Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering Tshilidzi Marwala and Ilyes Boulkaibet, University of Johannesburg, South Africa Sondipon Adhikari, Swansea University, UK Covers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineering Finite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering. The book covers probabilistic finite element model updating, achieved using Bayesian statistics. The Bayesian framework is employed to estimate the probabilistic finite element models which take into account of the uncertainties in the measurements and the modelling procedure. The Bayesian formulation achieves this by formulating the finite element model as the posterior distribution of the model given the measured data within the context of computational statistics and applies these in aeronautical and mechanical engineering. Probabilistic Finite Element Model Updating Using Bayesian Statistics contains simple explanations of computational statistical techniques such as Metropolis-Hastings Algorithm, Slice sampling, Markov Chain Monte Carlo method, hybrid Monte Carlo as well as Shadow Hybrid Monte Carlo and their relevance in engineering. Key features: Contains several contributions in the area of model updating using Bayesian techniques which are useful for graduate students. Explains in detail the use of Bayesian techniques to quantify uncertainties in mechanical structures as well as the use of Markov Chain Monte Carlo techniques to evaluate the Bayesian formulations. The book is essential reading for researchers, practitioners and students in mechanical and aerospace engineering.

Résumé : Essential reading for researchers, practitioners and students in mechanical and aerospace engineering, this book covers probabilistic finite element model updating, achieved using Bayesian statistics. --

FEM updating allows FEMs to be tuned better to reflect measured data. It can be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. This book applies both strategies to the field of structural mechanics, using vibration data. Computational intelligence techniques including: multi-layer perceptron neural networks; particle swarm and GA-based optimization methods; simulated annealing; response surface methods; and expectation maximization algorithms, are proposed to facilitate the updating process. Based on these methods, the most appropriate updated FEM is selected, a problem that traditional FEM updating has not addressed. This is found to incorporate engineering judgment into finite elements through the formulations of prior distributions. Case studies, demonstrating the principles test the viability of the approaches, and. by critically analysing the state of the art in FEM updating, this book identifies new research directions.

Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 40th IMAC, A Conference and Exposition on Structural Dynamics, 2022, the third volume of nine from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on: Uncertainty Quantification and Propagation in Structural Dynamics Bayesian Analysis for Real-Time Monitoring and Maintenance Uncertainty in Early Stage Design Quantification of Model-Form Uncertainties Fusion of Test and Analysis MVUQ in Action

This book combines a model reduction technique with an efficient parametrization scheme for the purpose of solving a class of complex and computationally expensive simulation-based problems involving finite element models. These problems, which have a wide range of important applications in several engineering fields, include reliability analysis, structural dynamic simulation, sensitivity analysis, reliability-based design optimization, Bayesian model validation, uncertainty quantification and propagation, etc. The solution of this type of problems requires a large number of dynamic re-analyses. To cope with this difficulty, a model reduction technique known as substructure coupling for dynamic analysis is considered. While the use of reduced order models alleviates part of the computational effort, their repetitive generation during the simulation processes can be computational expensive due to the substantial computational overhead that arises at the substructure level. In this regard, an efficient finite element model parametrization scheme is considered. When the division of the structural model is guided by such a parametrization scheme, the generation of a small number of reduced order models is sufficient to run the large number of dynamic re-analyses. Thus, a drastic reduction in computational effort is achieved without compromising the accuracy of the results. The capabilities of the developed procedures are demonstrated in a number of simulation-based problems involving uncertainty.

This is a follow up to Health Assessment of Engineered Structures. It incorporates the most recent developments in health assessment and monitoring of infrastructures covering several advanced conceptual frameworks, different types of sensors, and application potentials. Opportunities and challenges in theoretical, numerical, and experimental investigations generally overlooked in the profession are discussed. Also included are various types of Bayesian filtering concepts improving the commonly used techniques.Showcasing a multi-faceted, technology-based development in health assessment of infrastructures, several new approaches for health assessment are presented to assess the health of masonry structures, riveted steel railway bridges, and more, such as the use of:

Eco-efficient Repair and Rehabilitation of Concrete Infrastructures, Second Edition provides an updated state-of-the-art review on the latest advances in this important research field. The first section is brought fully up-to-date and focuses on deterioration assessment methods. Section two contains brand new chapters on innovative concrete repair and rehabilitation materials including: fly ash-based alkali-activated repair materials for concrete exposed to aggressive environments; repairing concrete structures with alkali-activated metakaolin mortars; concrete with micro encapsulated self-healing materials; concrete repaired with bacteria; concrete structures repaired with engineered cementitious composites; concrete repaired by electrodeposition; the assessment of concrete after repair operations and durability of concrete repair. The final section has also been amended to include six new chapters on design, Life-cycle cost analysis and life-cycle assessment. These chapters include maintenance strategies for concrete structures; a comparison of different repair methods; life cycle assessment of the effects of climate change on bridge deterioration; life-cycle-cost benefits of cathodic protection of concrete structures; life-cycle cost analyses for concrete bridges exposed to chlorides and life-cycle analysis of repair of concrete pavements. The book will be an essential reference resource for materials scientists, civil and structural engineers, architects, structural designers and contractors working in the construction industry. - Presents the latest research findings on eco-efficient repair and rehabilitation of concrete infrastructures - Provides comprehensive coverage from damage detection and assessment, to repair strategies, and structural health monitoring - Diverse author base offering insights on construction practice and employed technologies worldwide - Includes a section on innovative repair and rehabilitation materials, as well as case studies on life cycle cost analysis and LCA