Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A number of image processing techniques IPTs have been implemented for detecting civil infrastructure defects to partially replace human-conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying real-world situations e.g., lighting and shadow changes can lead to challenges to the wide adoption of IPTs. To overcome these challenges, this article proposes a vision-based method using a deep architecture of convolutional neural networks CNNs for detecting concrete cracks without calculating the defect features. As CNNs are capable of learning image features automatically, the proposed method works without the conjugation of IPTs for extracting features. The designed CNN is trained on 40 K images of 256 × 256 pixel resolutions and, consequently, records with about 98% accuracy. The trained CNN is combined with a sliding window technique to scan any image size larger than 256 × 256 pixel resolutions. The robustness and adaptability of the proposed approach are tested on 55 images of 5,888 × 3,584 pixel resolutions taken from a different structure which is not used for training and validation processes under various conditions e.g., strong light spot, shadows, and very thin cracks. Comparative studies are conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results show that the proposed method shows quite better performances and can indeed find concrete cracks in realistic situations.

/pdf/deep-learning-based-crack-damage-detection-using-1cucavd1rf.pdf

Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

A comprehensive review on modal parameter-based damage identification methods for beam- or plate-type structures is presented, and the damage identification algorithms in terms of signal processing...

Vibration-based Damage Identification Methods: A Review and Comparative Study:

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Regularization Of Inverse Problems

Structures in Other Domains The methodology of structural analysis discussed in this article has been applied beyond the narrow realm of natural language syntax that we have discussed in this article. Within the study of language, similar methods of analysis have been pervasively applied to the study of sounds (phonology), words (morphology), and meanings (semantics), yielding a range of of abstract structural representations whose properties bear considerable explanatory burden. There are a wealth of cases in each of these domains analogous to those discussed here, though space prevents us from going in these (see Akmajian, Demers, Farmer and Harnish 1995 for a traditional overview, and Jackendo 1994 for one more focused on connections with cognitive science). Additionally, these representations have shed substantial light on the processes of language acquisition and language change. It has been found that variation in the types of sentences that are used, whether during the course of children's acquisition of their native languages or in the centuries-long periods of linguistic change, are best characterized not as super cial and haphazard alterations, but rather in terms of parametric modi cations to the fundamental underlying grammatical rules and constraints. Moving outside the domain of language, one application of these same methods has been in the study of music cognition. Just as the representations of linguistic theory arise out of an attempt to model speakers' intuitions about well-formedness and possible meanings of the sentences of their

Structural analysis

Fatigue assessment of multi-loading suspension bridges using continuum damage model

http://ira.lib.polyu.edu.hk/bitstream/10397/21585/1/Wang_Structural_Damage_Measure.pdf

Structural damage measure index based on non-probabilistic reliability model

A new eigensolution of structures via dynamic condensation

Sparse Bayesian learning for structural damage detection using expectation–maximization technique

A huge number of data can be obtained continuously from a number of sensors in long-term structural health monitoring (SHM). Different sets of data measured at different times may lead to inconsistent monitoring results. In addition, structural responses vary with the changing environmental conditions, particularly temperature. The variation in structural responses caused by temperature changes may mask the variation caused by structural damages. Integration and interpretation of various types of data are critical to the effective use of SHM systems for structural condition assessment and damage detection. A data fusion-based damage detection approach under varying temperature conditions is presented. The Bayesian-based damage detection technique, in which both temperature and structural parameters are the variables of the modal properties (frequencies and mode shapes), is developed. Accordingly, the probability density functions of the modal data are derived for damage detection. The damage detection results from each set of modal data and temperature data may be inconsistent because of uncertainties. The Dempster–Shafer (D–S) evidence theory is then employed to integrate the individual damage detection results from the different data sets at different times to obtain a consistent decision. An experiment on a two-story portal frame is conducted to demonstrate the effectiveness of the proposed method, with consideration on model uncertainty, measurement noise, and temperature effect. The damage detection results obtained by combining the damage basic probability assignments from each set of test data are more accurate than those obtained from each test data separately. Eliminating the temperature effect on the vibration properties can improve the damage detection accuracy. In particular, the proposed technique can detect even the slightest damage that is not detected by common damage detection methods in which the temperature effect is not eliminated.

/pdf/data-fusion-based-structural-damage-detection-under-varying-4iv2awrpic.pdf

Yong Xia

Papers

Fatigue assessment of multi-loading suspension bridges using continuum damage model

Structural damage measure index based on non-probabilistic reliability model

A new eigensolution of structures via dynamic condensation

Sparse Bayesian learning for structural damage detection using expectation–maximization technique

Data fusion-based structural damage detection under varying temperature conditions