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.

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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

This paper addresses the aerodynamic effect on the nonlinear oscillation, particularly parametric vibration of cables in cable-stayed bridges. A simplified 2-DOF model, including a beam and a stayed cable, is formulated first. Response of the cable under global harmonic excitation which is associated with wind speed is obtained using the multiple scales method. Via numerical analysis, the stability condition of the cable in terms of wind speed is derived. The method is applied to a numerical example and a long-span bridge to analyze its all stay cables. It is demonstrated that very large vibration at one of the longest cables in the middle span of the bridge can be parametrically excited when the wind speed is over around 210 km/h (58.5 m/s).

Parametric Oscillation of Cables and Aerodynamic Effect

The guided wave technique is commonly used in structural health monitoring as the guided waves can propagate far in the structures without much energy loss. The guided waves are conventionally generated by the surface-mounted piezoelectric wafer active sensor (PWAS). However, there is still lack of understanding of the wave propagation in layered structures, especially in structures made of anisotropic materials such as carbon fiber reinforced polymer (CFRP) composites. In this paper, the Rayleigh-Lamb wave strain tuning curves in a PWAS-mounted unidirectional CFRP plate are analytically derived using the normal mode expansion (NME) method. The excitation frequency spectrum is then multiplied by the tuning curves to calculate the frequency response spectrum. The corresponding time domain responses are obtained through the inverse Fourier transform. The theoretical calculations are validated through finite element analysis and an experimental study. The PWAS responses under the free, debonded and bonded CFRP conditions are investigated and compared. The results demonstrate that the amplitude and travelling time of wave packet can be used to evaluate the CFRP bonding conditions. The method can work on a baseline-free manner.

Guided wave field calculation in anisotropic layered structures using normal mode expansion method

A novel damage detection method is applied to a 3-story frame structure, to obtain statistical quantification control criterion of the existence, location and identification of damage. The mean, standard deviation, and exponentially weighted moving average (EWMA) are applied to detect damage information according to statistical process control (SPC) theory. It is concluded that the detection is insignificant with the mean and EWMA because the structural response is not independent and is not a normal distribution. On the other hand, the damage information is detected well with the standard deviation because the influence of the data distribution is not pronounced with this parameter. A suitable moderate confidence level is explored for more significant damage location and quantification detection, and the impact of noise is investigated to illustrate the robustness of the method.

Statistical damage detection method for frame structures using a confidence interval

Sparse Bayesian technique for load identification and full response reconstruction

Varying temperature may cause a non-uniform temperature distribution of a bridge and lead to excessive movement and stresses of the structure. Traditional thermal analyses of bridges adopt a divide-and-conquer approach, which conducts a simplified 2D or local 3D heat-transfer analysis and then a global 3D structural analysis by inputting the calculated temperature into another bridge model. This process requires considerable manual intervention and is inefficient and may lead to inaccurate results. This study develops a unified approach of heat-transfer and structural analyses for the first time to calculate the temperature distribution and the associated responses of an entire structure by integrating the field monitoring data. The arch footbridge at the Hong Kong Polytechnic University is used as a testbed, and a detailed finite element model (FEM) of the bridge is established. The measured air temperature and solar radiation are used as the thermal boundary conditions. The hemisphere technique is adopted to calculate the view factor between different surfaces of the bridge, which are then used to obtain the solar radiation on all external surfaces in different instants on different dates. The 3D global hear-transfer analysis is conducted to obtain the temperature distribution of the entire bridge. The calculated temperature data of the bridge are then automatically input into the same FEM of the bridge to calculate the temperature-induced bridge responses via the structural analysis. The heat-transfer analysis and structural analysis share the same FEM while using different element types. Therefore, the manual intervention is avoided. The calculated and monitored temperature data and responses show a good agreement. The developed new unified approach enables an automatic and efficient analysis of thermal behaviors of bridges. This approach can be extended to other types of bridges.

Yong Xia

Papers

Parametric Oscillation of Cables and Aerodynamic Effect

Guided wave field calculation in anisotropic layered structures using normal mode expansion method

Statistical damage detection method for frame structures using a confidence interval

Sparse Bayesian technique for load identification and full response reconstruction

Temperature behaviors of an arch bridge through integration of field monitoring and unified numerical simulation