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:

Thank you for downloading regularization of inverse problems. Maybe you have knowledge that, people have search hundreds times for their favorite novels like this regularization of inverse problems, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some infectious bugs inside their computer. regularization of inverse problems is available in our book collection an online access to it is set as public so you can download it instantly. Our book servers spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the regularization of inverse problems is universally compatible with any devices to read.

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

Although vibration-based structural damage detection methods have demonstrated various degrees of success, the damage detection of civil structures still remains as a challenging task. The main obstacles include the insensitivity to local damage and the high sensitivity to measurement noise. A new structural damage detection method based on the statistical moments of dynamic responses of a structure has been recently proposed by the authors, and the numerical study manifested that the proposed method is sensitive to local structural damage but insensitive to measurement noise. The experimental investigation on this method is presented in this article. Three shear building models with and without damage were built and subjected to ground motions generated by a shaking table. The displacement and acceleration responses of each building model at each floor were recorded. The recorded ground motion and building responses as well as identified structural damping ratios were then used to identify damage locatio...

Experimental investigation on statistical moment-based structural damage detection method

Shear connectors are widely used to link the slab and girders together in slab-on-girder structures. Assessment of the integrity of shear connectors is an important issue in the maintenance and health monitoring of such structures. In this study, a 1:3 scaled bridge model was constructed with removable anchors linking the slab and girders as shear connectors. Each anchor consisted of a threaded bar penetrating through the soffit of a girder and anchored in the slab by an embedded nut cap with welded steel bar. Different damage scenarios were simulated by removing some of these connectors. A signal-based damage detection method in which the damage feature is characterized by the wavelet packet energy changes was applied to the damage identification of the shear connectors. Using measurements of hammer impact responses, the locations of the loosening of the shear connectors were effectively detected. This paper demonstrates that the wavelet packet-based energy index is a sensitive local damage indicator. The results also show that comparison of the relative vibration between the slab and the girders is a more effective technique for evaluating the shear connector damage than comparison of the vibration of the slab before and after shear connector damage. This finding is particularly interesting because data from the intact (undamaged) bridge is not required. The suitability and efficiency of the approach is expected to be applied in situ to assess the integrity of shear connectors in existing structures.

Yong Xia

Papers

Experimental investigation on statistical moment-based structural damage detection method

Damage identification of shear connectors with wavelet packet energy: Laboratory test study

Singular spectrum analysis for enhancing the sensitivity in structural damage detection

Review on field monitoring of high‐rise structures

Dynamic condensation approach to calculation of structural responses and response sensitivities