Performance indicator of a bridge expansion joint

In general the condition of a structure can be assessed in terms of a performance indicator. For example, this can be the strength of a structure. An asset manager is concerned with ensuring that the performance of a structure does not fall below a given minimum level. This can be achieved by inspecting or monitoring the structure. As the performance indicator decreases with time, the asset manager can decide to take pre-emptive measures to restore the condition to its initial level, thus avoiding getting too close to the minimum required level. In order to work this way, it is important to define a reliable performance indicator. Following an inventory of structures which are prone to some form of degradation over time, a modular bridge expansion joint was selected as a case to be considered in this investigation. In order to determine the observability of known failure mechanisms in terms of modal and spectral data an experiment is set up which can simulate the construction under these circumstances. Further, a Finite Element Model of the construction is made, which is tuned to the experimental setup. This model is used to validate the applied inverse modeling technique to identify the failure parameters. The inverse modeling is performed using a genetic algorithm. Through inverse modeling of the monitor data, changes over time of the identified failure parameters are obtained. In order to predict the development of the observed failure parameters in the future, these changes over time are incorporated in a prediction model. Taking uncertainties into account, stochastic processes are used to describe the degradation process. Thus, different types of processes can be used, e.g. Markov chains for discrete state changes in time or Gamma processes for continuous quantities in time. By continuously updating the prediction model with the monitor data, a risk based maintenance management tool is obtained by which pro-active and well-planned maintenance actions can be decided on. The developed methodology is applied to a full scale monitoring system of a real bridge in the Netherlands

Tensor Decomposition Reveals Concurrent Evolutionary Convergences and Divergences and Correlations with Structural Motifs in Ribosomal RNA

Evolutionary relationships among organisms are commonly described by using a hierarchy derived from comparisons of ribosomal RNA (rRNA) sequences. We propose that even on the level of a single rRNA molecule, an organism's evolution is composed of multiple pathways due to concurrent forces that act independently upon different rRNA degrees of freedom. Relationships among organisms are then compositions of coexisting pathway-dependent similarities and dissimilarities, which cannot be described by a single hierarchy. We computationally test this hypothesis in comparative analyses of 16S and 23S rRNA sequence alignments by using a tensor decomposition, i.e., a framework for modeling composite data. Each alignment is encoded in a cuboid, i.e., a third-order tensor, where nucleotides, positions and organisms, each represent a degree of freedom. A tensor mode-1 higher-order singular value decomposition (HOSVD) is formulated such that it separates each cuboid into combinations of patterns of nucleotide frequency variation across organisms and positions, i.e., “eigenpositions” and corresponding nucleotide-specific segments of “eigenorganisms,” respectively, independent of a-priori knowledge of the taxonomic groups or rRNA structures. We find, in support of our hypothesis that, first, the significant eigenpositions reveal multiple similarities and dissimilarities among the taxonomic groups. Second, the corresponding eigenorganisms identify insertions or deletions of nucleotides exclusively conserved within the corresponding groups, that map out entire substructures and are enriched in adenosines, unpaired in the rRNA secondary structure, that participate in tertiary structure interactions. This demonstrates that structural motifs involved in rRNA folding and function are evolutionary degrees of freedom. Third, two previously unknown coexisting subgenic relationships between Microsporidia and Archaea are revealed in both the 16S and 23S rRNA alignments, a convergence and a divergence, conferred by insertions and deletions of these motifs, which cannot be described by a single hierarchy. This shows that mode-1 HOSVD modeling of rRNA alignments might be used to computationally predict evolutionary mechanisms

Detection of secondary structures in 17-mer Ru(II)-labeled single-stranded oligonucleotides from luminescence lifetime studies.

The emission properties of a non intercalating complex, [Ru(TAP)2(dip)]2+ (TAP = 1,4,5,8-tetraazaphenanthrene; dip = 4,7-diphenyl-1,10-phenanthroline), tethered to 17-mer single-stranded oligodeoxyribonucleotides (ODNs) either in the middle or at the 5'-end of the sequence, are determined. The results highlight the fact that the luminescence of this metallic compound is sufficiently sensitive to its microenvironment to probe self-structuration of these short single-stranded ODNs. It is shown that the weighted averaged emission lifetimes (tau(M)) along with the quenching rate constants of luminescence by oxygen reflect particularly well different structures adopted by the different ODNs sequences. The determination of these parameters thus offers an elegant way to examine possible structurations of synthetic single-stranded ODNs that play important roles in biological applications.Journal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe

Performance indicator of a bridge expansion joint

In general the condition of a structure can be assessed in terms of a performance indicator. For example, this can be the strength of a structure. An asset manager is concerned with ensuring that the performance of a structure does not fall below a given minimum level. This can be achieved by inspecting or monitoring the structure. As the performance indicator decreases with time, the asset manager can decide to take pre-emptive measures to restore the condition to its initial level, thus avoiding getting too close to the minimum required level. In order to work this way, it is important to define a reliable performance indicator. Following an inventory of structures which are prone to some form of degradation over time, a modular bridge expansion joint was selected as a case to be considered in this investigation. In order to determine the observability of known failure mechanisms in terms of modal and spectral data an experiment is set up which can simulate the construction under these circumstances. Further, a Finite Element Model of the construction is made, which is tuned to the experimental setup. This model is used to validate the applied inverse modeling technique to identify the failure parameters. The inverse modeling is performed using a genetic algorithm. Through inverse modeling of the monitor data, changes over time of the identified failure parameters are obtained. In order to predict the development of the observed failure parameters in the future, these changes over time are incorporated in a prediction model. Taking uncertainties into account, stochastic processes are used to describe the degradation process. Thus, different types of processes can be used, e.g. Markov chains for discrete state changes in time or Gamma processes for continuous quantities in time. By continuously updating the prediction model with the monitor data, a risk based maintenance management tool is obtained by which pro-active and well-planned maintenance actions can be decided on. The developed methodology is applied to a full scale monitoring system of a real bridge in the Netherlands

Performance indicator of a bridge expansion joint Citation for published version (APA): PERFORMANCE INDICATOR OF A BRIDGE EXPANSION JOINT

Abstract. In general the condition of a structure can be assessed in terms of a performance indicator. For example, this can be the strength of a structure. An asset manager is concerne