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The problem of loss of stability of underground pipelines with a middle part in the form of a straight or П-shaped part located in liquefied soils is considered in the article. The pipeline lifting process occurs under the action of buoyancy force in the liquefied soil zone and then under the action of the longitudinal compressive force that appears due to the temperature of the transported product and its pressure. The problem is solved by the finite element method. The results are presented as a table and graphs of changes in the values of transverse displacements along the coordinate depending on the pipeline geometric characteristics and the soil rheological properties. It is established that the П-shaped part is a damper against pipeline buckling.

The article deals with the vibrations of a reinforced concrete bridge whose supports are located in soils with different properties under the action of overweight cargoes. The assumption is made that the structure deformed in the elastic zone when Hooke's law is valid. The Fourier method is used to solve the problem. The results are presented as graphs of changes in stresses and deflections along the coordinate and time, which are accompanied by analysis. When a concentrated load is applied, depending on the stiffness coefficient of the connection between the supports and the ground, stresses exceeding the limit load can occur in the middle of the beam cross-section. Thus, in the calculations, it is necessary to consider the nature of the interaction of the supports with the surrounding soils when calculating the beams for the action of moving loads.

Chemical oxygen demand (COD), an important indicative measure of the amount of oxidizable pollutants in wastewater, is often analyzed off-line due to the expensive sensor required for on-line analysis. However, its off-line analysis is time-consuming. An on-line COD estimation method was developed with photoelectrocatalytic (PEC) technology. Based on the on-line data of the oxidation-reduction potential (ORP), dissolved oxygen (DO) and pH of wastewater, four different artificial neural network methods were applied to develop working models for COD estimation. Six different batches of sequence batch reactor (SBR) effluent from a paper mill were treated with PEC oxidation for 90 minutes, and 546 data points were collected from the on-line measurements of ORP, DO and pH, and the off-line COD analysis. After having training and validation with 75% and 25% of data, and evaluation with four statistical criteria (R 2 , RMSE, MAE and MAPE), the estimation results indicated that the developed radial basis neural network (RBNN) model demonstrated the highest precision. Subsequently, the application of the RBNN model to a new batch of SBR effluent from the paper mill revealed that the RBNN model was acceptable for COD estimation during the PEC advanced treatment process of papermaking wastewater, which implied its possible application in the future.