Many civil infrastructures contain periodic elements for the purposes of foundation support or strength reinforcement. Good examples are the sleepers for rail track support and the rebars in reinforced concrete structures. For engineering nondestructive testing and evaluation (NDT&E) using ground penetrating radar (GPR) these periodic elements in the near subsurface act as scatterers that generate clutters in GPR profiles and mask the signature of the targets and hinder GPR's capability in defect detection and characterization. In this paper, we develop an algorithm using multibandpass filtering (MPF) technique to suppress the clutters in GPR profiles caused by periodic scatterers. We first extract the spatial information of the periodic scatterers and carry out spectral analysis to characterize the clutter energy in multiple wavenumbers. Then, we use this information to design the MPF in wavenumber domain and execute the filtering back in space domain with proper windowing. We test the effectiveness by applying the algorithm to a synthetic data set that simulates GPR survey on a railway system. The filtering results are very encouraging. We also apply MPF to the field GPR data collected on the Tibetan Railway. A fouled area in the ballast can be much clearly visualized than in the original, clutter-masked GPR profile. We also apply MPF in steel bar reinforced concrete slab and compare the results with the redatuming and migration methods. The MPF appears superior in clutter suppression caused by periodic scatters. MPF provides a practical and effective way to enhance GPR imaging in engineering NDT&E.

Ground-penetrating radar (GPR) is a nondestructive near-surface geophysical detection method, which is often used to locate subgrade diseases in railway subgrade detection. However, the strong noise reflected by the sleeper will obscure the effective information of the subgrade disease. In order to suppress this strong noise, the traditional processing method is generally filtering, which is dependent on expert experience. We propose a method of railway sleeper interference suppression based on UNet. We use datasets based on real railway subgrade structures to train UNet. Each dataset consists of a pair of GPR data with sleeper interference and GPR data without sleeper interference. The experimental results of simulation data show that the SSIM value between the data processed by UNet and the data without sleeper interference exceeds 0.99. We test our approach on field data collected on the Qinghai-Tibet Railway. Compared with traditional filtering methods, UNet does not rely on expert experience and is more effective. The research shows that our method can effectively suppress the sleeper strong interference in GPR data and improve the signal-to-noise ratio. We expect that our method can promote the development of railway subgrade detection.

The fluctuation of iron ore prices is one of the most important factors affecting policy. Therefore, the accurate prediction of iron ore prices has significant value in analysis and judgment regarding future changes in policies. In this study, we propose a correlation analysis to extract eight influencing factors of iron ore prices and introduce multiple linear regression analysis to the prediction. With historical data, we establish a model to forecast iron ore prices from 2020 to 2024. Taking prices in 2018 and 2019 as samples to test the applicability of the model, we obtain an acceptable level of error between the predicted iron ore prices and the actual prices. The prediction model based on multiple linear regression has high prediction accuracy. Iron ore prices will show a relatively stable upward trend over the next five years without the effects of COVID-19.