Artificial Neural Networks For Building Projects Cost Estimating ; Искусственные нейронные сети в оценке стоимости строительных проектов ; Штучні нейронні мережі в оцінці вартості будівельних проектів

Purpose. To form an idea about the use of neural networks for estimating the cost of construction projects. Artificial neural networks are successfully used in solving numerous complex non-linear problems associated with forecasting, evaluation, decision-making, optimization, systematization, and choice in the fields of construction and its management. Artificial neural networks are particularly effective for solving complex problems, such as cost estimation problems, when the relationship between variables cannot be expressed by simple mathematical relationships. The technique. The parametric estimation method is a method in which the statistical evaluation between historical data and other variables is used for valuation. Using this method, you can get a more accurate estimate of the cost, due to the fact that this approach requires a lower level of detail compared to other methodologies. The level of accuracy of the assessment depends on the complexity, the amount of resources allocated for such work and the cost data embedded in the model. Results. Cost estimation is one of the most important factors in the management of construction projects. Any feasibility study for a project requires an accurate cost estimate in order to make the right decision about the future fate of the project. Scientific novelty. Improving cost estimation methods contributes to more efficient control of time and expenses in construction. Practical value. The use of artificial neural networks can potentially eliminate some of the main disadvantages of traditional evaluation methods. This gives great prospects for improving the reliability and validity of the method of parametric valuation. ; Цель. Сформировать представление о применении нейронных сетей для оценки стоимости строительных проектов. Искусственные нейронные сети успешно используются в решении многочисленных комплексных нелинейных проблем, связанных с прогнозированием, оценке, принятием решений, оптимизацией, систематизацией и выбором в областях строительства и его ...