设计阶段工程造价控制的关键节点与策略 -工程造价专业

摘要

随着建筑行业向高质量发展转型，工程造价管理的重要性日益凸显，而设计阶段作为项目全生命周期中最具影响力的环节之一，其造价控制效果对整体成本目标的实现具有决定性作用。本研究以设计阶段工程造价控制为切入点，旨在探索关键节点及其优化策略，从而提升造价管理效率与精度。通过文献分析、案例研究及定量建模等方法，系统梳理了设计阶段造价控制的主要影响因素，并识别出方案设计、初步设计和技术设计三个核心节点。研究进一步提出基于BIM技术的动态协同机制和多维度评价体系，用以强化各节点间的衔接与反馈。结果表明，将技术经济分析融入设计全过程能够显著降低变更风险，同时提高资源利用效率。此外，研究创新性地引入人工智能算法辅助决策支持，实现了对设计方案的成本预测与优化。最终得出结论：通过明确关键节点、优化控制流程以及引入先进技术手段，可以有效增强设计阶段造价控制的科学性和前瞻性，为工程项目提供更具操作性的管理工具。这一研究成果不仅丰富了工程造价理论体系，还为实际应用提供了重要参考，对促进行业精细化管理水平提升具有重要意义。

关键词：设计阶段造价控制；关键节点优化；BIM技术应用；人工智能辅助决策；成本预测与资源效率

ABSTRACT

As the construction industry transitions towards high-quality development, the importance of engineering cost management has become increasingly prominent. Given that the design phase is one of the most influential stages in a project's entire life cycle, its effectiveness in cost control plays a decisive role in achieving overall cost ob jectives. This study focuses on cost control during the design phase, aiming to explore key nodes and their optimization strategies to enhance the efficiency and accuracy of cost management. By employing methods such as literature analysis, case studies, and quantitative modeling, the study systematically examines the primary influencing factors of cost control in the design phase and identifies three core nodes: conceptual design, preliminary design, and technical design. Furthermore, the research proposes a dynamic collaboration mechanism based on BIM technology and a multi-dimensional evaluation system to strengthen the connection and feedback among these nodes. The findings indicate that integrating techno-economic analysis into the entire design process can significantly reduce the risk of changes while improving resource utilization efficiency. Additionally, this study innovatively incorporates artificial intelligence algorithms for decision support, enabling cost prediction and optimization of design schemes. Ultimately, it concludes that by clarifying key nodes, optimizing control processes, and adopting advanced technological approaches, the scientific rigor and foresight of cost control in the design phase can be effectively enhanced, providing more operational management tools for engineering projects. This research not only enriches the theoretical fr amework of engineering cost management but also offers significant practical references, contributing substantially to the improvement of fine-grained management capabilities in the industry.

Keywords: Design Stage Cost Control; Key Node Optimization; Bim Technology Application; Artificial Intelligence Assisted Decision Making; Cost Prediction And Resource Efficiency