Introduction [to special issue (v.25:5, July 2010)of the CACAIE journal, devoted to some recent research applying CI in structural engineering and mechanics]

No abstract

Damage Detection of Structural Systems with Noisy Incomplete Input and Response Measurements

A probabilistic approach for damage detection is presented using noisy incomplete input and response measurements that is an extension of a Bayesian system identification approach developed by the authors. This situation may be encountered, for example, during low-level ambient vibrations when a structure is instrumented with accelerometers that measure the input ground motion and structural response at a few locations but the wind excitation is not measured. A substructuring approach is used for the parameterization of the mass and stiffness distributions. Damage is defined to be a reduction of the substructure stiffness parameters compared with those of the undamaged structure. By using the proposed probabilistic methodology, the probability of various damage levels in each substructure can be calculated based on the available data. A four-story benchmark building subjected to wind and ground shaking is considered in order to demonstrate the proposed approach

Unified probabilistic approach for model updating and damage detection

A probabilistic approach for model updating and damage detection of structural systems is presented using noisy incomplete input and incomplete response measurements. The situation of incomplete input measurements may be encountered, for example, during low-level ambient vibrations when a structure is instrumented with accelerometers that measure the input ground motion and the structural response at a few instrumented locations but where other excitations, e.g., due to wind, are not measured. The method is an extension of a Bayesian system identification approach developed by the authors. A substructuring approach is used for the parameterization of the mass, damping and stiffness distributions. Damage in a substructure is defined as stiffness reduction established through the observation of a reduction in the values of the various substructure stiffness parameters compared with their initial values corresponding to the undamaged structure. By using the proposed probabilistic methodology, the probability of various damage levels in each substructure can be calculated based on the available dynamic data. Examples using a single-degree-of-freedom oscillator and a 15-story building are considered to demonstrate the proposed approach

Experimental characterization of multiple cracks in a cantilever beam utilizing transient vibration data following a probabilistic approach

This paper puts forward a practical method for detecting multiple cracks on beams by utilizing transient vibration data. To explicitly address the uncertainty that is induced by measurement noise and modeling error, the Bayesian statistical framework is followed in the proposed crack detection method, which consists of two stages. In the first stage the number of cracks is identified by a computationally efficient algorithm that utilizes the Bayesian model class selection method. In the second stage, the posterior probability density function (PDF) of crack characteristics (i.e., the crack locations and crack depths) are determined by the Bayesian model updating method. The feasibility of the proposed methodology is experimentally demonstrated using a cantilever beam with one and two artificial cracks with depths between 0% and 50% of the beam height. The experimental data consists of transient vibration time histories that are collected at a single location using a laser Doppler vibrometer measurement system and impact excitations at three locations along the beam. The results show that the two-stage procedure enables the identification of the correct number of cracks and corresponding locations and extents, together with the coefficient of variation (COV).H.F. Lam, C.T. Ng, M. Veid

Model Identification and Seismic Analysis of Meloland Road Overcrossing

This report presents the results of research directed toward model identification and seismic analysis of the MRO. This research has been implemented to meet the requirements of Tasks 4 and 5 of the UNR-D&M research program (Sec. 1.1.3) and also to provide a basis for developing improved bridge modeling procedures as required under the D & M research program on SBOs (Sec. 1.1.4). The scope of this research effort consisted of our development of a finite element model of the MRO whose parameters were estimated through the application of state-of-the-art system identification methods to the MRO's recorded motions from the Imperial Valley Earthquake. These estimated model parameters were also checked for consistency with an overall range of model parameter values computed using established engineering procedures. This model was then used in a series of parametric dynamic analyses of the seismic response of the MRO which enabled us to evaluate the effects of uncertainties in the various model parameters on the demand forces and moments in the structural members and the foundation springs. Maximum foundation spring forces and moments obtained from these analyses were used as input to nonlinear static analyses of the MRO's pile foundations in order to compute the demand forces and moments within the piles. The demand forces and moments within the MRO's structural and pile elements were then compared against the capacities of these elements. These analysis results have been interpreted to assess the seismic performance and design of the MRO, and also to provide an important basis for our development of improved modeling and seismic evaluation procedures for short bridge overcrossing structures. The above efforts have focused on the modeling and analysis of the MRO's translational and rotational response to transverse horizontal input motions; i.e., the bridge's response to vertical and longitudinal input motions was not included in this research. This focus on the MRO's response to transverse horizontal input motions was adopted because: (a) this response will lead to more severe earthquake-induced internal forces and moments, particularly in the central pier which is the element of an SBO that is typically most vulnerable to seismic excitation; and (b) our past evaluations of the MRO's recorded motions have shown that its response to transverse horizontal input motions is strongly affected by SSI, whereas SSI has only a negligible effect of the MRO's response to vertical and longitudinal input motions (Werner, et. al., 1987)

A probabilistic method for the detection of obstructed cracks of beam-type structures using spatial wavelet transform

This paper reports both the theoretical development and the numerical verification of a practical wavelet-based crack detection method, which identifies first the number of cracks and then the corresponding crack locations and extents. The value of the proposed method lies in its ability to detect obstructed cracks when measurement at or close to the cracked region is not possible. In such situations, most nonmodel-based methods, which rely on the abnormal change of certain indicators (e.g., curvature and strain mode shapes) at or close to the cracks, cannot be used. Most model-based methods follow the model updating approach. That is, they treat the crack location and extent as model parameters and identify them by minimizing the discrepancy between the modelled and measured dynamic responses. Most model-based methods in the literature can only be used in single- or multi-crack cases with a given number of cracks. One of the objectives of this paper is to develop a model-based crack detection method that is applicable in a general situation when the number of cracks is not known in advance. To explicitly handle the uncertainties associated with measurement noise and modelling error, the proposed method uses the Bayesian probabilistic approach. In particular, the method aims to calculate the posterior (updated) probability density function (PDF) of the crack locations and the corresponding extents. The proposed wavelet-based crack detection method is verified and demonstrated through a comprehensive series of numerical case studies, in which noisy data were generated by a Bernoulli-Euler beam with semi-rigid connections. The results show that the method can correctly identify the number of cracks even when the crack extent is small. The effects of the number of cracks and the crack extents on the results of crack detection are also studied and discussed in this paper. © 2007 Elsevier Ltd. All rights reserved.H.F. Lam, C.T. N

Μοριακή και ανοσοϊστοχημική διερεύνηση της μικροδορυφορικής αστάθειας (MSI) σε καρκινώματα παχέος εντέρου

Εισαγωγή Στην εποχή μας η ιατρική επιστήμη έχει αρχίσει να υιοθετεί την εξατομικευμένη ιατρική θεραπεία σε διάφορες παθήσεις. Καθοριστικής σημασίας στην πρόγνωση και στη θεραπεία του καρκίνου αποτελεί το μοριακό προφίλ του όγκου. Στην παρούσα αναδρομική μελέτη μελετήσαμε τα μοριακά χαρακτηριστικά και τις κλινικοπαθολογοανατομικές συσχετίσεις του ΚΠΕ σε Έλληνες ασθενείς. Υλικά και Μέθοδοι/ Ασθενείς και Μέθοδοι Συλλέξαμε πληροφορίες και παθολογοανατομικά δεδομένα από 157 ασθενείς με καρκίνο του παχέος εντέρου. Mετά από απομόνωση του DNA από τους κύβους παραφίνης των ασθενών, ακολούθησε PCR πραγματικού χρόνου και High Resolution Melting Analysis (HRM) για την ανίχνευση μεταλλαγών στα γονίδια KRAS, BRAF, NRAS και στους υπό διερεύνηση δείκτες μικροδορυφορικής αστάθειας (ΒΑΤ26, ΒΑΤ25, ΝR24, NR21). Οι στατιστικές συσχετίσεις πραγματοποιήθηκαν με το πακέτο SPSSv 21.0. Αποτελέσματα Μεταλλάξεις του KRAS ανιχνεύτηκαν στο 39,3% των περιπτώσεων,  του BRAF στο 10,9% και του NRAS στο 4,9%. Μικροδορυφορική αστάθεια παρατηρήθηκε στο 11,5% των ασθενών με καρκίνο του παχέος εντέρου και συσχετίστηκε με καρκινώματα του δεξιού παχέος εντέρου. Ο MSI φαινότυπος είχε αρνητική συσχέτιση με το στάδιο, την παρουσία μεταστάσεων σε λεμφαδένες καθώς και τις μεταλλάξεις του KRAS, ενώ είχε θετική συσχέτιση με μεταλλάξεις του BRAF. Συμπέρασμα MSI θετικά καρκινώματα του παχέος εντέρου στον ελληνικό πληθυσμό είναι συχνότερα στο δεξιό παχύ έντερο, KRAS μη μεταλλαγμένα και BRAF μεταλλαγμένα. Η συλλογή περαιτέρω κλινικοπαθολογοανατομικών και μοριακών δεδομένων για κάθε πληθυσμό θα επιτρέψει στο μέλλον την καλύτερη κλινική προσέγγιση των ασθενών καθώς και την στοχευμένη θεραπεία αυτών.Background/Aim: In the present retrospective study, we assessed the molecular profile and clinicopathological correlations of Greek colorectal carcinoma (CRC) patients. Patients and Methods: Data from 157 CRC patients were collected. High Resolution Melting Analysis and Pyrosequencing/Sanger sequencing were applied to identify KRAS, BRAF, NRAS mutations and Microsatellite instability (MSI) status.Immunohistochemistry was used to characterize the associated Mismatch Repair Protein loss. Statistical calculations were performed using the statistical package SPSS v21.0. Results: KRAS mutations were detected in 39.3% of cases, BRAF in 10.9% and NRAS in 4.9%. MSI status was recognized in 11.5% of CRC patients and was associated with right colon tumors. MSI phenotype was inversely correlated with stage, N status and KRAS mutations and positively correlated with BRAF mutations. Conclusion: MSI positive CRCs in the Greek population are more often right-sided, free of metastasis, KRAS wild type and BRAF mutated. Providing more detailed clinicopathological and molecular data for specific populations enables better clinical management and individualized therapy in the future

Updating of a Model and its Uncertainties Utilizing Dynamic Test Data

The problem of updating a structural model and its associated uncertainties by utilizing structural response data is addressed. Using a Bayesian probabilistic formulation, 6the updated "posterior" probability distribution of the uncertain parameters is obtained and it is found that for a large number of data points it is very peaked at some "optimal" values of the parameters. These optimal parameters can be obtained by minimizing a positive-definite measure-of-fit function. This paper focuses on the identifiability of the optimal parameters. The problem of finding the whole set of optimal models that have the same output at the observed degrees of freedom for a given input is resolved for the first time, by presenting an algorithm which methodically and efficiently searches the parameter space. Also, a simplified expression is given for the weighting coefficients associated with each optimal model which are involved in the probability distribution for the predicted response