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.

The Karhunen-Loève theorem

La trasformata di Karhunen-Loève monodimensionale è la decomposizione di un processo stocastico del secondo ordine a parametrizzazione continua in coefficienti aleatori scorrelati. Nella presente dissertazione, la trasformata è ottenuta per via analitica, proiettando il processo, considerato in un intervallo di tempo limitato [a,b], su una base deterministica ottenuta dalle autofunzioni dell'operatore di Hilbert-Schmidt di covarianza corrispondenti ad autovalori positivi. Fondamentalmente l'idea del metodo è, dal primo, trovare gli autovalori positivi dell'operatore integrale di Hilbert-Schmidt, che ha in Kernel la funzione di covarianza del processo. Ad ogni tempo dell'intervallo, il processo è proiettato sulla base ortonormale dello span delle autofunzioni dell'operatore di Hilbert-Schmidt che corrispondono ad autovalori positivi. Tale procedura genera coefficienti aleatori che si rivelano variabili aleatorie centrate e scorrelate. L'espansione in serie che risulta dalla trasformata è una combinazione lineare numerabile di coefficienti aleatori di proiezione ed autofunzioni convergente in media quadratica al processo, uniformemente sull'intervallo temporale. Se inoltre il processo è Gaussiano, la convergenza è quasi sicuramente sullo spazio di probabilità (O,F,P). Esistono molte altre espansioni in serie di questo tipo, tuttavia la trasformata di Karhunen-Loève ha la peculiarità di essere ottimale rispetto all'errore totale in media quadratica che consegue al troncamento della serie. Questa caratteristica ha conferito a tale metodo ed alle sue generalizzazioni un notevole successo tra le discipline applicate.