The emergence of new kinds of micro-technologies, e.g., MEMS (microelectromechanical systems) devices, biomedical microma- chines, remote sensors, etc., has given rise to new approaches in battery development. The additional thesis contributes part of Finite Element Analysis of microbatteries, to be specific, the trenched mirobatteries architecture. The project aims to perform a parametric study of micro-battery, including a python script implemented and an example of asymmetric trench model simulation. The python executable allows to run the parametric study in an automatic way. The asymmetric working example provides a generic trench model template.

The python executable consists of six functional blocks, namely: reading the input data from users, generating meshes with multiple parameters, splitting the interface nodes, refining the mesh files, running the simulations, and collecting the generic output parameter. Generating meshes allows users to create 2D triangle mesh with two parameters for a given geometry. Splitting the interface nodes uses a generic manner to split interfaces for an arbitrary structure. Once splitting interface is done, the executable is able to seek the new nodes on the interface and compile them in a prescribed fashion before simulation. Coming into the last part, the executable makes use of a set of external programs to run Jem-Jive carrying out the relative outcome. A simple case will go through every step how this script works, after which a more complicated model will be illustrated.

Generally, though taking the advantage of symmetry of trench makes the parametric study easier and straightforward to handle with, which only the half of trench is considered, an asymmetric trench model is more probable and realistic. An example is provided afterwards with five pillars which not only their length may vary, but imperfection is introduced here: pillars can be slightly inclined as well until they touch each other. An important property worth noticing is the top and bottom of the trench stay smoothie although inclination occurs and the neutral layer of pillar still remains the same length. The example gives a detailed universal geometric derivation and formulas with respect to heights and inclined angles, which are regarded as two relative variables implemented by the python executable mentioned above.

The rapid development in statistics, information technology, and computational power have en- abled numerous innovative methods to emerge in Structural Health Monitoring (SHM) for structural damage detection, both in practical application and research. The intention of such methods is to use the data obtained from the monitoring system to extract sufficient information to identify damage types that may appear in the structure. However, the following type of questions are mostly unanswered for realistic structural types and monitoring systems: Which responses of the structure should be monitored? Which sensor locations and sensor combinations carry the most information? Among the currently available computational algorithms, which one is the most suit- able one in this context? Hence, this study aims to contribute on this front and provide answers for practical applicability. A comprehensive study of a realistic, prestressed concrete bridge built by the cantilever method - the Lezíria Bridge in Portugal is undertaken to provide insight into these questions.
The engineering challenge is studied in a probabilistic framework where the uncertainty sources are model uncertainty and measurement uncertainty. The Bayesian paradigm is used to handle the uncertainty component and a validated Finite Element (FE) model is applied to capture the mechanical behavior. The influence of the potential damage scenario, severity of damage, sensor type, the combination of sensors, prior knowledge of the structure, and the extent of uncertainty of the FE model and measurements are analyzed in this thesis. The informativeness of the Damage Identification (DI) process is reflected by the information content of its resulting posterior distributions. The information content is quantified using measures based on information entropy and the area of credible regions.
It is demonstrated that selecting different responses to monitor may lead to a significant change in the informativeness of the result. It is also shown that for all analyzed cases using the most informative sensor type provides adequate information that reflects the damage state, while the rest types could only complement very limited information. In addition, the hybrid Markov Chain Monte Carlo(MCMC) seems more efficient and effective to conduct Bayesian inference. The findings provide valuable, quantitative insight into the design of new monitoring systems.