SHM strategy optimization and structural maintenance planning based on Bayesian joint modelling

In this contribution, an example is used to illustrate the application of Bayesian joint modelling in optimizing the SHM strategy and structural maintenance planning. The model parameters were evaluated first, using the Markov Chain Monte Carlo (MCMC) method. Then different parameters including expected SHM accuracy and risk acceptance criteria were investigated in order to give an insight on how the maintenance planning and life-cycle benefit are influenced. The optimal SHM strategy was then identified as the one that maximizes the benefit

Global well-posedness and scattering for nonlinear Schr\"odinger equations with combined nonlinearities in the radial case

We consider the Cauchy problem for the nonlinear Schr\"odinger equation with combined nonlinearities, one of which is defocusing mass-critical and the other is focusing energy-critical or energy-subcritical. The threshold is given by means of variational argument. We establish the profile decomposition in $H^1(\Bbb R^d)$ and then utilize the concentration-compactness method to show the global wellposedness and scattering versus blowup in $H^1(\Bbb R^d)$ below the threshold for radial data when $d\leq4$.Comment: 40 pages, 0 figure

Classes of decision analysis

The ultimate task of an engineer consists of developing a consistent decision procedure for the planning, design, construction and use and management of a project. Moreover, the utility over the entire lifetime of the project should be maximized, considering requirements with respect to safety of individuals and the environment as specified in regulations. Due to the fact that the information with respect to design parameters is usually incomplete or uncertain, decisions are made under uncertainty. In order to cope with this, Bayesian statistical decision theory can be used to incorporate objective as well as subjective information (e.g. engineering judgement). In this factsheet, the decision tree is presented and answers are given for questions on how new data can be combined with prior probabilities that have been assigned, and whether it is beneficial or not to collect more information before the final decision is made. Decision making based on prior analysis and posterior analysis is briefly explained. Pre-posterior analysis is considered in more detail and the Value of Information (VoI) is defined