Optimization methods allow the most appropriate solutions to be selected for a variety of different problems. Optimization is, in a mathematical sense, the process of finding conditions that give extreme values of functions, and these methods are applicable in solving problems occurring in all phases of construction project management. This is especially important in the early, usually multidisciplinary stages of a project.

Forecasting models are used in all branches of construction, for various types of analyses, calculations, verifications, forecasts, and estimates. Some values, such as type, quantity, price, or time, have calculations and predictions that are carried out in all phases of project implementation, including the earliest stages. These generally lack both quantity and quality of information, and so are considered the biggest problems. In addition to calculations and forecasts, civil engineers are obliged to make various choices that can be considered optimization or forecasting problems, such as considering types of contracts, contractors, subcontractors, or materials. Decisions are made according to the results of predictions, and, based on these decisions, it is determined whether or not the project (or some of its phases) should be realized. They also define the further course of the project realization.

This Special Issue aims to provide a platform for research into the use of various optimization and forecasting models and methods to successfully and accurately solve problems in construction, including analyses, calculations, forecasts, estimates, and choices. The variety and wide range of possibilities of these methods make them a valuable tool in the field of civil engineering, and we welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Optimization methods used in civil engineering, including nonlinear programming, dynamic programming, resource allocation, Markov chains, and game theory
• Prediction models for solving civil engineering problems, including artificial neural networks, fuzzy and neural networks, and probabilistic analyses
• Data mining techniques, including tracking patterns, classification, association, outlier detection, clustering, regression, and prediction for decision-making, used in civil engineering
• Applicability in civil engineering of mathematical models determining economic growth