Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.
Asbestos cement (AC) water mains were installed extensively in North America, Europe, and Australia during
1920s-1980s and subject to a high breakage rate in recent years in some utilities. It is essential to understand
how the influential factors contribute to the degradation and failure of AC pipes. The historical failure data
collected from twenty utilities are used in this study to explore the correlation between pipe condition and its
In this paper, we applied four nonparametric regression methods to model the relationship between pipe
failure represented by average break rates and influential variables including pipe age and internal and external
working environmental parameters. The nonparametric regression models do not take a predetermined form
but it needs information derived from data. The feasibility of using a nonparametric regression model for the
condition assessment of AC pipes is investigated and understood.
Laser-based scanning can provide a precise surface profile. It has been widely applied to the inspection of
pipe inner walls and is often used along with other types of sensors, like sonar and close-circuit television
(CCTV). These measurements can be used for pipe deterioration modeling and condition assessment. Geometric
information needs to be extracted to characterize anomalies in the pipe profile. Since the laser scanning measures
the distance, segmentation with a threshold is a straightforward way to isolate the anomalies. However, threshold
with a fixed distance value does not work well for the laser range image due to the intensity inhomogeneity, which
is caused the uncontrollable factors during the inspection. Thus, a local binary fitting (LBF) active contour model is employed in this work to process the laser range image and an image phase congruency algorithm is adopted to provide the initial contour as required by the LBF method. The combination of these two approaches can successfully detect the anomalies from a laser range image.
The quantification of pitting corrosion in terms of material or metal loss is required for the understanding of pipe
condition. One approach to accurately map pitting corrosion is with a high-resolution laser scanner. However,
this process is time consuming and requires the removal of the pipe segment and sandblasting of its surface.
In this study, thermography is considered for the field testing. We investigated the potential of quantifying
pitting corrosion with thermography technique. A cleaned pipe was inspected with the pulsed thermography (PT) technique. Extracted signal features were used to characterize metal loss. The algorithms to process PT inspection data and extract signal features to characterize the pitting corrosion are presented in this paper.
In this work the Dempster-Shafer (DS) theory has been used for fusing nondestructive inspection (NDI) data. The success of a DS-based method depends on how the basic probability assignment
(BPA) or probability mass function is defined. In the case of nondestructive inspection of aircraft lap joints, which is of interest here, the inspection data is presented in raster-scanned images. These images are discriminated by iteratively trained classifiers. The BPA is defined based on the conditional probability of information classes and data classes, which are obtained from
ground truth data and NDI measurements respectively. Then, the Dempster rule of combination is applied to fuse multiple NDI inputs. The maximum mass outputs determine the final classification results. In this work, conventional eddy current (ET) and pulsed eddy current
(P-ET) techniques were employed to inspect the fuselage lap joints of a service-retired Boeing 727 aircraft in order to map corrosion sites. Estimation of the remaining thickness from the inspection data is the aim of this work. The ground truth data was obtained by
teardown inspections followed by a digital X-ray thickness mapping technique, which provides accurate thickness values. The experimental results verify the efficiency of the proposed method.
The fusion of data from Edge of Light(EOL) and eddy current inspections of aircraft lap joints is investigated in this study. The pillowing deformation caused by corrosion products is estimated by the EOL technique first. Eddy current (ET) techniques, e.g. multi-frequency eddy current testing (MF-ET) and pulsed eddy current testing (P-ET), can provide depth-sensitive inspections of fuselage joints. The objective of this study is to investigate how the testing results obtained from the two different methods correlate to each other and what kind of complementary information is available in each
result. This work contains two steps. First, the EOL inspection is quantified through a calibration process where a laser displacement sensor is used to provide the reference. The EOL estimation is for the total material loss while the eddy current or pulsed eddy current testing is employed to provide the complementary information on the remaining thickness.
Second, the ET data are fused with the principle component analysis method and the results are calibrated by a calibration experiment. Finally, the bottom layer corrosion is estimated through the subtraction of EOL and ET results. The preliminary results are presented in this paper.
Multi-frequency techniques are widely adopted for eddy current testing. One of the advantages of these techniques can be deduced from the skin depth formula (formula available in paper) where delta is the standard depth of penetration at excitation frequency f, with the other two parameters, mu and sigma, related to material properties. Thus, an inspection can be performed at several depths into the material with the simultaneous use of multiple frequencies. To investigate the potential of a multi-frequency eddy current technique (MFECT) for corrosion quantification, an experiment was carried out on a two-layered fuselage lap joint splice. Two data fusion approaches, namely Bayesian inference and multiresolution analysis, are investigated in this study to fuse eddy current images of different frequencies. The corrosion types are classified based on the percentage of material loss. The estimated thickness results, based on the fusion processes, are compared with accurate thickness maps obtained from teardown X-ray inspection data.
A computer vision inspection system, named Edge of Light TM (EOL), was invented and developed at the Institute for Aerospace Research of the National Research Council Canada. One application of interest is the detection and quantitative measurement of “pillowing” caused by corrosion in the faying surfaces of aircraft fuselage joints. To quantify the hidden corrosion, one approach is to relate the average corrosion of a region to the peak-to-peak amplitude between two diagonally adjacent rivet centers. This raises the requirement for automatically locating the rivet centers. The first step to achieve this is the rivet edge detection. In this study, gradient-based edge detection, local energy based feature extraction, and an adaptive threshold method were employed to identify the edge of rivets, which facilitated the first step in the EOL quantification procedure. Furthermore, the brightness profile is processed by the derivative operation, which locates the pillowing along the scanning direction. The derivative curves present an estimation of the inspected surface.
In this paper, a new model-based tracking algorithm is proposed for tracking rigid objects in 6 degrees of freedom. Only one calibrated camera is used in the approach which can handle the motion of objects with known geometry. Information in 2D images from the camera would conduct motion in 3D space. The useful image features are contour edges of object to be tracked. The matching process includes two aspects of: (1) feature extraction using local minimum energy and (2) global matching of known 3D models against the projected features. The algorithm is robust to change in lighting and background. The small motion hypothesis is used for fitting feature energy which is defined as the negative absolute value of the edge strength. An autoregressive AR(1) model is employed for detecting incorrect matches in terms of the feature energy. We have found a new invariance-based method to eliminate false matches caused by strong shadow or occlusion. The invariance is the ratio of trigonometric functions of the angles formed by a polygon. Both performance analysis and real object tracking show that the proposed algorithm is effective and robust.