Multi-scale reliability analysis of composite structures – Application to the Laroin footbridge

This work aims at developing a new methodology for the reliability assessment of composite structures and their design optimization. It relies on the coupling of well established methods: homogenization scheme for the mechanical modelling of composite materials and reliability methods to account for their inherent variability. Moreover, such approach is based on an accurate treatment of inherent uncertainties of these mechanical systems at various scales, including microscopic and macroscopic levels, that provides newperspectives for structural design. As an illustration, we propose to apply the multi-scale reliability analysis on the case of the Laroin footbridge (France) with carbon–epoxy stay cables. Since the reliability assessment of such structure is evaluated through the fibre failure, numerical simulations require the coupling of reliability methods, finite element modelling to derive macroscopic loading within cables and micromechanics to estimate the effective elastic properties of composite and local responses within constituents. Results demonstrate the feasibility of the coupled approach at a structure scale and its main interests for the optimization phase of materials and engineering structures

State-of-the-art review of automated structural design optimization

The 20th working conference of the IFIP Working Group 7.5 on Reliability and Optimization of Structural Systems (IFIP 2022) will be held at Kyoto University, Kyoto, Japan, September 19-20, 2022.Automated and intelligent structural design optimization has been a heated research topic in structural engineering community. The automated structural design may learn from existing design drawings and finite element models of previous design, infuse design experts' knowledge with design standards and codes, and notably reduce the time and difficulty of structural design compared to conventional approach. This study provides a review of current automated structural design optimization approaches, which may be categorized as the approaches based on finite element model and the deep-learning approach based on human design dataset. For the design optimization based on finite element model, the gradient-based algorithm and gradient-free algorithm are illustrated and compared. The selection of objective function and constraint functions for structural design optimization are summarized. The parallel computing method developed based on high-performance computing resources are also summarized. In addition, the deep learning approaches, which directly generate preliminary structural design drawings based son architectural drawings and datasets of human design results are also summarized. Major architectures in this research field are discussed, including generative adversarial networks (GAN), diffusion models and Variational Auto-Encoder (VAE). The combination between deep learning approach and conventional finite-element model-based approach are also discussed and the future development trends and potential challenges are discussed

Modeling and simulation of hydrokinetic composite turbine system

The utilization of kinetic energy from the river is promising as an attractive alternative to other available renewable energy resources. Hydrokinetic turbine systems are advantageous over traditional dam based hydropower systems due to zero-head and mobility. The objective of this study is to design and analyze hydrokinetic composite turbine system in operation. Fatigue study and structural optimization of composite turbine blades were conducted. System level performance of the composite hydrokinetic turbine was evaluated. A fully-coupled blade element momentum-finite element method algorithm has been developed to compute the stress response of the turbine blade subjected to hydrodynamic and buoyancy loadings during operation. Loadings on the blade were validated with commercial software simulation results. Reliability-based fatigue life of the designed composite blade was investigated. A particle swarm based structural optimization model was developed to optimize the weight and structural performance of laminated composite hydrokinetic turbine blades. The online iterative optimization process couples the three-dimensional comprehensive finite element model of the blade with real-time particle swarm optimization (PSO). The composite blade after optimization possesses much less weight and better load-carrying capability. Finally, the model developed has been extended to design and evaluate the performance of a three-blade horizontal axis hydrokinetic composite turbine system. Flow behavior around the blade and power/power efficiency of the system was characterized by simulation. Laboratory water tunnel testing was performed and simulation results were validated by experimental findings. The work performed provides a valuable procedure for the design and analysis of hydrokinetic composite turbine systems --Abstract, page iv

Reliability-based design optimization of shells with uncertain geometry using adaptive Kriging metamodels

Optimal design under uncertainty has gained much attention in the past ten years due to the ever increasing need for manufacturers to build robust systems at the lowest cost. Reliability-based design optimization (RBDO) allows the analyst to minimize some cost function while ensuring some minimal performances cast as admissible failure probabilities for a set of performance functions. In order to address real-world engineering problems in which the performance is assessed through computational models (e.g., finite element models in structural mechanics) metamodeling techniques have been developed in the past decade. This paper introduces adaptive Kriging surrogate models to solve the RBDO problem. The latter is cast in an augmented space that "sums up" the range of the design space and the aleatory uncertainty in the design parameters and the environmental conditions. The surrogate model is used (i) for evaluating robust estimates of the failure probabilities (and for enhancing the computational experimental design by adaptive sampling) in order to achieve the requested accuracy and (ii) for applying a gradient-based optimization algorithm to get optimal values of the design parameters. The approach is applied to the optimal design of ring-stiffened cylindrical shells used in submarine engineering under uncertain geometric imperfections. For this application the performance of the structure is related to buckling which is addressed here by means of a finite element solution based on the asymptotic numerical method

Stochastic System Design and Applications to Stochastically Robust Structural Control

The knowledge about a planned system in engineering design applications is never complete. Often, a probabilistic quantification of the uncertainty arising from this missing information is warranted in order to efficiently incorporate our partial knowledge about the system and its environment into their respective models. In this framework, the design objective is typically related to the expected value of a system performance measure, such as reliability or expected life-cycle cost. This system design process is called stochastic system design and the associated design optimization problem stochastic optimization. In this thesis general stochastic system design problems are discussed. Application of this design approach to the specific field of structural control is considered for developing a robust-to-uncertainties nonlinear controller synthesis methodology. Initially problems that involve relatively simple models are discussed. Analytical approximations, motivated by the simplicity of the models adopted, are discussed for evaluating the system performance and efficiently performing the stochastic optimization. Special focus is given in this setting on the design of control laws for linear structural systems with probabilistic model uncertainty, under stationary stochastic excitation. The analysis then shifts to complex systems, involving nonlinear models with high-dimensional uncertainties. To address this complexity in the model description stochastic simulation is suggested for evaluating the performance objectives. This simulation-based approach addresses adequately all important characteristics of the system but makes the associated design optimization challenging. A novel algorithm, called Stochastic Subset Optimization (SSO), is developed for efficiently exploring the sensitivity of the objective function to the design variables and iteratively identifying a subset of the original design space that has v i high plausibility of containing the optimal design variables. An efficient two-stage framework for the stochastic optimization is then discussed combining SSO with some other stochastic search algorithm. Topics related to the combination of the two different stages for overall enhanced efficiency of the optimization process are discussed. Applications to general structural design problems as well as structural control problems are finally considered. The design objectives in these problems are the reliability of the system and the life-cycle cost. For the latter case, instead of approximating the damages from future earthquakes in terms of the reliability of the structure, as typically performed in past research efforts, an accurate methodology is presented for estimating this cost; this methodology uses the nonlinear response of the structure under a given excitation to estimate the damages in a detailed, component level

Maintenance and safety of deteriorating systems: a life-cycle perspective

This paper reviews the key aspects associated with maintenance and safety of deteriorating infrastructure systems from a life-cycle perspective. The main conceptual aspects related to probabilistic optimization of maintenance and rehabilitation of structural systems are discussed. These aspects include life-cycle risk and sustainability assessment, risk-informed and utility-based decision making, and multi-objective optimization of interventions. In general, sustainability assessment is performed by quantifying economic, social, and environmental impacts associated with infrastructure management activities. This keynote paper also reviews various methods for determining optimum life-cycle maintenance, repair, and rehabilitation types and times, as well as the impact of such activities on the total life-cycle cost. The role of probabilistic performance indicators including reliability and risk, the sustainability assessment of deteriorating infrastructure systems, and risk- and utility-informed decision making are highlighted herein