Identification of cracks in pipelines based on machine learning and deep learning

Pipelines are important long-distance transportation structures in modern industry, and because many are buried deep underground, pipeline health monitoring is critical to industry; however, inspecting underground pipelines can be quite challenging due to the large financial and human resources required. For decades, different methods have been used to assess pipeline cracks. Ultrasonic quantitative nondestructive testing (QNDT) is one of the frequently used methods in pipeline health monitoring. In the current study, the coefficients of the reflected and transmitted waves due to different incident waves were first generated by using a semi-analytical finite element method based on classical elasticity theory. In that study, different types of pipes, including different geometries and materials, were considered. Then four different regression machine learning algorithms and three deep learning algorithms were used to identify crack features. In this study, the prediction accuracy was compared between the different algorithms and different datasets. The objective was to find the algorithm with the highest prediction rate and to select a suitable dataset for prediction. It was found that the extremely randomized tree (ERT) algorithm was the best in identifying cracks in the pipeline. The prediction accuracy will be improved by selecting different data sets. In addition, all algorithms performed better in predicting the radial crack depth (CDRD) than predicting the circumferential crack width (CWCD)

Detailed analysis of excited state systematics in a lattice QCD calculation of gAg_A

Excited state contamination remains one of the most challenging sources of systematic uncertainty to control in lattice QCD calculations of nucleon matrix elements and form factors. Most lattice QCD collaborations advocate for the use of high-statistics calculations at large time separations ($t_{\rm sep}\gtrsim1$ fm) to combat the signal-to-noise degradation. In this work we demonstrate that, for the nucleon axial charge, $g_A$, the alternative strategy of utilizing a large number of relatively low-statistics calculations at short to medium time separations ($0.2\lesssim t_{\rm sep}\lesssim1$ fm), combined with a multi-state analysis, provides a more robust and economical method of quantifying and controlling the excited state systematic uncertainty, including correlated late-time fluctuations that may bias the ground state. We show that two classes of excited states largely cancel in the ratio of the three-point to two-point functions, leaving the third class, the transition matrix elements, as the dominant source of contamination. On an $m_\pi\approx310$ MeV ensemble, we observe the expected exponential suppression of excited state contamination in the Feynman-Hellmann correlation function relative to the standard three-point function; the excited states of the regular three-point function reduce to the 1% level for $t_{\rm sep} >2$ fm while, for the Feynman-Hellmann correlation function, they are suppressed to 1% at $t_{\rm sep}\approx1$ fm. Independent analyses of the three-point and Feynman-Hellmann correlators yield consistent results for the ground state. However, a combined analysis allows for a more detailed and robust understanding of the excited state contamination, improving the demonstration that the ground state parameters are stable against variations in the excited state model, the number of excited states, and the truncation of early-time or late-time numerical data.Comment: v1: 13 pages plus appendices. The correlation function data and analysis code accompanying this publication can be accessed at this github repository: https://github.com/callat-qcd/project_fh_vs_3p

Modeling the Relationships between the Height and Spectrum of Submerged Tufa Barrage Using UAV-Derived Geometric Bathymetry and Digital Orthoimages

Tufa barrages play an important role in fluviatile tufa ecosystems and sedimentary records. Quantifying the height of tufa barrage is significant for understanding the evolution and development of the Holocene tufa barrage systems. However, for submerged tufa barrages, there is no low-cost non-contact method to retrieve barrage height. Generally, it is difficult to recognize small tufa barrages by means of remotely sensed satellite data, but the combination of unmanned aerial vehicles (UAV) and Structure-from-Motion (SfM) photogrammetry makes it possible. In this study, we used a fixed-wing UAV and a consumer-grade camera to acquire images of the submerged tufa barrage in Lying Dragon Lake, Jiuzhaigou National Nature Reserve, China, and estimated the height of the tufa barrage through UAV-based photogrammetric bathymetry. On this foundation, the relationship between barrage height and its spectrum was established through band ratio analysis using UAV-derived geometric bathymetry and digital orthoimages, which provided an alternative strategy to characterize the height of submerged tufa barrages. However, the spectral characteristics of submerged tufa barrages will oscillate with changes in the environmental conditions. In future research, we will consider using a dedicated aquatic multispectral camera to improve the experimentation

Quantifying earthquake-induced bathymetric changes in a tufa lake using high-resolution remote sensing data

Detecting earthquake-induced bathymetric changes helps to understand the geomorphologic process of tufa lakes. Traditional field measurement methods are difficult for spatially complete and continuous bathymetric mapping. Multi-temporal high-resolution optical satellite images are cost-efficient data used for bathymetric change detection. However, for detecting bathymetric changes in tufa lakes, collecting high-density depth calibration data and constructing highly robust water depth inversion models pose certain challenges. This study takes Huohua Lake before and after the Jiuzhaigou Earthquake as the research object, and carries out the bathymetric change detection based on high-resolution remote sensing data. Initially, the WorldView-2 (WV-2) multispectral images obtained before and after the earthquake under the water-storage state of the lake were used as the data source, and the unmanned aerial vehicle (UAV)-based measurement under the water-free state of the lake after the earthquake was used as the bathymetric calibration and validation data. Then using satellite-derived image reflectance, we constructed two-phase bathymetric models with machine learning methods, namely random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP). The comparison results with classical regression models indicate that machine learning-based water depth inversion models are generally superior. Specifically, the R2 (coefficient of determination) of the optimal model RF reach 0.85 and 0.91, with RMSE (root mean square error) of 1.40 m and 1.08 m. The bathymetric difference maps generated from water depth inversion results reveal that during the period from October 2016 to January 2022, the core area of Huohua Lake experienced more erosion than accretion due to the earthquake-induced flooding. The spatial patterns of changes show that the erosion mainly located in the raised tufa mound area, while the accretion was concentrated in the shallow flat area. This study provides a remote sensing approach for quantifying bathymetric changes in tufa lakes after extreme geological disasters

Hidden-charm Hexaquarks from Lattice QCD

We present a lattice QCD study of hidden-charm hexaquarks with quark content $usc\bar{d}\bar{s}\bar{c}$ based on four ensembles of gauge configurations generated by CLQCD Collaboration with pion mass in the range of 220-300MeV. Four operators with quantum numbers $0^{++}, 0^{-+}, 1^{++}$ and $1^{--}$ respectively are constructed to interpolate the hexaquarks. After validating the spectrum and the dispersion relation for ordinary hadrons, we calculate the masses of the hexaquarks and extrapolate the results to the physical pion mass and the continuum limit. We find that the masses of the four hexaquarks are all below the $\Xi_c \bar \Xi_c$ threshold, while the $0^{-+}$ hexaquark lies around the $\eta_c K^+K^-$ threshold. These results will be helpful for experimental searches in future and for a deep understanding of the nature of multiquark states.Comment: 7 pages, 6 figure