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In this study, the application of four classification techniques for computer vision–based pavement crack detection systems was investigated. The classification methods—artificial neural network (ANN), decision tree, k–nearest neighbor, and adaptive neuro-fuzzy inference system (ANFIS)—were selected on the basis of the complexity and clarity of their procedures. These methods were evaluated for ( a) prediction performance, ( b) computation time, ( c) stability of results for highly imbalanced data sets, ( d) stability of the classifiers’ performance for pavements in different deterioration stages, and ( e) interpretability of results and clarity of the procedure. According to the results, the ANN and ANFIS methods not only provide superior performance but also are more flexible and compatible for the crack detection application. The ANFIS method is called a “white-box classifier,” and the inferred knowledge from its membership functions can be used to characterize the imagery properties of detected image components.

Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task remains challenging due to the high variation of defects in shapes and sizes, demanding a better incorporation of contextual information into deep networks. In this paper, we show that an attention-based multi-scale convolutional neural network (A+MCNN) improves the automated classification of common distress and non-distress objects in pavement images by (i) encoding contextual information through multi-scale input tiles and (ii) employing a mid-fusion approach with an attention module for heterogeneous image contexts from different input scales. A+MCNN is trained and tested with four distress classes (crack, crack seal, patch, pothole), five non-distress classes (joint, marker, manhole cover, curbing, shoulder), and two pavement classes (asphalt, concrete). A+MCNN is compared with four deep classifiers that are widely used in transportation applications and a generic CNN classifier (as the control model). The results show that A+MCNN consistently outperforms the baselines by 1∼26% on average in terms of the F-score. A comprehensive discussion is also presented regarding how these classifiers perform differently on different road objects, which has been rarely addressed in the existing literature.

The reliable detection of relatively small changes in the characteristics of monitored systems, which simultaneously involve nonlinear phenomena as well as uncertain parameters, is a challenging problem whose resolution is crucial to the development of practical structural health monitoring methodologies slated for use with complex physical systems. This paper reports the results of a comprehensive experimental study involving an adaptive nonlinear component (an actively controlled magnetorheological damper) that was used to investigate the representation and propagation of uncertainties in a probabilistic format that provides a convenient means for reliable detection of small changes in uncertain nonlinear systems. In experimental studies of the MR damper, the uncertainty of the system characteristics was precisely controlled with known input-current statistics. A total of 4000 tests were performed, and the MR damper was identified using the restoring force method with both orthogonal and non-orthogonal basis functions. The identification results show that the identified coefficients involving orthogonal basis functions have several desirable features that are ideal for condition assessment purposes when dealing with complex nonlinear systems whose underlying physics is not amenable to easy modeling: (1) no a priori knowledge of the systems characteristics is required; (2) the orthogonal coefficients are statistically unbiased with respect to model complexity; and (3) the distributions of the orthogonal coefficients can be reliably used to detect changes in uncertain nonlinear systems, even if a reduced-order model is used in the identification process.

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A study is presented comparing several identification approaches, both parametric and nonparametric, for developing reduced-order nonlinear models of full-scale nonlinear viscous dampers commonly used with large flexible bridges. Such models are useful for incorporation into large-scale computational models, as well as for use as part of structural health monitoring studies based on vibration signature analysis. The paper reports the analysis results from a large collection of experimental tests on a 1112 kN (250 kip) orifice viscous damper under a wide range of frequency and amplitude oscillations. A simplified parametric design model is used in the parametric phase, as well as two different nonparametric methods: the Restoring Force Method, and artificial neural networks. The variations of model parameters with the excitation and response characteristics are investigated, and the relative accuracy and fidelity of the modeling approaches are compared and evaluated.

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