The purpose of this article is to describe an adaptive decision-making support model aimed at improving the efficiency of engineering infrastructure reconstruction program management in the context of developing the architecture and work breakdown structure of programs. As part of the study, the existing adaptive program management tools are analyzed, the use of infrastructure systems modelling tools is justified for program architecture and WBS creation. Existing models and modelling methods are viewed, and machine learning and artificial neural networks are selected for the model. The main components of the model are defined, which include a set of decision-maker preferences, decision-making tasks, sets of input data, and applied software components of the model. To support decision-making, the adaptive model applies the method of system modeling and predicting the value of the objective function at a given system configuration. Prediction is done using machine learning methods based on a dataset consisting of historical data related to existing engineering systems. The work describes the components of the redistribution of varied model parameters, which modify the model dataset based on the selected object type, which allows adapting the decision-making process to the existing program implementation goals. The functional composition done in Microsoft Azure Machine Learning Studio is described. The neural network parameters and evaluation results are given. The application of the developed adaptive model is possible in the management of programs for the reconstruction of such engineering systems as systems of heat, gas, electricity supply, water supply, and drainage, etc.

翻译：本文旨在描述一种自适应决策支持模型，旨在提高工程基础设施重建项目管理的效率，特别是在项目架构和工作分解结构（WBS）开发的背景下。研究分析了现有的自适应项目管理工具，论证了使用基础设施系统建模工具来创建项目架构和WBS的合理性。文中回顾了现有模型和建模方法，并选择机器学习和人工神经网络作为模型的核心技术。模型的主要组成部分被定义，包括决策者偏好集、决策任务集、输入数据集以及模型的应用软件组件。为支持决策，该自适应模型采用系统建模方法，并预测在给定系统配置下目标函数的值。预测基于包含现有工程系统历史数据的数据集，使用机器学习方法实现。研究描述了模型参数多样化的重新分配组件，这些组件根据所选对象类型调整模型数据集，从而使决策过程适应现有项目执行目标。文中阐述了在Microsoft Azure Machine Learning Studio中实现的功能组成，并提供了神经网络参数和评估结果。所开发的自适应模型可应用于热力、燃气、电力供应、供水和排水等工程系统重建项目的管理。