Substructuring approache in state space models for dynamic system parameters identification

In the recent decade, the request for structural health monitoring expertise increased exponentially in the United States. The aging issues that most of the transportation structures are experiencing can put in serious jeopardy the economic system of a region as well as of a country. At the same time, the monitoring of structures is a central topic of discussion in Europe, where the preservation of historical buildings has been addressed over the last four centuries. More recently, various concerns arose about security performance of civil structures after tragic events such the 9/11 or the 2011 Japan earthquake: engineers looks for a design able to resist exceptional loadings due to earthquakes, hurricanes and terrorist attacks. After events of such a kind, the assessment of the remaining life of the structure is at least as important as the initial performance design. Consequently, it appears very clear that the introduction of reliable and accessible damage assessment techniques is crucial for the localization of issues and for a correct and immediate rehabilitation. The System Identification is a branch of the more general Control Theory. In Civil Engineering, this field addresses the techniques needed to find mechanical characteristics as the stiffness or the mass starting from the signals captured by sensors. The objective of the Dynamic Structural Identification (DSI) is to define, starting from experimental measurements, the modal fundamental parameters of a generic structure in order to characterize, via a mathematical model, the dynamic behavior. The knowledge of these parameters is helpful in the Model Updating procedure, that permits to define corrected theoretical models through experimental validation. The main aim of this technique is to minimize the differences between the theoretical model results and in situ measurements of dynamic data. Therefore, the new model becomes a very effective control practice when it comes to rehabilitation of structures or damage assessment. The instrumentation of a whole structure is an unfeasible procedure sometimes because of the high cost involved or, sometimes, because it’s not possible to physically reach each point of the structure. Therefore, numerous scholars have been trying to address this problem. In general two are the main involved methods. Since the limited number of sensors, in a first case, it’s possible to gather time histories only for some locations, then to move the instruments to another location and replay the procedure. Otherwise, if the number of sensors is enough and the structure does not present a complicate geometry, it’s usually sufficient to detect only the principal first modes. This two problems are well presented in the works of Balsamo [1] for the application to a simple system and Jun [2] for the analysis of system with a limited number of sensors. Once the system identification has been carried, it is possible to access the actual system characteristics. A frequent practice is to create an updated FEM model and assess whether the structure fulfills or not the requested functions. Once again the objective of this work is to present a general methodology to analyze big structure using a limited number of instrumentation and at the same time, obtaining the most information about an identified structure without recalling methodologies of difficult interpretation. A general framework of the state space identification procedure via OKID/ERA algorithm is developed and implemented in Matlab. Then, some simple examples are proposed to highlight the principal characteristics and advantage of this methodology. A new algebraic manipulation for a prolific use of substructuring results is developed and implemented.

Spatial and temporal variation in settlement, growth and condition of the rockfish species Sebastes caurinus and Sebastes carnatus in the Channel Islands region, Santa Barbara, CA

Survival during the early life stages of marine species, including nearshore temperate reef fishes, is typically very low, and small changes in mortality rates, due to physiological and environmental conditions, can have marked effects on survival of a cohort and, on a larger scale, on the success of a recruitment season. Moreover, trade offs between larval growth and accumulation of energetic resources prior to settlement are likely to influence growth and survival until this critical period and afterwards. Rockfish recruitment rates are notoriously variable between years and across geographic locations. Monitoring of rates of onshore delivery of pelagic juveniles (defined here as settlement) of two species of nearshore rockfishes, Sebastes caurinus and Sebastes carnatus, was done between 2003-2009 years using artificial collectors placed at San Miguel and Santa Cruz Island, off Southern California coast. I investigated spatiotemporal variation in settlement rate, lipid content, pelagic larval duration and larval growth of the newly settled fishes; I assessed relationships between birth date, larval growth, early life-history characteristics and lipid content at settlement, considering also interspecific differences; finally, I attempt to relate interannual patterns of settlement and of early life history traits to easily accessible, local and regional indices of ocean conditions including in situ ocean temperature and regional upwelling, sea surface temperature (SST) and Chlorophyll-a (Chl-a) concentration. Spatial variations appeared to be of low relevance, while significant interannual differences were detected in settlement rate, pelagic larval duration and larval growth. The amount of lipid content of the newly settled fishes was highly variable in space and time, but did not differ between the two species and did not show any relationships with early life history traits, indicating that no trade off involved these physiological processes or they were masked by high individual variability in different periods of larval life. Significant interspecific differences were found in the timing of parturition and settlement and in larval growth rates, with S. carnatus growing faster and breeding and settling later than S. caurinus. The two species exhibited also different patterns of correlations between larval growth rates and larval duration. S. carnatus larval duration was longer when the growth in the first two weeks post-hatch was faster, while S. caurinus had a shorter larval duration when grew fast in the middle and in the end of larval life, suggesting different larval strategies. Fishes with longer larval durations were longer in size at settlement and exhibited longer planktonic phase in periods of favourable environmental conditions. Ocean conditions had a low explanatory power for interannual variation in early life history traits, but a very high explanatory power for settlement fluctuations, with regional upwelling strength being the principal indicator. Nonetheless, interannual variability in larval duration and growth were related to great phenological changes in upwelling happened during the period of this study and that caused negative consequences at all trophic levels along the California coast. Despite the low explanatory power of the environmental variables used in this study on the variation of larval biological traits, environmental processes were differently related with early life history characteristics analyzed to species, indicating possible species-specific susceptibility to ocean conditions and local environmental adaptation, which should be further investigated. These results have implications for understanding the processes influencing larval and juvenile survival, and consequently recruitment variability, which may be dependent on biological characteristics and environmental conditions.