Seven state-of-the-art machine learning techniques for estimation of construction costs of reinforced-concrete and prestressed concrete bridges are investigated in this paper, including artificial neural networks (ANN) and ensembles of ANNs, regression tree ensembles (random forests, boosted and bagged regression trees), support vector regression (SVR) method, and Gaussian process regression (GPR). A database of construction costs and design characteristics for 181 reinforced-concrete and prestressed-concrete bridges is created for model training and evaluation.

Depreciation expenses represent a signifi cant part of total expenses of construction machinery. Precise calculation of depreciation expenses is often diffi cult or impossible. Straight line method of depreciation, which is commonly used in the calculation of ownership costs of construction machinery, does not give accurate results. This paper analyzes methods of depreciation expenses calculation, as well as their impact on the overall expanses of construction machinery and the impact on the cost per unit of material processed.

Strengthening of concrete structures is applied as a solution for various deterioration problems in civil engineering practice. This also refers to the structures made of self-compacting concrete (SCC), which is increasingly in use, but there is a lack of research in this field. This paper presents an experimental analysis of flexural behavior of reinforced concrete (RC) continuous beams made of SCC, strengthened with fiber reinforced polymer (FRP) materials (glass (GFRP) and carbon (CFRP) bars, CFRP laminates), by the use of near surface mounted (NSM) and externally bonded (EB) methods. Six two-span continuous beams of a total length of 3200 mm, with the span between supports of 1500 mm and 120/200 mm cross section, were subjected to short-term load and tested. The displacements of beams and the strains in concrete, steel reinforcement, FRP bars and tapes were recorded until failure under a monotonically increasing load. The ultimate load capacities of the strengthened beams were enhanced by 22% to 82% compared to the unstrengthened control beam. The ductility of beams strengthened with GFRP bars was satisfactory, while the ductility of beams strengthened with CFRP bars and tapes was very small, so the failure modes of these beams were brittle.

Knowing the right moment for the sale of used heavy construction equipment is important information for every construction company. The proposed methodology uses ensemble machine learning techniques to estimate the price (residual value) of used heavy equipment, both present and in the near future. Each machine in the model is represented with four groups of attributes: age and mechanical (describing the machine), and geographical and economic (describing the target market). The research suggests that the ensemble model based on Random Forest, Light Gradient Boosting, and Neural Network members, and Support Vector Regression as a decision unit gives better estimates than the traditional regression or individual machine learning models. The model is built and verified on a large dataset of 500,000 machines, advertised in 50 US states from 1989 till 2012.

This paper discusses the current problem of the electronic memory reliability in terms of the ionizing radiation effects. The topic is actual since the high degree of components' miniaturization integrated into the flash memory causes the extreme sensitivity of this memory type to the ionizing radiation effects. The effects of ionizing radiation may cause changes in stored data, or even the physical destruction of the components. At the end, the experimentally and numerically obtained effects of radiation on specific flash memories are shown and discussed. The results obtained by laboratory and numerical experiments showed good agreement with each other and with the theoretically expected results.