This dissertation presents a novel procedure to select explanatory variables, without the influence of human bias, for deterioration model development using National Bridge Inventory (NBI) data. Using NBI information, including geometric data and climate information, candidate explanatory variables can be converted into normalized numeric values and analyzed prior to the development of deterministic or stochastic deterioration models. The prevailing approach for explanatory variable selection is to use expert opinion solicited from experienced engineers. This may introduce human influenced biases into the deterioration modeling process. A framework using Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression and covariance analysis are combined to compensate for this potential bias. Additionally, the cross validation analysis and solution path is used as a standard for the selection of minimum number of explanatory variables. The proposed method is demonstrated through the creation of deterministic deterioration models for deck, superstructure, and substructure for Wyoming bridges and compared to explanatory variables using the expert selection method. The comparison shows a significant decrease in error using the presented framework based on the L2 relative error norm. The final chapter presents a new method to develop stochastic deterioration models using logistic regression. The relative importance amongst explanatory variables is used to develop a classification tree for Wyoming bridges. The bridges in a subset are commonly associated with several explanatory variables, so that the deterioration models can be more representative and accurate than using a single explanatory variable. The logistic regression is used to introduce the stochastic contribution into the deterioration models. In order to avoid missing data problems, the binary categories condition rating, either remaining the same or decreased, are considered for logistic regression. The probability of changes in bridges’ condition rating is obtained and the averages for same condition ratings are used to create transition probability matrix for each age group. The deterioration model based on Markov chain are developed for Wyoming bridges and compared with the previous model based on percentage prediction and optimization approach. The prediction error is analyzed, which demonstrates the considerable performance of the proposed method and is suitable for relatively small data samples.

Structural Health Monitoring (SHM) with emerging technologies like e.g. fibre optic sensors, lasers, radars, acoustic emission and Micro Electro Mechanical Systems (MEMS) made an entrance into the civil engineering field in last decades. Expansion of new technologies together with development in data communication benefited for rapid development. The author has been doing research as well as working with SHM and related tasks nearly a decade. Both theoretical knowledge and practical experience are gained in this constantly developing field. This doctoral thesis presents lessons learned in SHM and sensory technologies when monitoring civil engineering structures, mostly bridges. Nevertheless, these techniques can also be used in most applications related to civil engineering like dams, high rise buildings, off-shore platforms, pipelines, harbour structures and historical monuments. Emerging and established technologies are presented, discussed and examples are given based on the experience achieved. A special care is given to Fibre Optic Sensor (FOS) technology and its latest approach. Results from crack detection testing, long-term monitoring, and sensor comparison and installation procedure are highlighted. The important subjects around sensory technology and SHM are discussed based on the author's experience and recommendations are given. Applied research with empirical and experimental methods was carried out. A state-of-the art-review of SHM started the process but extensive literature studies were done continuously along the years in order to keep the knowledge up to date. Several SHM cases, both small and large scale, were carried out including sensor selection, installation planning, physical installation, data acquisition set-up, testing, monitoring, documentation and reporting. One case study also included modification and improvement of designed system and physical repair of sensors as well as two Site Acceptance Tests (SATs) and the novel crack detection system testing. Temporary measuring and testing also took place and numerous Structural Health Monitoring Systems (SHMSs) were designed for new bridges. The observed and measured data/phenomena were documented and analysed.  Engineers, researchers and owners of structures are given an essential implement in managing and maintaining structures. Long-term effects like shrinkage and creep in pre-stressed segmental build bridges were studied. Many studies show that existing model codes are not so good to predict these long-term effects. The results gained from the research study with New Årsta Railway Bridge are biased be the fact that our structure is indeed special. Anyhow, the results can be compared to other similar structures and adequately used for the maintenance planning for the case study. A long-term effect like fatigue in steel structures is a serious issue that may lead to structural collapse. Novel crack detection and localisation system, based on development on crack identification algorithm implemented in DiTeSt system and SMARTape delamination mechanism, was developed, tested and implemented. Additionally, new methods and procedures in installing, testing, modifying and improving the installed system were developed. There are no common procedures how to present the existing FOS techniques. It is difficult for an inexperienced person to judge and compare different systems. Experience gained when working with Fibre Optic Sensors (FOS) is collected and presented. The purpose is, firstly to give advice when judging different systems and secondly, to promote for more standardised way to present technical requirements. Furthermore, there is need to regulate the vocabulary in the field. Finally, the general accumulated experience is gathered. It is essential to understand the complexity of the subject in order to make use of it. General trends and development are compared for different applications. As the area of research is wide, some chosen, specific issues are analysed on a more detailed level. Conclusions are drawn and recommendations are given, both specific and more general. SHMS for a complex structure requires numerous parameters to be measured. Combination of several techniques will enable all required measurements to be taken. In addition, experienced specialists need to work in collaboration with structural engineers in order to provide high-quality systems that complete the technical requirement. Smaller amount of sensors with proper data analysis is better than a complicated system with numerous sensors but with poor analysis. Basic education and continuous update for people working with emerging technologies are also obligatory. A lot of capital can be saved if more straightforward communication and international collaboration are established: not only the advances but also the experienced problems and malfunctions need to be highlighted and discussed in order not to be repeated. Quality assurance issues need to be optimized in order to provide high quality SHMSs. Nevertheless, our structures are aging and we can be sure that the future for sensory technologies and SHM is promising. The final conclusion is that an expert in SHM field needs wide education, understanding, experience, practical sense, curiosity and preferably investigational mind in order to solve the problems that are faced out when working with emerging technologies in the real world applications.  The human factor, to be able to bind good relationship with workmanship cannot be neglected either. There is also need to be constantly updated as the field itself is in continuous development. / QC 20111117 / SHMS of the New Årsta Railway Bridge