Floods play a significant role in terms of damage and safety during construction and operation of crucial objects such as a bridge over the Lena river and underwater crossings of trunk pipelines in the North and the Arctic. A rapid rise of spring high water on the Lena river is due to accelerated melting of snow in a basin and a meridional flow direction of the river. If flood control measures are not taken, then severe economic and social consequences are inevitable, especially in places with complex infrastructure. As, for example, heavily populated cities, the strategically important objects, the underwater crossings of the trunk pipelines, bridges and power lines. This paper presents results of a study of a possibility of use of neural network algorithms to predict danger of the flood from the spring high waters on a section of the Lena river based on statistical archival data obtained over 70 years and an assessment of effectiveness of the neural network approach. The artificial neural networks have proven their effectiveness in solving various prediction problems, especially when using the statistical data. The use of the neural network approach based on the prediction of a time series from previous values gives the good results. Modeling was carried out using methods of a multilayer perceptron (MLP) and radial basis network (RBF). Both selected methods showed sufficient adequacy of selected statistical models.

A quite simple method is proposed for the assessment of extremely cold subarctic climate environment destruction of the basalt fiber reinforced epoxy (BFRE) rebar. The method involves the comparison of experimentally obtained long-term moisture uptake kinetic curves of unexposed and exposed BFRP rebars. A moisture uptake test was carried out at the temperature of 60 °C and relative humidity of 98 ± 2% for 306 days. The plasticization can be neglected because of low-level moisture saturation (<0.41% wt.); the swelling and structural relaxation of the polymer network can be neglected due to the high fiber content of BFRP rebar; moisture diffusion into the basalt fibers can be neglected since it is a much lesser amount than in the epoxy binder. These assumptions made it possible to build a three-stage diffusion model. It is observed that an increase in the density of defects with an increase in the diameter of the BFRP rebar is the result of the technology of manufacturing a periodic profile. The diffusion coefficient of the BFRP rebar with a 6, 10, or 18 mm diameter increased at an average of 82.7%, 56.7%, and 30%, respectively, after exposure to the climate of Yakutsk during 28 months, whereas it was known that the strength indicators had been increased.

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