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This study examined the decline of traditional villages due to urbanization, focusing on their spatial patterns and architectural characteristics in China, particularly in the Guanzhong region. Using ArcGIS tools, kernel density and nearest-neighbor analyses quantitatively assessed the spatial distribution of these villages at macro- and micro-levels. Additionally, 3D laser scanning was employed to qualitatively analyze architectural features. The study demonstrated that (1) traditional villages are unevenly clustered nationwide, primarily in the southeast and southwest, creating a "three cores and multiple points" spatial pattern. (2) In the Guanzhong region, traditional village distribution also shows clustering with diverse patterns, including regiment, belt, and point formations. Higher densities are found in the eastern and northern regions, while the west and south are sparsely populated. Most villages are located at altitudes of 501–700 m, on slopes of 6–15°, and near water sources. (3) The basic residential structures in Guanzhong included the single, vertical multi-entry, and horizontal coupled courtyards, as well as the vertical and horizontal interleaved layouts; these buildings typically featured the foundations and walls made of earth, stone, and brick, combined with various wooden frames and single- or double-sloped roofs. This study overcomes the limitations of the traditional literature and field surveys by quantitatively and qualitatively analyzing the spatial patterns of traditional villages and the architectural forms of residential buildings from an architectural perspective. It graphically presents the data to provide an efficient and practical theoretical basis for the heritage preservation and development of traditional villages. [ABSTRACT FROM AUTHOR]

In engineering projects, the coordination and management of multiple objectives is an important part of project management, which has a direct impact on the realization of objectives in terms of project duration, cost, and quality. The current engineering project investment presents the characteristics of large scale, long period, and many risks, which puts forward higher requirements for the multiobjective coordinated management of the project. Therefore, the analysis and optimization of the various objectives of the engineering project is the basis for achieving multiobjective balance and coordination. The comprehensive consideration of the risks faced by the engineering project and the dynamics of the environment in which it is located can make the objective optimization more realistic. On the basis of risk identification and risk evaluation, this paper is committed to considering multiple objective factors that affect the outcome of risk decision-making to achieve the optimization of decision-making schemes and applies multiobjective genetic algorithm to the optimization of decision-making schemes, thus finding a way to optimize the decision-making scheme. This paper analyzes the research progress and status of construction project risk management decision and multiobjective evolutionary algorithm and points out the imperfections of the current research. Then it introduces the related theory of construction project risk management decision, the related terminology of multiobjective optimization problem and the method used in this paper —the principle, process, and characteristics of genetic algorithm and prepares for the following problem solving. In this paper, a mathematical model for multiobjective decision-making of engineering project risk management is established, and the multiobjective genetic algorithm is used to solve the model. Through the analysis of examples, a series of Pareto optimal solutions with good convergence and diversity are obtained, the best combination of various risk control measures is found, and three goals of risk evaluation value, management cost, and risk loss are achieved. The two models with or without risk correlation present certain differences, the model with correlation considered is more accurate and applicable than the model without correlation considered in the existing research. [ABSTRACT FROM AUTHOR]