Wetland cover classification grows out of the need for management and protection for wetland sources to depict wetland landscapes. Exploring improved classification methods is important to derive good-quality wetland mapping products. This study investigates and applies two artificial neural network (ANN) based ensemble methods, namely, the MultiBoost artificial neural network (MBANN) and the rotation artificial neural network (RANN), for wetland cover classification, taking the Zoige wetland sited in the Qinghai–Tibet Plateau, China as the case area. The RANN trains and combines diverse ANNs by constructing a series of sparse rotation matrices, whereas the MBANN is developed from the sequential iteration in combination with the parallel sampling technique. Sixteen features related to wetland covers were extracted based on the digital elevation model data and Landsat 8 OLI images. The deep visual geometry group (VGG11) and random forests (RF) were implemented for comparison with our methods. The classification capability evaluation shows that our ensemble methods significantly improve the single ANN and outperform the VGG11 and RF. The RANN yields the highest overall accuracy (0.961), followed by the MBANN (0.942), VGG11 (0.934), RF (0.931), and ANN (0.916). We further concern and evaluate the classifier’s robustness because it reflects the uniformity of classification capability. The RANN and the MBANN are insensitive to the reduction in data size, resistant to feature variability, and not influenced by data noise. Overall, the use of ensemble techniques can refine single ANN in classification capability and stability. The results from this study attest the important role of ensemble learning, which provides a promising scheme for wetland cover classification.

Metal pipes are often interfered with by currents of irregular frequency and wavelength. This is called stray current interference. The corrosion of metal pipes caused by stray current interference is one of the many factors contributing to their deterioration. Pipeline corrosion and leakage caused by stray currents can significantly impact the safety, environment, and economy of the surrounding areas. Over the past few years, stray current interference has become one of the research hotspots in the petrochemical and natural gas transportation industries. This review article investigates accident cases caused by stray currents and compares the corrosion mechanisms of DC stray currents and AC stray currents, analyzes the influence of pipeline material, environment, voltage and current, stress, and other factors on corrosion, and puts forward corrosion protection measures, such as cathodic protection, coating technology, internal corrosion control, and means of detecting stray current corrosion. Finally, it provides an outlook on future research directions on corrosion protection against stray currents.

The performance of high-density polyethylene (PE) pipes joints directly affects the total pipeline’s operation, and so studying the residual stress of butt fusion joints is crucial for enhancing the safety of gas pipelines. Based on a layer-by-layer ring cutting test method, we measured the distribution of residual stresses in the fusion zone and heat-affected zone of butt fusion joints for PE gas pipes. Firstly, the ring samples were cut, their diameter changes were measured, and the results were compared with those predicted by the theoretical calculations. This showed that the circumferential residual stresses of the butt fusion joint for the PE gas pipes are exponentially distributed in the base material (BM) zone, the weld zone (WZ) and the heat-affected zone (HAZ). Furthermore, the residual stresses in the HAZ are lower than those in the BM zone, and the smallest residual stresses were seen in the WZ. Finally, using X-ray diffraction (XRD) technology, the crystallinities in the BM zone, the WZ, and the HAZ of the butt joints were measured. The crystallinity gradually decreased from the WZ to the HAZ and the BM zone, and the crystallinity in each zone was also related to the magnitude of the residual stresses.

In this paper, a method based on image recognition was proposed to detect the defects of polyethylene (PE) gas pipeline, especially the deformation due to the indentation. First, the pipeline -detection VGG (PD-VGG) model was established based on the convolutional neural network (CNN), and appropriate model parameters were optimized through model training. The defect recognition rate of the improved model can reach 94.76%. Following, the weighted average graying algorithm was used to separate the defects characterized by deformation. Then, an improved gamma correction algorithm was applied to achieve image enhancement, and the interference of impurities adhered on intersurface of pipeline was also removed by using multilayer filters. The edge detection of the defect image was completed by using the Canny operator, and following the screening between the target contour and the interference contour by using top-contour. Finally, the algorithm for minimum outer rectangle algorithm was used to fit the defect contour, and the eigenvalues of deformation defects were extracted. The results indicate that the above defect detection method can better extract the deformation contour of the dented pipeline. The high agreement with the experimental results provides a basis for the research of effectively recognizing whether the pipeline has undergone ductile failure only through profile detection of defects.

In recent years, "digital pipeline" technology has been applied to an increasing number of applications, and has promoted the further development of pipeline integrity management. This article presents the overall framework of the digital pipeline system, comprising the pipeline integrity management system that includes risk assessment, evaluation of corrosion and associated protection systems. As an example, the technology has been applied to a domestic oil pipeline to obtain a comprehensive pipeline integrity assessment. The results of the evaluation fully reflect the pipeline status, risk factors and potential causes of accidents, and provide the basic information required for pipeline integrity management.

Constitutive models and deformation behaviors for polymer materials have long been complex and are always a hot research focus. As a typical semi-crystalline polymer, polyethylene (PE) gas pipes exhibit pronounced nonlinearity, strain dependence, and time dependence during long-term service. Simple material models fail to capture the scale-dependent characteristics of the PE pipes, resulting in difficulties in accurately describing and simulating their deformation and damage behavior. Currently, some PE gas pipes have entered the mid-to-late stages of service life, so it is necessary to propose a constitutive model representing their complex mechanical behavior for simulation and performance evaluation purposes. Based on results from aging tests, tensile tests, differential scanning calorimetry, and Fourier-transform infrared spectroscopy, this study proposes a method to select a rheological framework and a constitutive model that couples thermo-oxidative aging effects in PE gas pipes. The model is developed within the widely recognized rheological framework and is grounded in continuum mechanics, continuum damage mechanics, and the aging behavior of polymer materials. This method and model are suitable for characterizing the mechanical dependency of PE pipes and demonstrate strong fitting performance. According to the calculation results, the goodness of fit of this constitutive model for the uniaxial tensile test results at the different aging times ranges from 0.982 to 0.999. The findings provide theoretical support for the simulation and service life prediction for PE pipelines.