An innovative artificial immune system (AIS) is proposed herein for structural health monitoring (SHM) to ensure the structural integrity and functionality. While satisfactory results were obtained by previous AIS schemes, their performance is strongly structural-parameter-value (SPV) dependent and deviations of SPVs in testing from training due to modeling errors and measurement noises significantly deteriorates the AIS' performance. This thesis presents a less SPV-dependent AIS with a three-phase architecture, including damage-existence-detection, damage-location-determination, and damage-severity-estimation, using specially designed feature vectors (FVs) based on structural modal parameters. The maximum-relative-modal-parameter-change is used to detect the damage's existence and estimate its severity, and the pattern in normalized-modal-parameter-change is used to determinate the damage's location. Comparisons between the proposed FVs and their existing counterparts were conducted for 2/3/4-degree-of-freedom structures to illustrate the superior performance and less SPV-dependence of the proposed method, particularly in determining damage location. The proposed AIS was tested on a 4-degree-of-freedom model using 440 randomly generated damage conditions with a different SPV set per condition. A success rate of 95.23% in the determination of damage's existence and its location was obtained. The trained AIS for the 4-degree-of-freedom model was further evaluated by a four-story and two-bay by two-bay prototype structure used in the benchmark problem proposed by the IASC-ASCE Structural Health Monitoring Task Group. Results have shown great potentials of the proposed approach in its real-world applications.

This thesis investigates through simulation why tests of unit root and stationarity occasionally result in different conclusions. The thesis focusses on the KPSS test and the ADF test and both review cases with and without a trend. The goal is to bring additional knowledge of whether one of the tests are more reliable in terms of size and power and when contradictory results occur. The result shows that both KPSS and ADF suffer from low power and size distortion and that the problems persists for the most common time series lengths. Problems particularly arise when the time series contains a trend or is a process with both an autoregressive and a moving average part. There is no clear evidence that one of the tests are superior to the other, it rather depends on sample size, parameter value and type of ARIMA process.

Models are important tools for understanding the hydrological processes that govern water transport in the landscape and for prediction at times and places where no observations are available. The degree of trust placed on models, however, should not exceed the quality of the data they are fed with. The overall aim of this thesis was to tune the modeling process to account for the uncertainty in the data, by identifying robust parameter values using methods from computational geometry. The methods were developed and tested on data from the Choluteca River basin in Honduras. Quality control of precipitation and discharge data resulted in a rejection of 22% percent of daily raingage data and the complete removal of one out of the seven discharge stations analyzed. The raingage network was not found sufficient to capture the spatial and temporal variability of precipitation in the Choluteca River basin. The temporal variability of discharge was evaluated through a Monte Carlo assessment of the rating-equation parameter values over a moving time window of stage-discharge measurements. Al hydrometric stations showed considerable temporal variability in the stage-discharge relationship, which was largest for low flows, albeit with no common trend. The problem with limited data quality was addressed by identifying robust model parameter values within the set of well-performing (behavioral) parameter-value vectors with computational-geometry methods. The hypothesis that geometrically deep parameter-value vectors within the behavioral set were hydrologically robust was tested, and verified, using two depth functions. Deep parameter-value vectors tended to perform better than shallow ones, were less sensitive to small changes in their values, and were better suited to temporal transfer. Depth functions rank multidimensional data. Methods to visualize the multivariate distribution of behavioral parameters based on the ranked values were developed. It was shown that, by projecting along a common dimension, the multivariate distribution of behavioral parameters for models of varying complexity could be compared using the proposed visualization tools. This has a potential to aid in the selection of an adequate model structure considering the uncertainty in the data. These methods allowed to quantify observational uncertainties. Geometric methods have only recently begun to be used in hydrology. It was shown that they can be used to identify robust parameter values, and some of their potential uses were highlighted. / Modeller är viktiga verktyg för att förstå de hydrologiska processer som bestämmer vattnets transport i landskapet och för prognoser för tider och platser där det saknas mätdata. Graden av tillit till modeller bör emellertid inte överstiga kvaliteten på de data som de matas med. Det övergripande syftet med denna avhandling var att anpassa modelleringsprocessen så att den tar hänsyn till osäkerheten i data och identifierar robusta parametervärden med hjälp av metoder från beräkningsgeometrin. Metoderna var utvecklade och testades på data från Cholutecaflodens avrinningsområde i Honduras. Kvalitetskontrollen i nederbörds- och vattenföringsdata resulterade i att 22 % av de dagliga nederbördsobservationerna måste kasseras liksom alla data från en av sju analyserade vattenföringsstationer. Observationsnätet för nederbörd befanns otillräckligt för att fånga upp den rumsliga och tidsmässiga variabiliteten i den övre delen av Cholutecaflodens avrinningsområde. Vattenföringens tidsvariation utvärderades med en Monte Carlo-skattning av värdet på parametrarna i avbördningskurvan i ett rörligt tidsfönster av vattenföringsmätningar. Alla vattenföringsstationer uppvisade stor tidsvariation i avbördningskurvan som var störst för låga flöden, dock inte med någon gemensam trend. Problemet med den måttliga datakvaliteten bedömdes med hjälp av robusta modellparametervärden som identifierades med hjälp av beräkningsgeometriska metoder. Hypotesen att djupa parametervärdesuppsättningar var robusta testades och verifierades genom två djupfunktioner. Geometriskt djupa parametervärdesuppsättningar verkade ge bättre hydrologiska resultat än ytliga, var mindre känsliga för små ändringar i parametervärden och var bättre lämpade för förflyttning i tiden. Metoder utvecklades för att visualisera multivariata fördelningar av välpresterande parametrar baserade på de rangordnade värdena. Genom att projicera längs en gemensam dimension, kunde multivariata fördelningar av välpresterande parametrar hos modeller med varierande komplexitet jämföras med hjälp av det föreslagna visualiseringsverktyget. Det har alltså potentialen att bistå vid valet av en adekvat modellstruktur som tar hänsyn till osäkerheten i data. Dessa metoder möjliggjorde kvantifiering av observationsosäkerheter. Geometriska metoder har helt nyligen börjat användas inom hydrologin. I studien demonstrerades att de kan användas för att identifiera robusta parametervärdesuppsättningar och några av metodernas potentiella användningsområden belystes.