A novel double-hybrid learning method for modal frequency-based damage assessment of bridge structures under different environmental variation patterns

Monitoring of modal frequencies under an unsupervised learning framework is a practical strategy for damage assessment of civil structures, especially bridges. However, the key challenge is related to high sensitivity of modal frequencies to environmental and/or operational changes that may lead to economic and safety losses. The other challenge pertains to different environmental and/or operational variation patterns in modal frequencies due to differences in structural types, materials, and applications, measurement periods in terms of short and/or long monitoring programs, geographical locations of structures, weather conditions, and influences of single or multiple environmental and/or operational factors, which may cause barriers to employing stateof-the-art unsupervised learning approaches. To cope with these issues, this paper proposes a novel double-hybrid learning technique in an unsupervised manner. It contains two stages of data partitioning and anomaly detection, both of which comprise two hybrid algorithms. For the first stage, an improved hybrid clustering method based on a coupling of shared nearest neighbor searching and density peaks clustering is proposed to prepare local information for anomaly detection with the focus on mitigating environmental and/or operational effects. For the second stage, this paper proposes an innovative non-parametric hybrid anomaly detector based on local outlier factor. In both stages, the number of nearest neighbors is the key hyperparameter that is automatically determined by leveraging a self-adaptive neighbor searching algorithm. Modal frequencies of two full-scale bridges are utilized to validate the proposed technique with several comparisons. Results indicate that this technique is able to successfully eliminate different environmental and/or operational variations and correctly detect damage

Scheme dependence of NLO corrections to exclusive processes

We apply the so-called conformal subtraction scheme to predict perturbatively exclusive processes beyond leading order. Taking into account evolution effects, we study the scheme dependence for the photon-to-pion transition form factor and the electromagnetic pion form factor at next-to-leading order for different pion distribution amplitudes. Relying on the conformally covariant operator product expansion and using the known higher order results for polarized deep inelastic scattering, we are able to predict perturbative corrections to the hard-scattering amplitude of the photon-to-pion transition form factor beyond next-to-leading order in the conformal scheme restricted to the conformal limit of the theory.Comment: RevTeX, 25 pages, 2 figures, 5 tables, minor changes, to be published in Phys. Rev.

Complete next-to-leading order perturbative QCD prediction for the pion electromagnetic form factor

We present the results of a complete leading-twist next-to-leading order (NLO) QCD analysis of the spacelike pion electromagnetic form factor at large momentum transfer Q. We have studied their dependence on the form of the pion distribution amplitude. For a given distribution amplitude, we have examined the sensitivity of the predictions to the choice of the renormalization and factorization scales. Theoretical uncertainty of the LO results related to the renormalization scale ambiguity has been significantly reduced by including the NLO corrections. Adopting the criteria according to which a NLO prediction is considered reliable if, both, the ratio of the NLO to LO contributions and the strong coupling constant are reasonably small, we find that reliable perturbative predictions for the pion electromagnetic form factor with all distribution amplitudes considered can already be made at a momentum transfer Q<10 GeV, with corrections to the LO results being typically of the order of ~ 20%. To check our predictions and to discriminate between the distribution amplitudes, it is necessary to obtain experimental data extending to higher values of Q.Comment: 39 pages, RevTex, 17 figures included; revised version (an error in the analytical expression for T_H corrected, numerical results correspondigly modified; presentation of the results modified to some extent and some points discussed in more detail after referees reports

Next-to-next-to-leading order prediction for the photon-to-pion transition form factor

We evaluate the next-to-next-to-leading order corrections to the hard-scattering amplitude of the photon-to-pion transition form factor. Our approach is based on the predictive power of the conformal operator product expansion, which is valid for a vanishing $\beta$-function in the so-called conformal scheme. The Wilson--coefficients appearing in the non-forward kinematics are then entirely determined from those of the polarized deep-inelastic scattering known to next-to-next-to-leading accuracy. We propose different schemes to include explicitly also the conformal symmetry breaking term proportional to the $\beta$-function, and discuss numerical predictions calculated in different kinematical regions. It is demonstrated that the photon-to-pion transition form factor can provide a fundamental testing ground for our QCD understanding of exclusive reactions.Comment: 62 pages LaTeX, 2 figures, 9 tables; typos corrected, some references added, to appear in Phys. Rev.

The Evolution of the Pion Distribution Amplitude in Next-to-Leading Order

The evolution of the pion distribution amplitude in next-to-leading order is studied for a fixed and a running coupling constant. In both cases, the evolution provides a logarithmic modification in the endpoint region. Assuming a simple parameterization of the distribution amplitude at a scale of $Q_0\sim 0.5\ \rm GeV$, it is shown numerically that these effects are large enough at $Q\sim 2\ \rm GeV$ that they have to be taken into account in the next-to-leading-order analysis for exclusive processes. Alternatively, by introducing a new distribution amplitude that evolves more smoothly, this logarithmic modification can be included in the hard-scattering part of the considered process.Comment: 17 pages LaTeX + 3 uuencoded and compressed postscript figure

Health monitoring of large‐scale civil structures: An approach based on data partitioning and classical multidimensional scaling

A major challenge in structural health monitoring (SHM) is the efficient handling of big data, namely of high‐dimensional datasets, when damage detection under environmental variability is being assessed. To address this issue, a novel data‐driven approach to early damage detection is proposed here. The approach is based on an efficient partitioning of the dataset, gathering the sensor recordings, and on classical multidimensional scaling (CMDS). The partitioning procedure aims at moving towards a low‐dimensional feature space; the CMDS algorithm is instead exploited to set the coordinates in the mentioned low‐dimensional space, and define damage indices through norms of the said coordinates. The proposed approach is shown to efficiently and robustly address the challenges linked to high‐dimensional datasets and environmental variability. Results related to two large‐scale test cases are reported: the ASCE structure, and the Z24 bridge. A high sensitivity to damage and a limited (if any) number of false alarms and false detections are reported, testifying the efficacy of the proposed data‐driven approach