of uncertainty, we assume that the parameters both of brace stiffnesses and external forces are uncertain but bounded. A brace member represents the sum of the stiffness of the actual brace and the contributions of some non-structural elements, and hence we assume that the axial stiffness of each brace is uncertain. By using the $\mathcal{S}$-lemma, we formulate a semidefinite programming (SDP) problem which provides an outer approximation of the minimal bounding ellipsoid. The minimum bounding ellipsoids are computed for a braced frame under several uncertain circumstances.

• Wind and Structures
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• v.15 no.3
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• pp.209-221
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• 2012

The flutter reliability analysis of long span bridges requires use of a software tool that predicts the uncertainty in a flutter response due to uncertainties in the model formulation and input parameters. Existing flutter analysis numerical codes are not capable of dealing with stochastic uncertainty in the analysis of long span bridges. The goal of the present work is to develop a software tool (FREASB) to enable designers to efficiently and accurately conduct flutter reliability analysis of long span bridges. The FREASB interfaces an open-source Matlab toolbox for structural reliability analysis (FERUM) with a typical deterministic flutter analysis code. The paper presents a brief introduction to the generalized first-order reliability method implemented in FREASB and key steps involved in coupling it with a typical deterministic flutter analysis code. A numerical example concerning flutter reliability analysis of a long span suspension bridge with a main span of 1385 m is presented to demonstrate the application and effectiveness of the methodology and the software.

• Ocean Systems Engineering
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• v.2 no.1
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• pp.1-16
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• 2012

Offshore wind turbines are complex structural and mechanical systems located in a highly demanding environment. This paper proposes a multi-level system approach for studying the structural behavior of the support structure of an offshore wind turbine. In accordance with this approach, a proper numerical modeling requires the adoption of a suitable technique in order to organize the qualitative and quantitative assessment in various sub-problems, which can be solved by means of sub-models at different levels of detail, both for the structural behavior and for the simulation of loads. Consequently, in a first place, the effects on the structural response induced by the uncertainty of the parameters used to describe the environmental actions and the finite element model of the structure are inquired. After that, a meso-level FEM model of the blade is adopted in order to obtain the detailed load stress on the blade/hub connection.

• Structural Engineering and Mechanics
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• v.26 no.4
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• pp.377-392
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• 2007

A discrete sliding mode control (SMC) method based on hybrid model of neural network and nominal model is proposed to reduce the vibration of flexible structures, which is a robust active controller developed by using a sliding manifold approach. Since the thick boundary layer will reduce the virtue of SMC, the multilayer feed-forward neural network is adopted to model the uncertainty part. The neural network is trained by Levenberg-Marquardt backpropagation. The design objective of the sliding mode surface is based on the quadratic optimal cost function. In course of running, the input signal of SMC come from the hybrid model of the nominal model and the neural network. The simulation shows that the proposed control scheme is very effective for large uncertainty systems.

• Earthquakes and Structures
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• v.12 no.1
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• pp.47-54
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• 2017

Introduction of robustness index in the structure is done in three ways: deterministic robustness index, probabilistic robustness index, and risk-based robustness index. In past decades, there have been numerous researches to evaluate robustness index in both deterministic and probabilistic ways. In this research, by using a risk analysis, a risk-based robustness index has been defined for the structure. By creating scenarios in accordance with uncertainty parameters of critical and unexpected gas blast accident, a new method has been suggested for evaluating risk-based robustness index. Finally, a numerical example for the evaluation of risk-based robustness index of a four-storey reinforced concrete moment frame, designed and built based on Eurocode 8 code, has been presented with results showing a lower risk of robustness.

• Steel and Composite Structures
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• v.7 no.3
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• pp.253-262
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• 2007

A methodology to design symmetrically laminated fibre-reinforced structures under transverse loads for minimum weight, with manufacturing uncertainty in the ply angle, is described. The ply angle and the ply thickness are the design variables, and the Tsai-Wu failure criteria is the design constraint implemented. It is assumed that the probability of any tolerance value occurring within the tolerance band, compared with any other, is equal, and thus the approach is a worst-case scenario approach. The finite element method, based on Mindlin plate and shell theory, is implemented, and thus effects like bending-twisting coupling are accounted for. The Golden Section method is used as the search algorithm, but the methodology is flexible enough to allow any appropriate finite element formulation, search algorithm and failure criterion to be substituted. In order to demonstrate the procedure, laminated plates with varying aspect ratios and boundary conditions are optimally designed and compared.

• Geomechanics and Engineering
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• v.2 no.1
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• pp.57-69
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• 2010

A major consideration in the design of tunnels in urban areas is the prediction of the ground movements and surface settlements associated with the tunneling operations. Excessive ground movements can damage adjacent building and utilities. In this paper, a neural network model is used to predict the maximum surface settlement, based on instrumented results from three separate EPB tunneling projects in Singapore. This paper demonstrates that by coupling the trained neural network model to a spreadsheet optimization technique, the reliability assessment of the settlement serviceability limit state can be carried out using the first-order reliability method. With this method, it is possible to carry out sensitivity studies to examine the effect of the level of uncertainty of each parameter uncertainty on the probability that the serviceability limit state has been exceeded.

• Computers and Concrete
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• v.29 no.1
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• pp.59-67
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• 2022

In this paper, a reliability-based approach has been implemented to develop seismic analytical fragility curves of highway bridges. A typical bridge class of the Central and South-eastern United States (CSUS) region was selected. Detailed finite element modelling is presented and Incremental Dynamic Analysis (IDA) is used to capture the behavior of the bridge from linear to nonlinear behavior. Bayesian linear regression method is used to define the demand model. A reliability approach is implemented to generate the analytical fragility curves and the proposed approach is compared with the conventional fragility analysis procedure.

• Korean Management Science Review
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• v.19 no.2
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• pp.91-124
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• 2002

The use of network planning models and tools is essential for effective KII (Korea Information Infrastructure) planning and analysis in that it will significantly reduce the risk and uncertainty embeded in the development and the provision of future broadband services. The purpose of this study is to develop a theoretical framework and a computer tool for modeling the various aspects of the KII topology and architecture and evaluating the techno-economic feasibility of the KII implementation strategy.

• Structural Engineering and Mechanics
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• v.6 no.8
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• pp.955-969
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• 1998

Engineering problems are inherently imprecision tolerant. Biologically inspired soft computing methods are emerging as ideal tools for constructing intelligent engineering systems which employ approximate reasoning and exhibit imprecision tolerance. They also offer built-in mechanisms for dealing with uncertainty. The fundamental issues associated with engineering applications of the emerging soft computing methods are discussed, with emphasis on neural networks. A formalism for neural network representation is presented and recent developments on adaptive modeling of neural networks, specifically nested adaptive neural networks for constitutive modeling are discussed.